From b441533a1cfd0269a9f80f544f41b48f82eaf8e1 Mon Sep 17 00:00:00 2001
From: Harsha S <sharsha315@gmail.com>
Date: Sat, 23 Mar 2024 14:08:32 +0000
Subject: [PATCH 01/27] DOC FIX - Formatted Docstrings for the
 retrieve_user_proxy_agent.py and Added first single line for the class
 RetrieveUserProxyAgent.

---
 .../contrib/retrieve_user_proxy_agent.py      | 121 ++++++++++++------
 1 file changed, 82 insertions(+), 39 deletions(-)

diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
index f252f60e5ec..fb217ad776a 100644
--- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
@@ -62,6 +62,10 @@
 
 
 class RetrieveUserProxyAgent(UserProxyAgent):
+    """(In preview) The Retrieval-Augmented User Proxy retrieves document chunks based on the embedding
+    similarity, and sends them along with the question to the Retrieval-Augmented Assistant
+    """
+
     def __init__(
         self,
         name="RetrieveChatAgent",  # default set to RetrieveChatAgent
@@ -73,67 +77,106 @@ def __init__(
         r"""
         Args:
             name (str): name of the agent.
+
             human_input_mode (str): whether to ask for human inputs every time a message is received.
                 Possible values are "ALWAYS", "TERMINATE", "NEVER".
                 1. When "ALWAYS", the agent prompts for human input every time a message is received.
                     Under this mode, the conversation stops when the human input is "exit",
                     or when is_termination_msg is True and there is no human input.
-                2. When "TERMINATE", the agent only prompts for human input only when a termination message is received or
-                    the number of auto reply reaches the max_consecutive_auto_reply.
-                3. When "NEVER", the agent will never prompt for human input. Under this mode, the conversation stops
-                    when the number of auto reply reaches the max_consecutive_auto_reply or when is_termination_msg is True.
+                2. When "TERMINATE", the agent only prompts for human input only when a termination
+                    message is received or the number of auto reply reaches
+                    the max_consecutive_auto_reply.
+                3. When "NEVER", the agent will never prompt for human input. Under this mode, the
+                    conversation stops when the number of auto reply reaches the
+                    max_consecutive_auto_reply or when is_termination_msg is True.
+
             is_termination_msg (function): a function that takes a message in the form of a dictionary
                 and returns a boolean value indicating if this received message is a termination message.
                 The dict can contain the following keys: "content", "role", "name", "function_call".
+
             retrieve_config (dict or None): config for the retrieve agent.
-                To use default config, set to None. Otherwise, set to a dictionary with the following keys:
-                - task (Optional, str): the task of the retrieve chat. Possible values are "code", "qa" and "default". System
-                    prompt will be different for different tasks. The default value is `default`, which supports both code and qa.
-                - client (Optional, chromadb.Client): the chromadb client. If key not provided, a default client `chromadb.Client()`
-                    will be used. If you want to use other vector db, extend this class and override the `retrieve_docs` function.
-                - docs_path (Optional, Union[str, List[str]]): the path to the docs directory. It can also be the path to a single file,
-                    the url to a single file or a list of directories, files and urls. Default is None, which works only if the collection is already created.
-                - extra_docs (Optional, bool): when true, allows adding documents with unique IDs without overwriting existing ones; when false, it replaces existing documents using default IDs, risking collection overwrite.,
-                    when set to true it enables the system to assign unique IDs starting from "length+i" for new document chunks, preventing the replacement of existing documents and facilitating the addition of more content to the collection..
-                    By default, "extra_docs" is set to false, starting document IDs from zero. This poses a risk as new documents might overwrite existing ones, potentially causing unintended loss or alteration of data in the collection.
+
+                To use default config, set to None. Otherwise, set to a dictionary with the
+                following keys:
+                - task (Optional, str): the task of the retrieve chat. Possible values are
+                    "code", "qa" and "default". System prompt will be different for different tasks.
+                     The default value is `default`, which supports both code and qa.
+                - client (Optional, chromadb.Client): the chromadb client. If key not provided, a
+                     default client `chromadb.Client()` will be used. If you want to use other
+                     vector db, extend this class and override the `retrieve_docs` function.
+                - docs_path (Optional, Union[str, List[str]]): the path to the docs directory. It
+                     can also be the path to a single file, the url to a single file or a list
+                     of directories, files and urls. Default is None, which works only if the
+                     collection is already created.
+                - extra_docs (Optional, bool): when true, allows adding documents with unique IDs
+                    without overwriting existing ones; when false, it replaces existing documents
+                    using default IDs, risking collection overwrite., when set to true it enables
+                    the system to assign unique IDs starting from "length+i" for new document
+                    chunks, preventing the replacement of existing documents and facilitating the
+                    addition of more content to the collection..
+                    By default, "extra_docs" is set to false, starting document IDs from zero.
+                    This poses a risk as new documents might overwrite existing ones, potentially
+                    causing unintended loss or alteration of data in the collection.
                 - collection_name (Optional, str): the name of the collection.
                     If key not provided, a default name `autogen-docs` will be used.
                 - model (Optional, str): the model to use for the retrieve chat.
                     If key not provided, a default model `gpt-4` will be used.
                 - chunk_token_size (Optional, int): the chunk token size for the retrieve chat.
                     If key not provided, a default size `max_tokens * 0.4` will be used.
-                - context_max_tokens (Optional, int): the context max token size for the retrieve chat.
+                - context_max_tokens (Optional, int): the context max token size for the
+                    retrieve chat.
                     If key not provided, a default size `max_tokens * 0.8` will be used.
-                - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values are
-                    "multi_lines" and "one_line". If key not provided, a default mode `multi_lines` will be used.
-                - must_break_at_empty_line (Optional, bool): chunk will only break at empty line if True. Default is True.
+                - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values
+                    are "multi_lines" and "one_line". If key not provided, a default mode
+                    `multi_lines` will be used.
+                - must_break_at_empty_line (Optional, bool): chunk will only break at empty line
+                    if True. Default is True.
                     If chunk_mode is "one_line", this parameter will be ignored.
                 - embedding_model (Optional, str): the embedding model to use for the retrieve chat.
-                    If key not provided, a default model `all-MiniLM-L6-v2` will be used. All available models
-                    can be found at `https://www.sbert.net/docs/pretrained_models.html`. The default model is a
-                    fast model. If you want to use a high performance model, `all-mpnet-base-v2` is recommended.
-                - embedding_function (Optional, Callable): the embedding function for creating the vector db. Default is None,
-                    SentenceTransformer with the given `embedding_model` will be used. If you want to use OpenAI, Cohere, HuggingFace or
-                    other embedding functions, you can pass it here, follow the examples in `https://docs.trychroma.com/embeddings`.
-                - customized_prompt (Optional, str): the customized prompt for the retrieve chat. Default is None.
-                - customized_answer_prefix (Optional, str): the customized answer prefix for the retrieve chat. Default is "".
-                    If not "" and the customized_answer_prefix is not in the answer, `Update Context` will be triggered.
-                - update_context (Optional, bool): if False, will not apply `Update Context` for interactive retrieval. Default is True.
-                - get_or_create (Optional, bool): if True, will create/return a collection for the retrieve chat. This is the same as that used in chromadb.
-                    Default is False. Will raise ValueError if the collection already exists and get_or_create is False. Will be set to True if docs_path is None.
-                - custom_token_count_function (Optional, Callable): a custom function to count the number of tokens in a string.
-                    The function should take (text:str, model:str) as input and return the token_count(int). the retrieve_config["model"] will be passed in the function.
-                    Default is autogen.token_count_utils.count_token that uses tiktoken, which may not be accurate for non-OpenAI models.
-                - custom_text_split_function (Optional, Callable): a custom function to split a string into a list of strings.
-                    Default is None, will use the default function in `autogen.retrieve_utils.split_text_to_chunks`.
-                - custom_text_types (Optional, List[str]): a list of file types to be processed. Default is `autogen.retrieve_utils.TEXT_FORMATS`.
-                    This only applies to files under the directories in `docs_path`. Explicitly included files and urls will be chunked regardless of their types.
-                - recursive (Optional, bool): whether to search documents recursively in the docs_path. Default is True.
+                    If key not provided, a default model `all-MiniLM-L6-v2` will be used. All available
+                    models can be found at `https://www.sbert.net/docs/pretrained_models.html`.
+                    The default model is a fast model. If you want to use a high performance model,
+                    `all-mpnet-base-v2` is recommended.
+                - embedding_function (Optional, Callable): the embedding function for creating the
+                    vector db. Default is None, SentenceTransformer with the given `embedding_model`
+                    will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
+                    functions, you can pass it here,
+                    follow the examples in `https://docs.trychroma.com/embeddings`.
+                - customized_prompt (Optional, str): the customized prompt for the retrieve chat.
+                    Default is None.
+                - customized_answer_prefix (Optional, str): the customized answer prefix for the
+                    retrieve chat. Default is "".
+                    If not "" and the customized_answer_prefix is not in the answer,
+                    `Update Context` will be triggered.
+                - update_context (Optional, bool): if False, will not apply `Update Context` for
+                    interactive retrieval. Default is True.
+                - get_or_create (Optional, bool): if True, will create/return a collection for the
+                    retrieve chat. This is the same as that used in chromadb.
+                    Default is False. Will raise ValueError if the collection already exists and
+                    get_or_create is False. Will be set to True if docs_path is None.
+                - custom_token_count_function (Optional, Callable): a custom function to count the
+                    number of tokens in a string.
+                    The function should take (text:str, model:str) as input and return the
+                    token_count(int). the retrieve_config["model"] will be passed in the function.
+                    Default is autogen.token_count_utils.count_token that uses tiktoken, which may
+                    not be accurate for non-OpenAI models.
+                - custom_text_split_function (Optional, Callable): a custom function to split a
+                    string into a list of strings.
+                    Default is None, will use the default function in
+                    `autogen.retrieve_utils.split_text_to_chunks`.
+                - custom_text_types (Optional, List[str]): a list of file types to be processed.
+                    Default is `autogen.retrieve_utils.TEXT_FORMATS`.
+                    This only applies to files under the directories in `docs_path`. Explicitly
+                    included files and urls will be chunked regardless of their types.
+                - recursive (Optional, bool): whether to search documents recursively in the
+                    docs_path. Default is True.
+
             `**kwargs` (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
 
         Example:
 
-        Example of overriding retrieve_docs - If you have set up a customized vector db, and it's not compatible with chromadb, you can easily plug in it with below code.
+        Example of overriding retrieve_docs - If you have set up a customized vector db, and it's
+        not compatible with chromadb, you can easily plug in it with below code.
         ```python
         class MyRetrieveUserProxyAgent(RetrieveUserProxyAgent):
             def query_vector_db(

From 9a03544ce63e83250c95b3899a4b586253a11107 Mon Sep 17 00:00:00 2001
From: Harsha S <sharsha315@gmail.com>
Date: Sat, 23 Mar 2024 14:30:42 +0000
Subject: [PATCH 02/27] DOC FIX - Formatted Docstrings for  theinitiate_chats
 functiion of ChatResult class in  autogen/agentchat/chat.py

---
 autogen/agentchat/chat.py | 37 ++++++++++++++++++++++++-------------
 1 file changed, 24 insertions(+), 13 deletions(-)

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index d5e127e971f..1296607c281 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -25,7 +25,9 @@ class ChatResult:
     summary: str = None
     """A summary obtained from the chat."""
     cost: tuple = None  # (dict, dict) - (total_cost, actual_cost_with_cache)
-    """The cost of the chat. a tuple of (total_cost, total_actual_cost), where total_cost is a dictionary of cost information, and total_actual_cost is a dictionary of information on the actual incurred cost with cache."""
+    """The cost of the chat. a tuple of (total_cost, total_actual_cost), where total_cost is a
+       dictionary of cost information, and total_actual_cost is a dictionary of information on
+       the actual incurred cost with cache."""
     human_input: List[str] = None
     """A list of human input solicited during the chat."""
 
@@ -135,24 +137,33 @@ def __post_carryover_processing(chat_info: Dict[str, Any]):
 
 def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
     """Initiate a list of chats.
-
     Args:
         chat_queue (List[Dict]): a list of dictionaries containing the information about the chats.
 
-        Each dictionary should contain the input arguments for [`ConversableAgent.initiate_chat`](/docs/reference/agentchat/conversable_agent#initiate_chat). For example:
+        Each dictionary should contain the input arguments for [`ConversableAgent.initiate_chat`]
+        (/docs/reference/agentchat/conversable_agent#initiate_chat).
+        For example:
             - "sender": the sender agent.
             - "recipient": the recipient agent.
-            - "clear_history" (bool): whether to clear the chat history with the agent. Default is True.
-            - "silent" (bool or None): (Experimental) whether to print the messages in this conversation. Default is False.
-            - "cache" (Cache or None): the cache client to use for this conversation. Default is None.
-            - "max_turns" (int or None): maximum number of turns for the chat. If None, the chat will continue until a termination condition is met. Default is None.
-            - "summary_method" (str or callable): a string or callable specifying the method to get a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
-            - "summary_args" (dict): a dictionary of arguments to be passed to the summary_method. Default is {}.
-            - "message" (str, callable or None): if None, input() will be called to get the initial message.
+            - "clear_history" (bool): whether to clear the chat history with the agent.
+               Default is True.
+            - "silent" (bool or None): (Experimental) whether to print the messages in this
+               conversation. Default is False.
+            - "cache" (Cache or None): the cache client to use for this conversation.
+               Default is None.
+            - "max_turns" (int or None): maximum number of turns for the chat. If None, the chat
+               will continue until a termination condition is met. Default is None.
+            - "summary_method" (str or callable): a string or callable specifying the method to get
+               a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
+            - "summary_args" (dict): a dictionary of arguments to be passed to the summary_method.
+               Default is {}.
+            - "message" (str, callable or None): if None, input() will be called to get the
+               initial message.
             - **context: additional context information to be passed to the chat.
-                - "carryover": It can be used to specify the carryover information to be passed to this chat.
-                    If provided, we will combine this carryover with the "message" content when generating the initial chat
-                    message in `generate_init_message`.
+                - "carryover": It can be used to specify the carryover information to be passed
+                   to this chat.
+                   If provided, we will combine this carryover with the "message" content when
+                   generating the initial chat message in `generate_init_message`.
 
     Returns:
         (list): a list of ChatResult objects corresponding to the finished chats in the chat_queue.

From ec682070d9c5d7614d95e9b4eda46b76c707868a Mon Sep 17 00:00:00 2001
From: Beibin Li <BeibinLi@users.noreply.github.com>
Date: Sun, 24 Mar 2024 12:46:55 -0700
Subject: [PATCH 03/27] Add vision capability (#2025)

* Add vision capability

* Configurate: description_prompt

* Print warning instead of raising issues for type

* Skip vision capability test if dependencies not installed

* Append "vision" to agent's system message when enabled VisionCapability

* GPT-4V notebook update with ConversableAgent

* Clean GPT-4V notebook

* Add vision capability test to workflow

* Lint import

* Update system message for vision capability

* Add a `custom_caption_func` to VisionCapability

* Add custom function example for vision capability

* Skip test Vision capability custom func

* GPT-4V notebook metadata to website

* Remove redundant files

* The custom caption function takes more inputs now

* Add a more complex example of custom caption func

* Remove trailing space

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
---
 .github/workflows/contrib-tests.yml           |   2 +-
 .../contrib/capabilities/vision_capability.py | 211 ++++++
 autogen/agentchat/contrib/img_utils.py        |   6 +
 autogen/agentchat/conversable_agent.py        |  43 +-
 notebook/agentchat_lmm_gpt-4v.ipynb           | 715 +++++++++++++-----
 .../capabilities/test_vision_capability.py    | 116 +++
 6 files changed, 878 insertions(+), 215 deletions(-)
 create mode 100644 autogen/agentchat/contrib/capabilities/vision_capability.py
 create mode 100644 test/agentchat/contrib/capabilities/test_vision_capability.py

diff --git a/.github/workflows/contrib-tests.yml b/.github/workflows/contrib-tests.yml
index f46d6883fbc..ced35dc115b 100644
--- a/.github/workflows/contrib-tests.yml
+++ b/.github/workflows/contrib-tests.yml
@@ -247,7 +247,7 @@ jobs:
       - name: Coverage
         run: |
           pip install coverage>=5.3
-          coverage run -a -m pytest test/agentchat/contrib/test_img_utils.py test/agentchat/contrib/test_lmm.py test/agentchat/contrib/test_llava.py test/agentchat/contrib/capabilities/test_image_generation_capability.py --skip-openai
+          coverage run -a -m pytest test/agentchat/contrib/test_img_utils.py test/agentchat/contrib/test_lmm.py test/agentchat/contrib/test_llava.py test/agentchat/contrib/capabilities/test_image_generation_capability.py test/agentchat/contrib/capabilities/test_vision_capability.py --skip-openai
           coverage xml
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3
diff --git a/autogen/agentchat/contrib/capabilities/vision_capability.py b/autogen/agentchat/contrib/capabilities/vision_capability.py
new file mode 100644
index 00000000000..acfb9c8f6d8
--- /dev/null
+++ b/autogen/agentchat/contrib/capabilities/vision_capability.py
@@ -0,0 +1,211 @@
+import copy
+from typing import Callable, Dict, List, Optional, Union
+
+from autogen.agentchat.assistant_agent import ConversableAgent
+from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
+from autogen.agentchat.contrib.img_utils import (
+    convert_base64_to_data_uri,
+    get_image_data,
+    get_pil_image,
+    gpt4v_formatter,
+    message_formatter_pil_to_b64,
+)
+from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent
+from autogen.agentchat.conversable_agent import colored
+from autogen.code_utils import content_str
+from autogen.oai.client import OpenAIWrapper
+
+DEFAULT_DESCRIPTION_PROMPT = (
+    "Write a detailed caption for this image. "
+    "Pay special attention to any details that might be useful or relevant "
+    "to the ongoing conversation."
+)
+
+
+class VisionCapability(AgentCapability):
+    """We can add vision capability to regular ConversableAgent, even if the agent does not have the multimodal capability,
+    such as GPT-3.5-turbo agent, Llama, Orca, or Mistral agents. This vision capability will invoke a LMM client to describe
+    the image (captioning) before sending the information to the agent's actual client.
+
+        The vision capability will hook to the ConversableAgent's `process_last_received_message`.
+
+        Some technical details:
+        When the agent (who has the vision capability) received an message, it will:
+        1. _process_received_message:
+            a. _append_oai_message
+        2. generate_reply: if the agent is a MultimodalAgent, it will also use the image tag.
+            a. hook process_last_received_message (NOTE: this is where the vision capability will be hooked to.)
+            b. hook process_all_messages_before_reply
+        3. send:
+            a. hook process_message_before_send
+            b. _append_oai_message
+    """
+
+    def __init__(
+        self,
+        lmm_config: Dict,
+        description_prompt: Optional[str] = DEFAULT_DESCRIPTION_PROMPT,
+        custom_caption_func: Callable = None,
+    ) -> None:
+        """
+        Initializes a new instance, setting up the configuration for interacting with
+        a Language Multimodal (LMM) client and specifying optional parameters for image
+        description and captioning.
+
+        Args:
+            lmm_config (Dict): Configuration for the LMM client, which is used to call
+                the LMM service for describing the image. This must be a dictionary containing
+                the necessary configuration parameters. If `lmm_config` is False or an empty dictionary,
+                it is considered invalid, and initialization will assert.
+            description_prompt (Optional[str], optional): The prompt to use for generating
+                descriptions of the image. This parameter allows customization of the
+                prompt passed to the LMM service. Defaults to `DEFAULT_DESCRIPTION_PROMPT` if not provided.
+            custom_caption_func (Callable, optional): A callable that, if provided, will be used
+                to generate captions for images. This allows for custom captioning logic outside
+                of the standard LMM service interaction.
+                The callable should take three parameters as input:
+                    1. an image URL (or local location)
+                    2. image_data (a PIL image)
+                    3. lmm_client (to call remote LMM)
+                and then return a description (as string).
+                If not provided, captioning will rely on the LMM client configured via `lmm_config`.
+                If provided, we will not run the default self._get_image_caption method.
+
+        Raises:
+            AssertionError: If neither a valid `lmm_config` nor a `custom_caption_func` is provided,
+                an AssertionError is raised to indicate that the Vision Capability requires
+                one of these to be valid for operation.
+        """
+        self._lmm_config = lmm_config
+        self._description_prompt = description_prompt
+        self._parent_agent = None
+
+        if lmm_config:
+            self._lmm_client = OpenAIWrapper(**lmm_config)
+        else:
+            self._lmm_client = None
+
+        self._custom_caption_func = custom_caption_func
+        assert (
+            self._lmm_config or custom_caption_func
+        ), "Vision Capability requires a valid lmm_config or custom_caption_func."
+
+    def add_to_agent(self, agent: ConversableAgent) -> None:
+        self._parent_agent = agent
+
+        # Append extra info to the system message.
+        agent.update_system_message(agent.system_message + "\nYou've been given the ability to interpret images.")
+
+        # Register a hook for processing the last message.
+        agent.register_hook(hookable_method="process_last_received_message", hook=self.process_last_received_message)
+
+    def process_last_received_message(self, content: Union[str, List[dict]]) -> str:
+        """
+        Processes the last received message content by normalizing and augmenting it
+        with descriptions of any included images. The function supports input content
+        as either a string or a list of dictionaries, where each dictionary represents
+        a content item (e.g., text, image). If the content contains image URLs, it
+        fetches the image data, generates a caption for each image, and inserts the
+        caption into the augmented content.
+
+        The function aims to transform the content into a format compatible with GPT-4V
+        multimodal inputs, specifically by formatting strings into PIL-compatible
+        images if needed and appending text descriptions for images. This allows for
+        a more accessible presentation of the content, especially in contexts where
+        images cannot be displayed directly.
+
+        Args:
+            content (Union[str, List[dict]]): The last received message content, which
+                can be a plain text string or a list of dictionaries representing
+                different types of content items (e.g., text, image_url).
+
+        Returns:
+            str: The augmented message content
+
+        Raises:
+            AssertionError: If an item in the content list is not a dictionary.
+
+        Examples:
+            Assuming `self._get_image_caption(img_data)` returns
+            "A beautiful sunset over the mountains" for the image.
+
+        - Input as String:
+            content = "Check out this cool photo!"
+            Output: "Check out this cool photo!"
+            (Content is a string without an image, remains unchanged.)
+
+        - Input as String, with image location:
+            content = "What's weather in this cool photo: <img http://example.com/photo.jpg>"
+            Output: "What's weather in this cool photo: <img http://example.com/photo.jpg> in case you can not see, the caption of this image is:
+            A beautiful sunset over the mountains\n"
+            (Caption added after the image)
+
+        - Input as List with Text Only:
+            content = [{"type": "text", "text": "Here's an interesting fact."}]
+            Output: "Here's an interesting fact."
+            (No images in the content, it remains unchanged.)
+
+        - Input as List with Image URL:
+            content = [
+                {"type": "text", "text": "What's weather in this cool photo:"},
+                {"type": "image_url", "image_url": {"url": "http://example.com/photo.jpg"}}
+            ]
+            Output: "What's weather in this cool photo: <img http://example.com/photo.jpg> in case you can not see, the caption of this image is:
+            A beautiful sunset over the mountains\n"
+            (Caption added after the image)
+        """
+        copy.deepcopy(content)
+        # normalize the content into the gpt-4v format for multimodal
+        # we want to keep the URL format to keep it concise.
+        if isinstance(content, str):
+            content = gpt4v_formatter(content, img_format="url")
+
+        aug_content: str = ""
+        for item in content:
+            assert isinstance(item, dict)
+            if item["type"] == "text":
+                aug_content += item["text"]
+            elif item["type"] == "image_url":
+                img_url = item["image_url"]["url"]
+                img_caption = ""
+
+                if self._custom_caption_func:
+                    img_caption = self._custom_caption_func(img_url, get_pil_image(img_url), self._lmm_client)
+                elif self._lmm_client:
+                    img_data = get_image_data(img_url)
+                    img_caption = self._get_image_caption(img_data)
+                else:
+                    img_caption = ""
+
+                aug_content += f"<img {img_url}> in case you can not see, the caption of this image is: {img_caption}\n"
+            else:
+                print(f"Warning: the input type should either be `test` or `image_url`. Skip {item['type']} here.")
+
+        return aug_content
+
+    def _get_image_caption(self, img_data: str) -> str:
+        """
+        Args:
+            img_data (str): base64 encoded image data.
+        Returns:
+            str: caption for the given image.
+        """
+        response = self._lmm_client.create(
+            context=None,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "text", "text": self._description_prompt},
+                        {
+                            "type": "image_url",
+                            "image_url": {
+                                "url": convert_base64_to_data_uri(img_data),
+                            },
+                        },
+                    ],
+                }
+            ],
+        )
+        description = response.choices[0].message.content
+        return content_str(description)
diff --git a/autogen/agentchat/contrib/img_utils.py b/autogen/agentchat/contrib/img_utils.py
index 6062f3b0553..2d259241874 100644
--- a/autogen/agentchat/contrib/img_utils.py
+++ b/autogen/agentchat/contrib/img_utils.py
@@ -24,6 +24,12 @@ def get_pil_image(image_file: Union[str, Image.Image]) -> Image.Image:
         # Already a PIL Image object
         return image_file
 
+    # Remove quotes if existed
+    if image_file.startswith('"') and image_file.endswith('"'):
+        image_file = image_file[1:-1]
+    if image_file.startswith("'") and image_file.endswith("'"):
+        image_file = image_file[1:-1]
+
     if image_file.startswith("http://") or image_file.startswith("https://"):
         # A URL file
         response = requests.get(image_file)
diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index 2a2de8b551e..ef350f7d9e2 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -5,37 +5,35 @@
 import json
 import logging
 import re
+import warnings
 from collections import defaultdict
 from functools import partial
 from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Type, TypeVar, Union
-import warnings
+
 from openai import BadRequestError
 
 from autogen.exception_utils import InvalidCarryOverType, SenderRequired
 
-from ..coding.base import CodeExecutor
-from ..coding.factory import CodeExecutorFactory
-from ..formatting_utils import colored
-
-from ..oai.client import OpenAIWrapper, ModelClient
-from ..runtime_logging import logging_enabled, log_new_agent
+from .._pydantic import model_dump
 from ..cache.cache import Cache
 from ..code_utils import (
     UNKNOWN,
-    content_str,
     check_can_use_docker_or_throw,
+    content_str,
     decide_use_docker,
     execute_code,
     extract_code,
     infer_lang,
 )
-from .utils import gather_usage_summary, consolidate_chat_info
-from .chat import ChatResult, initiate_chats, a_initiate_chats
-
-
+from ..coding.base import CodeExecutor
+from ..coding.factory import CodeExecutorFactory
+from ..formatting_utils import colored
 from ..function_utils import get_function_schema, load_basemodels_if_needed, serialize_to_str
+from ..oai.client import ModelClient, OpenAIWrapper
+from ..runtime_logging import log_new_agent, logging_enabled
 from .agent import Agent, LLMAgent
-from .._pydantic import model_dump
+from .chat import ChatResult, a_initiate_chats, initiate_chats
+from .utils import consolidate_chat_info, gather_usage_summary
 
 __all__ = ("ConversableAgent",)
 
@@ -2603,22 +2601,25 @@ def process_last_received_message(self, messages):
             return messages  # Last message contains a context key.
         if "content" not in last_message:
             return messages  # Last message has no content.
-        user_text = last_message["content"]
-        if not isinstance(user_text, str):
-            return messages  # Last message content is not a string. TODO: Multimodal agents will use a dict here.
-        if user_text == "exit":
+
+        user_content = last_message["content"]
+        if not isinstance(user_content, str) and not isinstance(user_content, list):
+            # if the user_content is a string, it is for regular LLM
+            # if the user_content is a list, it should follow the multimodal LMM format.
+            return messages
+        if user_content == "exit":
             return messages  # Last message is an exit command.
 
         # Call each hook (in order of registration) to process the user's message.
-        processed_user_text = user_text
+        processed_user_content = user_content
         for hook in hook_list:
-            processed_user_text = hook(processed_user_text)
-        if processed_user_text == user_text:
+            processed_user_content = hook(processed_user_content)
+        if processed_user_content == user_content:
             return messages  # No hooks actually modified the user's message.
 
         # Replace the last user message with the expanded one.
         messages = messages.copy()
-        messages[-1]["content"] = processed_user_text
+        messages[-1]["content"] = processed_user_content
         return messages
 
     def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None:
diff --git a/notebook/agentchat_lmm_gpt-4v.ipynb b/notebook/agentchat_lmm_gpt-4v.ipynb
index b49f4472a50..3da51028b63 100644
--- a/notebook/agentchat_lmm_gpt-4v.ipynb
+++ b/notebook/agentchat_lmm_gpt-4v.ipynb
@@ -5,7 +5,13 @@
    "id": "2c75da30",
    "metadata": {},
    "source": [
-    "# Agent Chat with Multimodal Models: GPT-4V"
+    "# Engaging with Multimodal Models: GPT-4V in AutoGen\n",
+    "\n",
+    "In AutoGen, leveraging multimodal models can be done through two different methodologies:\n",
+    "1. **MultimodalAgent**: Supported by GPT-4V and other LMMs, this agent is endowed with visual cognitive abilities, allowing it to engage in interactions comparable to those of other ConversableAgents.\n",
+    "2. **VisionCapability**: For LLM-based agents lacking inherent visual comprehension, we introduce vision capabilities by converting images into descriptive captions.\n",
+    "\n",
+    "This guide will delve into each approach, providing insights into their application and integration."
    ]
   },
   {
@@ -14,9 +20,13 @@
    "metadata": {},
    "source": [
     "### Before everything starts, install AutoGen with the `lmm` option\n",
+    "\n",
+    "Install `pyautogen`:\n",
     "```bash\n",
-    "pip install \"pyautogen[lmm]>=0.2.3\"\n",
-    "```"
+    "pip install \"pyautogen[lmm]>=0.2.17\"\n",
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n"
    ]
   },
   {
@@ -36,10 +46,14 @@
     "import requests\n",
     "from PIL import Image\n",
     "from termcolor import colored\n",
+    "import numpy as np\n",
     "\n",
     "import autogen\n",
+    "from autogen.code_utils import content_str\n",
     "from autogen import Agent, AssistantAgent, ConversableAgent, UserProxyAgent\n",
-    "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent"
+    "from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability\n",
+    "from autogen.agentchat.contrib.multimodal_conversable_agent import MultimodalConversableAgent\n",
+    "from autogen.agentchat.contrib.img_utils import get_pil_image, pil_to_data_uri"
    ]
   },
   {
@@ -91,37 +105,16 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6868785c",
+   "cell_type": "markdown",
+   "id": "e9c628db",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[1, 2, 4]\n"
-     ]
-    }
-   ],
    "source": [
-    "x = [1, 2, gpt4_llm_config]\n",
-    "\n",
-    "\n",
-    "def replace(arr, x, y):\n",
-    "    idx = arr.index(x)\n",
-    "    if idx >= 0:\n",
-    "        arr[idx] = y\n",
-    "\n",
-    "\n",
-    "replace(x, gpt4_llm_config, 4)\n",
-    "\n",
-    "print(x)"
+    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration)."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "67157629",
    "metadata": {
     "scrolled": false
@@ -133,7 +126,7 @@
      "text": [
       "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
       "\n",
-      "What's the breed of this dog here? \n",
+      "What's the breed of this dog?\n",
       "<image>.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
@@ -141,10 +134,28 @@
       ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
       "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
       "\n",
-      "The dog in the image appears to be a Poodle or a Poodle mix, possibly a Labradoodle or Goldendoodle, given its curly coat and overall facial structure. These types of dogs are known for their curly, hypoallergenic fur and friendly disposition. The exact breed can be difficult to determine from a photo alone, especially with mixed breeds, as they can inherit various traits from each parent breed.\n",
+      "The dog in the image appears to be a Goldendoodle, which is a crossbreed between a Golden Retriever and a Poodle. They are known for their curly, hypoallergenic coats, which can vary in color, and their friendly and affectionate nature.\n",
       "\n",
       "--------------------------------------------------------------------------------\n"
      ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/beibinli/autogen/autogen/agentchat/conversable_agent.py:1121: UserWarning: Cannot extract summary using last_msg: 'list' object has no attribute 'replace'. Using an empty str as summary.\n",
+      "  warnings.warn(f\"Cannot extract summary using last_msg: {e}. Using an empty str as summary.\", UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ChatResult(chat_id=None, chat_history=[{'content': \"What's the breed of this dog?\\n<img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\", 'role': 'assistant'}, {'content': 'The dog in the image appears to be a Goldendoodle, which is a crossbreed between a Golden Retriever and a Poodle. They are known for their curly, hypoallergenic coats, which can vary in color, and their friendly and affectionate nature.', 'role': 'user'}], summary='', cost=({'total_cost': 0.013030000000000002, 'gpt-4-1106-vision-preview': {'cost': 0.013030000000000002, 'prompt_tokens': 1132, 'completion_tokens': 57, 'total_tokens': 1189}}, {'total_cost': 0}), human_input=[])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -184,7 +195,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "73a2b234",
    "metadata": {
     "scrolled": false
@@ -196,7 +207,7 @@
      "text": [
       "\u001b[33mUser_proxy\u001b[0m (to image-explainer):\n",
       "\n",
-      "How about this breed? \n",
+      "What is this breed?\n",
       "<image>\n",
       "\n",
       "Among the breeds, which one barks less?\n",
@@ -206,9 +217,9 @@
       ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
       "\u001b[33mimage-explainer\u001b[0m (to User_proxy):\n",
       "\n",
-      "The dog in the image is a Siberian Husky, identifiable by its thick fur, erect triangular ears, and distinctive facial markings.\n",
+      "The dog in the image is a Siberian Husky. They are known for their striking appearance, with thick fur, erect triangular ears, and distinctive markings.\n",
       "\n",
-      "Between Siberian Huskies and Poodles (or their mixes like Labradoodles and Goldendoodles), Siberian Huskies are generally known to be less prone to barking. They are more likely to howl or talk in other vocal ways. Poodles and their mixes can vary in their tendency to bark, but they are often more vocal than Huskies in terms of barking. However, individual temperaments can vary greatly within any breed, and training also plays a significant role in a dog's vocal behavior.\n",
+      "When comparing the barking tendencies of Siberian Huskies and Goldendoodles, Huskies are generally known to bark less. Siberian Huskies are more prone to howling and vocalizing in other ways rather than barking. Goldendoodles can vary in their tendency to bark based on the traits they inherit from their Golden Retriever and Poodle parents. However, every dog is an individual, and their environment, training, and socialization can greatly influence their tendency to bark.\n",
       "\n",
       "--------------------------------------------------------------------------------\n"
      ]
@@ -240,6 +251,16 @@
     "- Critics: LMM-based agent that provides comments and feedback on the generated image."
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "1e057758",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "working_dir = \"tmp/\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 6,
@@ -247,7 +268,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "class FigureCreator(AssistantAgent):\n",
+    "class FigureCreator(ConversableAgent):\n",
     "    def __init__(self, n_iters=2, **kwargs):\n",
     "        \"\"\"\n",
     "        Initializes a FigureCreator instance.\n",
@@ -267,7 +288,6 @@
     "            error_msg = f\"Either {messages=} or {sender=} must be provided.\"\n",
     "            logger.error(error_msg)  # noqa: F821\n",
     "            raise AssertionError(error_msg)\n",
-    "\n",
     "        if messages is None:\n",
     "            messages = self._oai_messages[sender]\n",
     "\n",
@@ -280,7 +300,7 @@
     "            max_consecutive_auto_reply=10,\n",
     "            system_message=\"Help me run the code, and tell other agents it is in the <img result.jpg> file location.\",\n",
     "            is_termination_msg=lambda x: x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
-    "            code_execution_config={\"last_n_messages\": 3, \"work_dir\": \".\", \"use_docker\": False},\n",
+    "            code_execution_config={\"last_n_messages\": 3, \"work_dir\": working_dir, \"use_docker\": False},\n",
     "            llm_config=self.llm_config,\n",
     "        )\n",
     "\n",
@@ -307,13 +327,17 @@
     "\n",
     "        # Data flow begins\n",
     "        commander.initiate_chat(coder, message=user_question)\n",
-    "        img = Image.open(\"result.jpg\")\n",
+    "        img = Image.open(os.path.join(working_dir, \"result.jpg\"))\n",
     "        plt.imshow(img)\n",
     "        plt.axis(\"off\")  # Hide the axes\n",
     "        plt.show()\n",
     "\n",
     "        for i in range(self._n_iters):\n",
-    "            commander.send(message=\"Improve <img result.jpg>\", recipient=critics, request_reply=True)\n",
+    "            commander.send(\n",
+    "                message=f\"Improve <img {os.path.join(working_dir, 'result.jpg')}>\",\n",
+    "                recipient=critics,\n",
+    "                request_reply=True,\n",
+    "            )\n",
     "\n",
     "            feedback = commander._oai_messages[critics][-1][\"content\"]\n",
     "            if feedback.find(\"NO_ISSUES\") >= 0:\n",
@@ -324,12 +348,12 @@
     "                recipient=coder,\n",
     "                request_reply=True,\n",
     "            )\n",
-    "            img = Image.open(\"result.jpg\")\n",
+    "            img = Image.open(os.path.join(working_dir, \"result.jpg\"))\n",
     "            plt.imshow(img)\n",
     "            plt.axis(\"off\")  # Hide the axes\n",
     "            plt.show()\n",
     "\n",
-    "        return True, \"result.jpg\""
+    "        return True, os.path.join(working_dir, \"result.jpg\")"
    ]
   },
   {
@@ -347,7 +371,7 @@
       "\u001b[33mUser\u001b[0m (to Figure Creator~):\n",
       "\n",
       "\n",
-      "Plot a figure by using the data from here:\n",
+      "Plot a figure by using the data from:\n",
       "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
       "\n",
       "I want to show both temperature high and low.\n",
@@ -357,7 +381,7 @@
       "\u001b[33mCommander\u001b[0m (to Coder):\n",
       "\n",
       "\n",
-      "Plot a figure by using the data from here:\n",
+      "Plot a figure by using the data from:\n",
       "https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\n",
       "\n",
       "I want to show both temperature high and low.\n",
@@ -366,63 +390,88 @@
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCoder\u001b[0m (to Commander):\n",
       "\n",
-      "To achieve this, I will provide you with a Python script that uses the `pandas` library to read data from the CSV file located at the provided URL, and `matplotlib` to plot both the high and low temperatures. If you don't already have `pandas` and `matplotlib` installed, you will need to install them first by running `pip install pandas matplotlib`.\n",
+      "Sure, let's first download the CSV data from the provided URL using Python and then we'll plot the figure with both temperature high and low.\n",
       "\n",
-      "Please execute the following Python code:\n",
+      "To start with, I'll provide you with a Python script that will download the CSV file and then plot the temperatures highs and lows.\n",
+      "\n",
+      "Please run this Python code:\n",
       "\n",
       "```python\n",
       "# filename: plot_temperatures.py\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
       "\n",
-      "# URL of the CSV file\n",
+      "# URL to download the CSV data\n",
       "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
       "\n",
-      "# Read the data from the URL\n",
+      "# Download the data\n",
       "data = pd.read_csv(url)\n",
       "\n",
-      "# Extract the Date, Temperature High, and Temperature Low columns\n",
-      "dates = pd.to_datetime(data['date'])\n",
-      "temp_high = data['temp_max']\n",
-      "temp_low = data['temp_min']\n",
-      "\n",
-      "# Plot the high and low temperatures\n",
+      "# Now, let us plot the high and low temperatures\n",
       "plt.figure(figsize=(10, 5))\n",
-      "plt.plot(dates, temp_high, label='High Temperature', color='r')\n",
-      "plt.plot(dates, temp_low, label='Low Temperature', color='b')\n",
+      "plt.plot(data['date'], data['temp_max'], label='High Temp')\n",
+      "plt.plot(data['date'], data['temp_min'], label='Low Temp')\n",
+      "\n",
+      "# Providing labels and title\n",
       "plt.xlabel('Date')\n",
       "plt.ylabel('Temperature (°C)')\n",
       "plt.title('High and Low Temperatures in Seattle')\n",
       "plt.legend()\n",
-      "plt.grid(True)\n",
+      "\n",
+      "# Rotate the dates on x-axis for better readability\n",
+      "plt.xticks(rotation=45)\n",
+      "plt.tight_layout()\n",
       "\n",
       "# Save the figure\n",
       "plt.savefig('result.jpg')\n",
       "\n",
-      "# Show the plot\n",
-      "plt.show()\n",
+      "print(\"The plot has been saved as 'result.jpg'.\")\n",
+      "```\n",
+      "\n",
+      "Make sure you have the required packages (`pandas` and `matplotlib`) installed. If they are not installed, you can install them using `pip`:\n",
+      "\n",
+      "```sh\n",
+      "pip install pandas matplotlib\n",
       "```\n",
       "\n",
-      "After running this script, it will output a plot of the high and low temperatures and save the figure as `result.jpg` in the current directory. Open the `result.jpg` file to view the figure.\n",
+      "After running the script, you should see a message indicating that the plot has been saved as 'result.jpg'.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[31m\n",
       ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is sh)...\u001b[0m\n",
       "\u001b[33mCommander\u001b[0m (to Coder):\n",
       "\n",
       "exitcode: 0 (execution succeeded)\n",
       "Code output: \n",
-      "Figure(1000x500)\n",
+      "The plot has been saved as 'result.jpg'.\n",
+      "\n",
+      "Requirement already satisfied: pandas in /home/beibinli/anaconda3/lib/python3.9/site-packages (1.5.3)\n",
+      "Requirement already satisfied: matplotlib in /home/beibinli/anaconda3/lib/python3.9/site-packages (3.7.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.8.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from pandas) (2.8.2)\n",
+      "Requirement already satisfied: pytz>=2020.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from pandas) (2022.7.1)\n",
+      "Requirement already satisfied: numpy>=1.20.3 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from pandas) (1.23.5)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (1.0.5)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (0.11.0)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (4.25.0)\n",
+      "Requirement already satisfied: kiwisolver>=1.0.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (1.4.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (23.2)\n",
+      "Requirement already satisfied: pillow>=6.2.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (10.1.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (3.0.9)\n",
+      "Requirement already satisfied: importlib-resources>=3.2.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from matplotlib) (5.13.0)\n",
+      "Requirement already satisfied: zipp>=3.1.0 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from importlib-resources>=3.2.0->matplotlib) (3.11.0)\n",
+      "Requirement already satisfied: six>=1.5 in /home/beibinli/anaconda3/lib/python3.9/site-packages (from python-dateutil>=2.8.1->pandas) (1.16.0)\n",
       "\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCoder\u001b[0m (to Commander):\n",
       "\n",
-      "The script executed successfully, and it created a figure of the high and low temperatures from the Seattle weather dataset. You should find the figure saved as `result.jpg` in the current directory.\n",
+      "The Python script executed successfully, and the plot has been saved as 'result.jpg'. You should now be able to find the figure file at the mentioned location. \n",
       "\n",
-      "You can view this figure by opening the `result.jpg` file on your computer.\n",
+      "The necessary packages were already installed in your environment, as indicated by your output, so no further action was required there.\n",
       "\n",
-      "If there's anything more you'd like to do with this plot or if you have any other requests, please let me know.\n",
+      "The figure shows the high and low temperatures for Seattle as indicated by the data in the CSV file you provided. If you have any other requests or need further assistance, feel free to ask. If not, and if you are satisfied with the result, we can conclude this task.\n",
       "\n",
       "TERMINATE\n",
       "\n",
@@ -431,7 +480,7 @@
     },
     {
      "data": {
-      "image/png": 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mno8/Y5y/f/Au3cyo63oSXXhwWVuM8QFpixACMUZCCBNnZEyEHH92SmkS4Rjn0cf8iPExbT7uh/rc8fGNIxI5Z/7oj/6IlZUVfviHf5icM91ud3IMTdM8IKLQNM2kXHPM+B+Pzfg4x+/pdDoPIEqOv3c8bpsrCTbv+sdVEM65ye5//Hzza8ffOWb1j79/fJ02RwvGjuHmSoaZmZnJeI05GCKC954YI1BSDt77h/zecbXDeHzG13qKKbYDps7Aw2A8LJtznI/2vY+ErZJL/Eov/1Y53gfjoY7/wTXi49dsTgtsfu3YAH3yk5/k3nvv5cILL+Taa6+dhN/H/45fv9nAbg71b/7u8Y5/8zx68N8ffLybjTncz3V4qPdunpObnYjNzzeHyTc7MONx2Zy7Hz9XVe666y5ijFx99dUAX+IEbXY8Nn/m5jz55r8/ePzHYzo+vgefN/CAcP+Dx/TBv3/wODx4bjzU2GxOiYy/b3xdN2sWjP8+HoPN6Z3N5zN2ajZ//4PH/MH4cnP3ycSZvi480e/fjpg6Aw+B8a4AmJR/fTUT4eEW862KzcS2rX6sYzyUcdiKeCgDtZWPF+6vxvhyxm0rYDvO3Ydy1rYituPYfjXrwjhyBJwVUZ4pZ+AhMN5N1XXNgQMHJiHFr+bzNhPFNu8YtyK89+Sct8UNP96xbg6Fb2Vsp7nw4B3+5p37VsSDd/ybIzpbEZsN61Y+zjHOpnVhnFKr65qv+7qve4KOcmth6gw8DJxzLC0t8eu//ut88zd/86OaTA9+7cmTJ9m9ezfdbncidrOVsPl4c86srKywZ8+eSehzK2Ms5bt79+4t6bk/1FzYs2cPVVU9IAK1FWFmLC0tsbCwMOFbbDWjtfl4VlZW6PV6zM7ObvmxBVhfX0fkoaWwtwI2j23TNAyHQ3bt2vUlf9uKiDGytrY2Ics+Wowdn4985CP8t//2384KAampM/AwGOc1n/a0p/E93/M9X5WhOXDgAFdcccXkM7ai0RqjaRruueceLr/88i+p496KUFVuu+02rrrqqsk128q45ZZbuOqqqx5AkNvKuP3227nkkksmYdKtPBfuuecedu3axezsbNm9eo8zwMAcGOAwlPHPIDx557O4uDip4oCtPRfW19dZWlriwgsv3BbrQoyRu+++myuuuGISJXo0GEdrDh48uOXXlMcLU2fgYbD5xny0E3+z1/xg5vRWzBM/3PFufr5VsTkfOD7mrXS8Dze224EzsPn4Ni+IW+V4H47kNb7PEoAKPkLqGkmULkaDYQhdpPz3JJzPZrLldlsXtuKxPhLGc/excAY2k2zPdEydgYfAQzGlzxZs9fDfFE8utt09IYYFQQQqBFGhciAGzmTLCLJvJ4OzFdNFjze2y7V4PDF1BqaYYoozEgFDHFi7sHsVRMGJIFYcgimmmKJgi/jFU0wxxRSPL8QAE7KAIoBgAmCo2IRHMMUUU0ydgSmmmOJMhQkYYzeAKBkTg5RpTBlK5knkD04xxZbC1BmYYoopzkwIIIJvf8xiWGyQxVV8HGG0pQZTTDHF1BmYYoopzkxEKQ+yIaoYmXx6lU/95/8KB++hQ3yyD3GKKbYMps7AFF8WpqBmGBlDyWbluRmmNt1cPRzGNe6Mh0hxpoCW57rpdQ/z/vHYK4qaTsa8/C63u9uzEOPTzoBCAqI4rE0NjF/SjjRC0RiwpmHlc5+Bk0v4s3TovjKU0RuPn1MBkzKXDUQz5GGZj9kgK6ZGNiObYqrlhVNsG0ydgSkeGVbu6Yii1oBmFMgGEVDl7DVIXwFEBTVQUcQSngRkEoZYYbc/InIxdIqS0eJAZMgoidhK6JylMIozEDM1MHQey1IMk0FHoaIIDuEET4VIIrhTmKRWaWCKh4ZhWLnHDSQ5zBxjXce8tMSpD7yHPKixaGhqwMpcTaSyMEyxrTB1BqZ4RJhAcoYDHB7M4bXsuDIN6usn+xC3LAwwZ4iAmKASiBIwPB7BXGG0PyKclfpfA8OhDvDgcHjCk6qg92TCABVBnYIzvCXm4ogqNagpyQkmimBtBQGAlFCLZEq1oTBlED40DMFweIpGQw5GbmsyAOp7T3L7f/qf6OISo26k7jQkB50MHQTzMt0ibDNMnYEpHhGCEcgEFZTAyBXnQIBqZRl3+j6wra8D/6RAjCyKoAiC4snSQSkyucmVML89TMG7UZwFUUNwOBEaIAk4A6eeTZbu7IIYmUx0hnrwccDcqRP4wQYqJYGSHyrsYmXuIlNj9cgosza0QzgKirM8Sa2IZfY2NVWq6S2vEI4fp8QFwEyocWft1NyumDoDU3wZKJJrSFAbjEi4nHBN5vbffReHfv0tELde86WtgiQtr0INn6GXpNXLt9ZgPYJJEsjkkhqwEgPIJCRHiIaonLWRASiOqriSbvHDIZ/5tV9j4+//Hi8Oh+K+xNwbaJsFPwsV5h4NzGzCazEzsEg3JdAMGJobUtWA1az94Qc48P/7ZXwcgTeySJu8mo7xdsLUGZjiEWFYCcVSSEPz68v4g/fhR4l81330bzoAW7AT49ZBW+VuCVtdo3PiFC5HUC1ply+zYBoG0mrom1HZCDl2AoZ1S+o8e+HFisQwDmKkc8ftpDsPIiZ0TXAP4lOYATlNRnxqqh4Z0joCYpHZPKJ3YgniCLGMizWjkMEl3JGjzH7xLmSwAZoRMzpMFR63G6bOwBSPiIywLpCD0M3G6C/+hoM//Z+wwYBKle7GxpQ1/LAQnLlCFCRy31/8BQff+cdQbwAZ3/7tkcySxzBf6hHElLC+zI3/+ZcZ3XMQ9WdvIYdwv7ExESRn9gwHzDYRFUGyfIkxEuH+0g6zqTfwCBAEZ4IFEGvgyFEO/NpbsNEGkHArG8xGAZQ1P0J1CKMBkhQHk/TCFNsHU2dgikeEAzpUZY9lSj50EL3ti1hoyC4j26Bv/JMGg6CtUZJEfdvNVHfcieUhRsZnwz+S7o2V8W/al4gZrh7i/+EWbLRGknQ21xIAxVmtHZhTujYiSKKWhxvSlkDYpgumTuzDQ1pfKUqCXOMXl5k5cDe2ehqxiIwi3eQgwYY15DyC4QZqmbp1uqa+1vbC1BmY4hHhTOjlgFfAKVKB8zVIxAXF6lhy2u2jpMet1BmrMWkefxZisnuVEs6fj4k5HSFm4L58sHr8l8Kcb4exUeaaDcQaTPPZm/o2KMoBjkQpHxSTllApmBMMT9F2KKWwUHLekl1xbIE81soo9ZttKW2plz+Lpy73i2QIg5tuonn/Dew8doTBbbdjJshwhKiDZLhRQ2gSuY6oGBFBheJsaalNtpYj0z7dLL4xxRbB1BmY4pEhDpxHQo0YjGQOs4iMFBeHjIo2Cdbe5IaRULDULgRP9gk8yfCAM5zNMCNdmqoB6SFSoWFiux4Bjq6M104PtgPxI8RBTzqFjHi2Qh3BaLUbPKo9iJ5uLtLDCU+2BI2hCSSvI82QKldAqYdPFIfV2tJ4VVpyJ5zVMS8x8EYYGUf+4I+o3/QmZk/dTvribagGSA1ZM7iMHw4gRbRWvKuYAXBjo986A1irsMFYz2iKLYZpC+MpvkKMa4wdE1m9nImaS+66dStLKWICd7/y+1lbzS0PfCLI2GO6/0+PNDCy+c/jnxzWKhG23MKzFG0dhRrOl+eNd9SV4bySRXFUeDyo0YnK6mc+ibvrMEEzEo2uSRlAD9kXgR0BAkIFrTWTs3LyjtNSHmBY06mb4rgunirRrpwQFDRhTUPIiiUFPEXdASoHJTXTjquV9eH+BeEsHNgtjKkzMMWXxQPqhY2y408Zcls/LyWMDUaVcyl9q6SEBVvG99kI2/yDgYibOAMTL+kxoEhBT7dWQOFlACA0QYiVMUOiYxmoijyxgK5scPDX38wCguQG1CO5GK3Gldx4xxKZQCTgM4i0pI2zdP6OIVBEsgx0ZRUxsJQQl0ETWteElNGUQYo+gUpx1pyBbzkEDislChPNUn9Wl8ZuNUzTBFM8aogZxIykTBBXFgsznCky2MDuXYQcEctnPUdrUq9NyWl75QFSrY95eKZyryWU7cAhiBVVAWcZvzHCDt+H1A2NCckbpBG77jvGjvU11CJmQm4dWIBuHhGO3kdndf3+zz6LY9kTE23t+La/8zEVAoYWR980Qd1gmtAUMVPEdLIe6Kb+BqJFQAsUHTMUp9gymDoDU3xFGBstM52EuyUlXOvs+2z4PGLjH27icz//RthYh5TO+glWBG7aJ6p4VYj5fvGbRwsB5xwun72GCtrgSuk+VIiD6vDmqDKsf/LzfOanfobRfcfIAhoAl5H1NUJucK4QOKMvsto+G+7kKW7/mV/g1Hv/AqdF3dC+bOOIMxet+X6AwTYzyAoIolaUnZOVqIAqmhpEIyFHqjrjm5pAJrUOQVsg+4AGUlNsHZzta/UUXwYPjmjbmAqcczFIaiVSkBMiihw7RrrlICZlRzH5kLMU90cFuD+9ovrYk/3ast85yzdWAipWjEpbbuHE0YyG5LuPoH9/I1oPCCjSFhnERiEJnQhYJo/3rWrYaEjnxptoDt0LJuVzz1qyS4FJ6euggJoSQiDFtu1ztiKHnR2SQTWTpJRkuJTJSyvY2gbSNAhGmkQCSgWInc0Du0UxdQam+PKw+//V0qYQUkZy21LXjdVcIlVqmB01QMKcEKddDe93CNSwlItD8Jg/rOULnO35l9acFBIhgFBrxnUd1gyY1wZvDZUqLoNJYGiepI6ZRpAUCRTei3kBn6gY4l1GxRAE03yWO7LSprmK8+pnZkg5F5lsM3z2kD2okM1IGhFLSEp87Ld+h6Mf/wSSI2q5lBtOrI1vH1NsJZwVBEJ7hIXTzJCzl5L9ZTFWcVMJIIokK8twTrjcYFbIQs4LWICs9F1CpIOJ4La1u1miIOPwprRlaGKO6Eor5w7+4QnnBl5LrbaZYsRJn3cVwaUhQgDLGA6tumAZp4qKJ3lHhYP2u0UmtW+teM72xldiZx+x2GJMzMSBc7hQoTlh1lDliKQap2V+moPGBcgOZ9Y6soIT0NgAjuiNEIQkRqWGSPX4nOgWhEFJ9amhTshiBBOwhKQRGvqllFUbqnqIipDnZnFqoEokkTFIgmTDW8TFDDiINat33E46fxel+sUQsUk04CwPuGxZbOul+tHAWuGL8UNbApaqEsehr/b5FA+EKCQqsgQkKqYO4ghHjSbBaynPIvdLKNyvYywgFopG+XZ2tkwnAjVqCXLCaiPqkEh8RIqZ0C625lBJ5LxatBw1Y2psfOAGBh/5K+5761s48nu/h6zVrB46zN3XvwcbNZQsjJBpGdnj9AxW+kFs42EdQx/h8Ui4n9ImZA8ETzd7utEQa5jZWGXwoY+go4RohLSCkeknJVWGiZKpWL/x06z92Q3YRoeGLs4ET0RFwc7c3atRav5NS0a/MUUV0uK9HH3372D1EDK4NGB+5RSNCaP5OXydIDU0UhOlwZqEpIZO3MAPIuo7GMbe0Todp1jVoXIdAgmxDLncF55pP5OthrPGGRjDzEgpISLknAsZyzlUS8jbbe+t7BODtkxIzBBNhJxgMKLaWKfKqaUEjfkBY3rQGWCpJiy1duFs88jmoVc3zDR1K2v78G+PQVkNEB2EumFmFCFm3EA5/Lvv4fAb/hf1b7+DlTf/PnLvCQaf+CyHfv+d+PV1+qPS6bA0KWpH2NqowBmSJpBHeDwibHPUqh2WGPGVJwajipFD73wPaXUVSQkZ1cyOalzOhKjQlrwuvf/jHP6fb0XXB+3nZoI2qDVt74czYB4/DAzAF25PF3AZ4qFj3PTm34eVIeop3IBhBHHozBy15EmEsIKSMqBUF1gysrhSgdDEwiVifD3H7CPa353JI7s9cVZYvgenCUQEVZ0Y/rFDMMXDwVohvYxopJMjw1tuw267gyo3k1cV5sCZYaQmaB2CZC0DWjLZG4NPfY7RZz6P6NgZeghIMeTeIIwa6kP30Y8R8ggkMbs+YG5jmV0bp9kTV8FO0z92iIU8wMKI7JrxoD7geGys7HgGDPVjdgZajAluooYTiBKxyqhM2aGGSzXNHQepf+vt7F5ZIwWlkRKx8dmYGTbMr662ERdBYs3oY5+i+cItX5UWxFbH+LRim+Ly2bCsVGsjLjg5RI4eL4TgBLLRgHhsbgcjEpCQBkQzSiL7kjqzph2wlHCjWEoJW6ftwVIlZ8DUPeNwVnAGRISmaXj/+9/P5z73OXbs2MH3fu/38oUvfIG//Mu/5IILLuD7v//7mZ2dnXAIxumEMR6Jd/CVYHNqYvz5WxHjkP74/KUNT5f8bEa1QXLDvR/9a2buO4ZcuW+yS7VWbcxJabc72b0+ga12N1+nzdfu8cL9nfEohhglWeKe936Q+X7gwuc9H6N62J26TzCvhp1cY3jkCFgDVcSqGu2Un4PPZEbQi7gTx6liIjslVx7/YD6CamlrYHa/3OsTiM1j+2TgEb9V2kKC0hADULJlZFScLUjgE0sf/Su6//1N9HSDpnsZdShEThGD0EA1AouoOHS4wdLv/yn+miuZf8bzQNwTbrierLH1QI0QEEQEcSBNzdxwiKwtEeIAokFUcIL2ZzCNkEaQwawVbXbgvMOSEpJBHQkxIqolYpMNCZsqktr/2f3PnlB8tevCVl2rH2+cFc4AgPeel770pbziFa/g137t1/jsZz/L+9//fv7tv/23vPvd7+ZTn/oUL3nJSyYpgzGXYGzEH23eezwBx+mHlBLe+8nvtloeffPx5pzJbTfCcpyKmC+5blE8Rm/QMJsNp6lltyvSEuVQxWJEel203bE9UdjM/RineR7XsTVtN4iufapUGHMbNfOulFRaeLjFYpyZlaJ6Wzmi1ujGKmnxOJJHBASLSkdD0dA/dIqdG4IbCb7vi8Z7KCtnGduMpYzFhOVMG+d9QjBeBMfpNOAJj6DlnHGT6yfII0ye0lCoFcFKCbNMGDRoWsZno5MLUbCjI7ppHeczPia6WSDXJJQkkexGhfsqgZAyMyePIc2F7bx+4ibveB14wubuI8FaESxvqCkORcWR4wamQ2z1FBy5B2HHREJYQoDVE7C6Arm0KiZlfHBIJyCDEfnEErK4SK8Z4FKrqdGpyDkhUiFt/zKktOR+okIvOefJ2v1Yxna8Zp9NBPOzwhkYL2rOOX7lV36FO++8kxe96EXs2bOHiy66iGc84xkcOnQI5wrz9a677uLtb387KysrNE3Dbbfd9qi9w83G9eTJk5t22rIlIwPjY2uahvX1dXIuQiI+BBTDqWO23qA+uchsE3EbI6psDOohS7fdxmi+R68R3OEjjAYDjhw8yKhTkarq4cPojxNOnDgxOQd4fAmLYgkTIdFDZEgXRQdQLS0zsB6nbvkidW/hId9rGE1QXOM49/Qp6jjANw0n/vxv+PxNt/Ps46dIUhGbEglYvuMOVg7fS1hb4+SBO1jaNcA5IQXwqjiLzNx9lNhEjh45QjpwGzn4J1QcZ3FxkRgj3vsHOAWPC+xLTYGZQXt/OPdlZo53pGQEg3NPL4FG4qduZOnoKS5QI68NOXXLQVYW7+PcrtCtjc7JIZ21mpX77uHwzTeycPo0ebTO4NDdxFHGjh7j9OIh3MqFLN5+K8gT5/ysr68jIiwvL0/G+GsGkyLW5BoaFxDJzIw8vYN3keuGo+++nmO/s86lP/TjDDfWqSSBRtZvOsDJ69/LxokVcoyM7j7MaOk0oQI9fDeffOOvcF5zmmr5BMMjh7n7ttsY+T70GixD1fQZdTK4EV4rnihnQFVZWlqarGPjdfcrHp52vV5fX99ya/UThbPGGVBVZmZm+Nf/+l9z/fXX8/nPf57hcDgxfuMUAcAll1zCT//0T3P06FH+5E/+hCuuuOIx36hmxu23386ll15Kp9N5AFdhK6Kua+655x6uuOIKoJUUECFk8HHEPXv24H1FqLpggpvtc/FVV2LzPXzjGJ5/PsdnZrjwsstwOxaIGOEJXORSSoQQuOSSS/DeP+5ja6WvHU4rkotFgnnguG92gZlzdrD/mqdj3dmHff+GNPRiRTh2H/fOdpDTRpA5Zu5ZYiYEtDdLHK6DKDsvOp/lymDWcc5TLmDuwqfQM4/IuIwugetzqNvhvPPPp3P1VRCe2MhAVVWcf/759Ho94PGvDJFN6+y49bVMnAHHI4WVMmXR76igJ45xb8czsxZJJzdwTsm9LrufchXurpsQMbwY4iuCD+w8bx97rnwa9+3chfS7zJ5/KbN+jv6uvcz3ArZznvOe+tTST+IJwokTJxAR9uzZ86iN1VcLQ1AgaGTki8M/s6YMP/0JBpaYP3aa0fET7LrwfPJMhQVwe3eyczRAcLg9u8jdwOw5+5mbW8DPdZAds9jBu5jNp2mCMb9vHzuuuhqrumQ3wrsKSRW5MpAGZ50n7Pzquqbf73PRRRc9Jk6YmZFzZm5ubhoZOJMw3qG/973vRUS48cYb+ZZv+RYWFxf5nd/5He644w5+/Md/fPJ67z2qreJWG95/NJPpoTzJcYpg7FRsmQlmZQdrFGlRQVFfatudCEYm4DAPljyow6GYjjBtqzCkSMHiHTFHkiZwoA6ChGLMnqDz9d4TY6SqqgdEXh4PlHHxiG3q2FgK08lxQHI7EVeu60MpChpG1zyhze2LOaoEYTBkLpWOj522A2FqFDbamu4USz5cIlE8HggKYrlV3euCVogXcI8cSv+qzr9dEEMoy8TjHca2NlxcKIAOp7B+y80sHryTS7/l24tTII4yC0vEPhhILgI2KkpwrsSdK09UjzSZXj2gRhhUHgtC0EyIGVMh1QPwSnPzLSyvvQMZDqm19DboqRDrEX59gOTCexH3xBjpzWvE1yoFsxmKTaIwkySYGLFuSI0S1lbojtaLIJMlzHVgZhYz6NQ1Q0rKTBFEFUKfrDULg3U6QdlQRVPkzvfewL7nPY+5y/cVxyqUtUAIZWyfoMiAc26yUXgs68LmSO7ZgrPCGQAIIfB1X/d1nDp1ih/90R/l4osv5pnPfCa33XYbr371q7nkkkuA+43W2AF4vBbA8Y2+JSeYlV1WiIpKJlbShmpByHgTGm84wI8UyQ2pXsTcTOEMiIB6EMVyxKFkjOyEXqYYrScIIvKELaZS9kAlX+oML47oDG8D3Og0woV8uVtIcWRveK9IClSxQtKQ7GpwXcwXuXeVHmxEGI0I0kEaoTeqMWeY60ISkJookUgXa0IhyQlf9hi+Gozn/xNiqIRSJkkk0y2Es7/8C+q/+Uv0um/FOY8C3iCL0QAzJvgEWkEWijPlBIJDpQu6TuXWqbNHGsG0iA+ZBKIJ3djQjTX5/Tdweu0P2f/Mb2QQu4hEKqmBmibWzGSKwM4TGLkfG6gnYz2Y1PqLI9DqNnQjmUzKfRicJkhGKJ1IRzaDdRaovdDUDVUzKsqCAj5HtLMXq9cIo9OEXo/GAra6zPEPv425uT4Ll+7GpEvytN/neSJLNTaP62OZu0/WdXkycVY4A+Md+ZVXXskVV1yBmeG9p6oqnv/8509etyUN9dcAJpBRQgdy9viWNFU7qDQ8oNGoasZJJouy4aV0JyODbzCFqmnwmvGaC7GrtDjfxpAH/CQtu9+lcR31I82XB+98pJArU4NKRlsmdqVGUrA4wrSGJjE6dC+jD38MkUTn2c+n89RnQtcj1HR0HadDSu3WE3PWXwvcP3r316P74YiuaYl2SKuvkISOCFKsCBbAxuu7tCUFZlRZkRPHkGFND0+FITjiMLPeqTAfOBeoxVhoMn4wROqalBLqjIxCjOQmtiy3MxuCIMnhW9VFJ4bUmV4EGdSY90CRv1ZnuLmKngpuLbZ8jhKhyR5CrwODAaEeICheEzQjQj1Ecp7cOA8Y1TO4dHM74qxwBoBJrv7BSoRj8aFxJGCr5/SfCIiV+vkmOLIGulFwMWIdYTxFXPs6xSAbeVjTefbXoaNTJXztEqYeXyuigqkU8pWcWfd8KZ80JOa2HeuXg2wiUBpqGWlqDMUsY1Vp/+wVsibQhll1LN94Iyf+y/9itj7FzP/xb+k/9eswXwEZkXXQmiwOwW1rX2ucKZC2dFNHo9b5NExKXtu1mYTgS4CkCAMavpWuERQzpbIEy8t4Lc2JaEZYzITk8S99EYQu8TP/QO0MTKhU2jbbGUNpcmK2juS6dQaewEqCJxvWxgYsl5LCPKaeNImQDN0Ywvw8ZoXLkSuQ3TNFYrxWNEgr8lBaGVfdgC2v01vfwDeJTtPg6iGuqUFzW6K5iRA6SVFMsVVw1li9saEf7/zH0YKp6iBloSWTzQgqVMNEOnyE3sYKPrWVGABi2Li0LSnx6qeQpAL1JAJKRU4BI2AGaSwVewZJjAhAViTnEkb+MkzjVqahLeUynCoSa5xl0Iz4UjJYJUOaTCcldGOZMDjFruFpzh2uMSuRXEEtFSoeNQMV1DroGeJmjfsMpDqWMR1rOqBYZeALgyNJEcpxFDfVaDedqjjLqCm0Y2raFEMUhebqK+GZ15ByJFvGTKgUiA2lN4SRNCNNwpp0pgg8PiyMcn8WZ71tRmSJlBowQ5tmck3MhI2eJ+2eJVIiM0pqP8RwanSdJ9xznN6JJdyJRfobQ1w9omoaJOeyAkwiA7bpMcVWwVlhBcf5n80kvnEu1Hv/Jc/PqlTBuN7YlAqlk8CWl/nkT/4UR//8Q6XLnpQcowiYJZwqPho200XNg4VCajNPjcOpx9eZKjZki1/+GLYoHrxUCaUBKwZOH8iEf1hImypQw3LG5yKKE1KkirH0gk8Jnw0ZGc4FmpXTDE8ew9IyaMI6ghOjExWLhlkFKbRJiO09V8cCNEIZz9xEcm675GmmkwdkGmIwEvdHmMRKlmTym5zxpq1jADhfRHE0kTLIjh3kbhcfMz0tIlrdLISsWFNjOaGacHWr4aBnjuTzwyED6gwTo6sJ34xIeUSyunWSDJIwokKf8VTc3AISAjGt09EGyYZEcNkIVYBPfobqvmP4Q4foLi/jN4aEwQjXtI7D5sjAV3TzTPG1xFnhDEzx8LA22odkQsskrG3I7gO34D77eSCW/Ky1Km9WdgI+KnQ7jOlAoaUENVrj0hAOHkG+eAC/fJztvAN4yP2LFcNu2b7sqd3/fiPg8AZ+VNMdNfRTpHNihWpUl+Y82cg7dzK7cxczx05SaSYrOOexxUXswB249RGmHsPjrXVOtjvaFAEGsWnwzpX6lrUh7vBRJA+INDiBjrUVBeP3MX5vW5KIoR7WgEYMs5qcElL1wQV8TMzUDRIbZgy6S8v4nAuXwxTqiMuT+ponZzy+BhAr6SkLYDlii8vo6VVSM8C7jAw3cK6tptmxh8v/5Y/iqp1IVsJwlXDkMCFb20VTcTMdWF+lS8blml6q6R47QX8wwiUbN/do0f5w5g7vtsTUGdj2KKvoePnabLw2Py/qwMWYT15b9GzJHiLt35wRu5GF4WniDR9gNFonQus1lPc4NUKtuE63VBJgBM04TaS0Sm/1JEf+w89y6w/9S0792Q2I5q/9sDxueKDBF2h3+eN2wu2rxuPaygTbeDwnIjolQhUUZFTjFk9ix06Sb74DPb1MHQpJa9CrCOap/uxj7IyCsw7VKLL0R2/ntv/vv8UfOYKpYqKINW21+PbHeKPYjOoSmVPj5Ke/wK3/6b/iT50i0OAs47LhrW1ONMnBlH80G4Yjeo+/6BKGAqYRjQ0hdEDBNTUzowZSQ1pbpb7li4SsiBZ5YmvJg2o2MVbj+6e9pA94vmWxmRvF5jWgTddTiKtKURL84rvez53vej9aDxEtcsJmCXDU0kP3novFGbpZkI/+Ff6jf9l25FSyB52tCvfAImaKpMzok38PJ07hUymrhYcgEE6xZTB1BrY7Jnd56xJMFq1y1ytGY1YMutJKrBqloAuwDKYoXVQC6g2RhsoSnaUlSA3OoHGJJBmfBcsBFxuC9+Q236jk0lY3buAGI2Zvv4WdB75Ide+Jbc3MfuDiJYiCibEBWB5Aswq5aLDHQq9sa7jLuAYzxKxwrURKnnuwzsygphqOCG1lgXUF1cjQCdXcAv2VDboxI+qwRugcuofZO25FNtZxSUBrIG7zUPZ4993ymFXRWGPmcQZueYnqtjtgmPHmcdb29pi8PxfyIAIqZM0kUWpX0XvttxL37KIoOCeCOMDIKeGz4mIk5IgMR9TZUBM01aCJkMd19/olLnYGakqRzDjFsWVhWmak3e+sokrCGAFGxolCTtRHj5Duuw8ZJNBMM9nM12QzcrdLQvCporr3BL21dZxZkeMmYTN9sgnBF96wNwjDIYEIEsEckiPduEK2VNKLU2wpTJ2BMwLakq02RePa1cqstBgdWmkxatZgLVlwBFiu8bkmWJdMIPuES4bLDpeNKoOPxogBtY/47Ig6g8QB3eBIppgJI1dUhnQYCdnTlQGeGjeq2M6s7MmiT+FWOBM0Z0YukP/h71j+o9/BNkaYKo1FUi67ymwjTCM+aanCCI6Rd6gY5CHVKCJ1CU3HILgckdEGIhVWzRUnDcUk0fguvfUBPWuoJSHSxw1HNDjSNq4lcCgiho672FgkaE1jHUSEarhEP66CdDGZKYJPPmNSjI23GqMhS2EVBp/BG0k62M4FBr0O5ABZqYLDCYxyLCJZwxEdbXAYte8Vhaw0AKvpqiegRcdhczhAy/01Kj9ubUcAA8ulvXBr2NUUmkQ0pQHwhpAxPN18mk69jK99OU8PLnuwDVyucd6jc4lhbxedVL5BDCyPcClivVm8dgm59CIJJgRNmBuCq8FVxMN3svi2X4fBahGJmOYJthSmzsAZAWnJbV9areM1098Y0IsJC4K5UuATtC3/d4WaPX5/MKNSj6NbQnspIWIsDGtmRwlJdYkCWEaqokaYXasQF4QctdUzaxnKOX1th+JxR9m9GoK1GgOSYGGg6Kdu4ou/+YfoWo1ko6tKwPCjjK9HRJeoK0cKjuw8sdshB4/WDWVfppiA854gguWMimCdqv0iwCsa19C0UkhdsSZJwtRQEXQ7r6dtVGszr0JzbksJFRkM0Nig6f6aiXH3yPLEky1QuxKtCdmQFBhJRZ6ZLdcsJ2KOuODBOYI4nCqubtrP1BJxyBmXtJAHx/wYPGKufZTrH4A5a5kyW3jsjVadEkANAZIouEQ/NiysjfC14NTjVOiMlGpQk0zx3Q5ewatABGdFj6C3dy8Lr30ZtfeYo6hhunJ/S+iBtF1OvZClzM+OgqRSTbB66F6++BtvpTl0ovAIpthSmDoD2xwTAiBltySb6dmWYXWNE29/J/70KbIzGtoOg9ouaGP997Zcy6ngalArJWzS1ORYc/pPPsT6Z24m2hCVuljErsdJMfoBRZ2QmraTHoKhSGqepJF5fDDuxT4eZwPISncYmR9ldkcFyaRqPH6ZeMvtHH7bu/HDERklCZAF5zoE1yGs1MwlqDSX9ziPx5GbWIx7tyr/SnEZ+h/7JIO/+yz9JmF//SncySXAUanit3WaYNPusM3/5xTxaOnIuL5OahpSLGx0+ZJzDWQCA4ozIAlEKlZ6PXTHAkiA1FCnCN2qcDYQQsrIaIgYOLEikFXnUkYbM4qWagJ9QBauOG4GYRwwYAsHvQSStHJhbRgjYxAUPXWMxT94F7qeQH2RGG6MznpNQ0JnungtZEzJELLDaaCamSU+6wqyC20IUkFHGIKEWVQyqeOwEIgCjRc6WfAxY86omoZzjq+QF08V2c0pthSmzsAZgLJEFkvlKPe+UurY89FjHPzN3yGeOkFCESmqYigtEcuR8a3RK/ltGSlZQmkaM9iAtSXufvPbOPbxT0EelVyqGNLxYEaVRnRjA5SyLDGZkBQlJ+7fym0zGEUAp80VZAFzViSbrQFRgjO0a6xJazS0ofn4p7j3rX+MO7XKTMr0GsUPlU52mDjcRoPPSuMMLd5AkdlRLcS4bkVuv7/KGf/Rv6V/5z30hwn3px9iZ0pkMULOJZy9zbF5dlhOhXCaM9QjNGUsb1JT2ETyH0drHCB5BJZoXOCyb/9Wus/9enAlshVzhCq0zoAjtLluAZwVQiIxl/LOFMv8jjU0Gc2GuuJlF2kiw9TIpuQtXB5nCAlptQLKBC5JgYzec5C7f/t3iM2IxpU0osuZMKpJZHKvKp06XQafi8MpZazTXI8sHq+OoIarR5iCVF1MMnXl0dBtOTIluiC5cDkqyczEBpeHWJW366pwxmLqDJwB0HaHJW0M1aAsmJqhbti7sYHXCAjeQolvurGSa1EiKzXbhXXgm0zOmb426K234TbWOWdY0xs1oImqbczjOl18Ulg8AQfvwTdDOimVMq2Wae9i2ra+QKl9F6SNxRfRG8N8pvFKlEzlS/Sgi8epxyxiJ4+zMBogOYLWmMvgMnWlxK7DVlbAlBTKiDt//w5OBbRXkVFUocrg4gY9jfg6UnllxIjoBPNjgtsZAgHNsTiQpliMk3kkm/4/+dEMYmRmdRU9dBdd6rIbfc4zkfPOodfpIesDNCfodh5QUSOUXhsmIOKKoqSUzpyVJDqjIfHgYTQrNUrOqXA/Sk1eMapf+xF6dLA2TeAKyTLgEalw9ZBd9TrZp1YlOON0hDSjUlVRdYBCBrQ0JDUjtHLk4JEIEqrC02gS7tC9eBw2M4O5TBJP9N02StbKu6uiIiVS6cB6QuzIlDKwxTB1Bs4ATHKu7Q9Kq3uPQdPg0wg0IQg+FV0AC8WMFEFbN45/AwnqEcEbfnWVA//Pf2Hl81+gqjw4Q5qIU3ASEN/FKRx/93v43H/4OWywhtQj0Fxkd6V1BrYpxmFga4WDvIFvmWTZIuYN3wk4FfoJJDsIwvLJoyg10KC+ZuTWSNWQ1X4kzwXy4il6BlUUvAKtM6AtZ4BOhbr7a+nVtRl1Fepz+tRdw6KCRdj6JunLYLOBNzQXUSs0o01TcvdqD+1QOqWfE0t//lfc9tP/jmrxRIms9LpIVRFMIEY0JSyEworX+ymhIpDEkcRjSVtlx4yLEb3xJj79n38J1pdRalyK+FGDWkSd4lzY0ro5MuY1UJwXBSoVnHmMDDbC65BqY0gYNGgaIXFISBnf6SIIIUVOfuLvSaMBRiJJohNLVDGfs4vUDay97Y/R5Q1srle4SHjMdRArjqxR5IwdAtFQHOZ9W9A4xVbCWdOb4EzGpBfQuKxQSnMRMNJohGohRXkTJIJ6R3Ql/znhGSiF8OcUiSPIDVWOzB06TLp3kbojpDxkTikGzHmc72Ap4T79DywcOgIb63TqulDj1JXGJzFNyGHjf0r8YrzXswcpyGwhyHjn3vIBrKjb5cYITaIyqPse67iSO3aulA/mRFcV1iL6hbvpzvTQ46fYMYzlhlNFzGOhLUUMHieOFNOEM2AtjyO3XAWnkFo7NhONXi3ooRO4S/pYt7sVR+/Rw7Q4A9aBnEl1MymFLZQCmwgLldmU0MXjzN5+D+HA7czGhrVOD/EBJNDNAqsbWMpFjtyUzeIBpUGXkMRBymQHxBGhLav1q3dhTY3g0FNDnHQI5+0AivARTh6QtthqCO0tp2JkhE4SxLkypyzi6wZOHkeHNWE0wDU1oVFCp4eKpxuNlS8eQlyZeOqNjnesW6Z64bORk2vMffI2VnbNorMVTozKHFmKo+RpnWlTfIY4NJxVSKqodJzfeRIHaIoHYOoMbHeYEEyKYIorXew8JTcNiVgP0Byhbkruz7es+AftatRTBGzMo3WN04ZgMJcildWkUPKvDEaoGeY8UvVwJ48Tjt7HjgsvKn3NmxpM6MSIZWk19AWxiFKV8KwK5sbipO2qvCUXBRvTKyb7GDODrPiUEYXoPRaE6IttgAqXanr1iMEd93LXL7yBmRhxc3PM5aboNIoBmeQEUyOGHuIdapnkDLo9RITB3CyuP48/ebxkfSRTbQzYoUK4524+/9Mf4Mpf+gXmrrx6crgPGMYtOaabsSn4LxTWe7S2I6FizQAktqH9+4NX2SsRodson/3lX+LCD32cvaOIh8Ld8IXA2h01rP7t36JNg3V6OPWMuj1kfo7q5MniDIvQyRnysDQui5GA4TaG7NgRcTlTJeP23/t95ub3cv4P/TOs6k18260+xCpCxlMpmG8wPJYCiCeurPAP/++fIaiyZ15xdcLnIdbtYE7wp1fo/u3H0bkZmOngtIO4ilEX/FWXk4e34SQTFDSUaEK5BWz8E1kML+Wel6gTJq7TokcwxdbB9HJscwjgcjFS6nMhtZnDEwClGW1gOSLrG6W8qKMIRsdaYRUrJnnoS5IA7cMoUhGxXHLgpFVcEvxGTVBAjOQc0p9lPg3ZuXIaRxEWIdU4dcyMakJDu/gYNGtkNcTypF77/tTE1oQAAcWjKJBF2o55qSivKfRyB5faYkqfECrMhnR1QFpeprrjRs676VPsOHgr4oW626GRhFhDNwqVwaC/QAoVLpfgaT7nPAZirDz1GYTXfAcjX7XOU43UG0jowMph5m/9Am4w3MasgWI+XEt3I5aeF8k7kAyDNUwSaqXFrjoPSZFmlZQiOjCqz/w983fdXASE1KFOoCpszl4zpHP6FJYT5mdQrdCrryK/6AXUCIonmDBbj5DRaYJ6pBFIgmVj6IfgIFiX2XuPMHP08KRj4gOrILYmTNpmRHhCbaCLGAk/Cqh5JDXsv/XzXHrrpwnDTBgkhAF5vo+a4jfW6d17B3uf9XyaXTNIM0OWCuZ6uKc9h9iZIfmm9DaRHqYOgpHdBuZLszJthaHEAbEuXBhpOQRbeOzORkydgW0Og5ITFGlZ/A4yuOhAPDZIuNiqA7Y6pLL5vW11V9esZWcLud5ATEtznFFN/su/Y2Z1RCdnpGlwCriAzcwWIpEUQZPxzkotl92zAKmGQ3dz+i3vwKVU2Nre2onX6rlt4eSrPOjnUmpm5JzvJ7HZmGxYXmDadiQ0xYsRUFwrySzOI278SSVMjRNcrOHPP87CmjG84DxOzHYYzPXZOG8XTadwOipx5CYRexUaIIhM0hfQ1r1vfmwTTA5V9QH8gBwFF/uAQ6UVztGGe3/3D6luvAk/inQSBC3G2cYhr3GUIZU20UkVfJeNmT7LT7mAPDuDN4eJEJ0hlYcYi2PdRJwZbtSUVshadrkuplKGuwlbd9a2U0BLL4fKDK1H3PW2d2PHTiExETSDGpUpPkYkZtKx43DoMGF+HhEHCqOUWL9gDzhH7Ak5tFUC/RkIDrOEeYNeRe0DG70OWTyotXPTkNxe140BTloCp5xR1NczAlNnYLtDILuiQSjWGhctzHRzQt4YIjFDHJdS3Z8znXyEGqGuWyKhkZdPk1OJIMzEBH/1CdzKCpKHQKmNV1ehs3MMXQmRK6UcTOq6aOeb4kSQZkg6fDv3/f47SgoBJY3VWkQLmWkbQDb/q4aplrRAW/s+vpGkTSOojhc9acsGy/UplQMFxRkTQgjMBIf83WdYaBw7X/hczn/1K9kIAbniYmrvS2jVdcl1Jva7Ja2j7YNiA79EN3/CLN0mUGuNL2CQGi35q9RQpVTOsYnc9wd/wtp7PwDW4NXTukQlJO5KpQtamg+pGiknTAL7X/ESLvjJH0X37sVRCIVu1050pltKGVWRJoIpUo+oUoYUi5MS4/jiF8bCNnC2ZMwtlUyOQw7/1h+j9xzD0ghvGUmFQExWJCXiyir50FF8vw+uzNPerj3sfNkLIVTUlSGVgHNU3RnEVYXs6RXrOgZVRfcfv6otLSwZaGeCxARqpJWVUjaqui3G72zD1Bk4QyBt7l2B7AzzikpGm0HJ5Y+FW3iI+3BY09x6K67JhASyfJqoihoEjB0pY3lIDrGkDTCoulD1UfFkaReVJuJjwkmR5BUMNEGzTh/DxUwsgYuyy8PDpJJh6+LBm22zopTHpFSt5UIZSLa2V0FRYxPnWmNdrLVz0pLgKOWZKphzRDI9hOwgXLSPzpVXEKRHtXMH3dn5Ut5YdUtviKpCkXKtYtnhjZtOjYdyiw/pQ6Pt4+AUyGWMjSG2dAS98y58UlwWdtQJO7EIXsuYtyGucfTAQuEMjNNSLpfmx71LLqL73K8ndmeLQ+odcsF+0kwPmogzwZpYCHdNTchaImo541IsGhxs4rpscZT0vGGSMImcu97gYiQTS2vxBFB0NFzKOM3MRUUW5lHncM7D/A78VZfh1RcSsi+REi9d8J1CQBYFJ9S+i156KcEqpI3WiAmkck8MlpbQ2EBK27ulxhmKqTOw3dFWBHgAadsIe2h8aUNqgzW8ZaxuJt3KHvz+9dvu4NP/178jnziN1AlbWqIRQZ1DDbJGahdRSYRxs5h+B+eEKrsSATCgLgRDj2v3+4bTUi4XRXB1Li13tXRJTCZgFdtim7CJMGaAZm07OrcRgNY+iJaujlBInS74SadHUS27Vim7KxNBcaRuF/OGs8ygF2C2D9UMkouWQzRIVaeQ4EwQXxVlOASGDeQihFMOczuHX6017gZq5Fgjss7a332EO9/4P3BNQhIYiWawXsiwbXMoAC8exENvBpyjQZGkuHqE9DrQ6TCqZrFqFnEeVwVSVdF4V3bHQI4N0qtI5NKIJyUkZyRGxDKtMPV28AXAQQMkSgTOSxERaiROUinjvKGLpU9DyIbM9EniiriS60J3Fm89esmorE2bhC656rUlgtZyjypy1cOr4ExRBB9CSZFJZrS8jFNFVLfF8J1tmDoD2xzjkKohJCllfyZGJuLuvI/+sUWqNmf34NS8QSFaDTdYuO9uGA5b5yKx1gkMXFvsborfGFANItXiCoKQ+0WfPKjitfxrw0ho8mQHBaAxY6NM1oitraA330onDoqGv0rhNXwNx+urwf257VK6KVBaGbe706JUaGAlhSBmOOdwSFkAc8Z5j1mpaVczmtBnqRfQfiCSiZWDXGHmyUFwoUPVm2OxI2zs6JEExAcka3EAYhGTmrAQNg/mNhhYY5NSgrb8h7G2gIIlpVpfYXbxFJaK8qXzNb2Tx+HICbLLmBTNTcta5tPec9BQEcXRO71KdzBAnGLS9tygW7QbKoeEAFUoAjoi5QB2zZM7DkuxPY42TWCKmD7o6LcuDGhaLkVoKS54JZPHMqUU91GRHMmimAftB7ITNHhOe1DfR/BFAAsF77Buj9POk3yRLcd5sqvI3QqxQhKsg2eE4i0BSh4M8FYiWW5rD91ZiakzcIYgI9SANaU5EfUGR//zf8d+7510Uyph7YeAieFcotesYOvL4CBKove0q0l7dqMILind0+tUx1aRu46WTcWMh9CKGzlh9cQixz5/Iy5mfNVBHEUFNWbcRmI2NQxu/DS3/Lv/G1u8G68jfN5mO4QJQVBbgpQUEZsHNbsvKQTFOSGEqnAGsqI5I95PuOgZGOyYY+55Tyd3HeYEZw6GATPHqF94H1HBP+up5Iv20ARBQkA1k2VspNrDGzf92QaBlofE2IlsVQdz9Pjcx9eKz0quBPOZzmgVPvYxjv7ab6EutT0jyvUQAnR60J/hwq//OvLnP0+1dArrluhV1wLOAuoMqzyEUByCpqQDTBWb6RJpDX/OZVxjLKHvcfprC5NexxjzSRwOaUoKyjzktuU2amQrLAhyLvLK3mG9iuiEDTJzz7garSqicyV9I0YOjtyt2HXNlcRet+g0+ED2Hu14VJQkmaZbUe3dTTYFMZrhoI0M3k9oMXvgY0J6meYRvuaYOgPbHOPFXyzRySNkfRmflRA93H4Hc8ePURkkc5iDhkQEzJTQ1MhwDVKkcg5sDQ3CiAWqF74Uvv4ZROcQVfox0ju1gsaaGBwVHTCHAT4ZrqkZnjiMS5E8M4NCSQ2kGos1rh4Sjt/H3KE70eGgVBrI1hd1HYfdVe7fB0oquWp1hqYGLBV1IK1AU4mGmMeJIhVkUbxGfMyoVFiYRTqzIEK87GKu+MF/wdAFnBN8iqAe9R2aSvHBo67D7n/5E8hFF5ZwbOiT1TASpOF4b0cjNYK1hPz7eQRbGfdzMUq42sZheHJbldHBoqCWkGaILJ9gR9Mwv7HB7NGjiBbdjCyUlEEnIN5Dp0f3GdfgYw1xBL7o8aGCiLY734D5bunSubFMNzaIqwhhFrNAwCHJtZGBYXECc1Xy8ORJamhrwrBcGlllXDHUIkg2/MBKmawpjRTOgORE1aaa8As4MjI/x57X/m/gfZsKy2Q/g4nHB7j0f3slnV3n4r3HnCP5PmZFMCsFR7X/Arrf8KJC6rQNNK0h2sWSYFLSbMnA4hCalXKnTQQit/TgnpGYOgNnALKBz4m1L9zIkT/4bfz6CO/6JBng2/ygJl9Y7kTUHFGV9U98gnve+jZ0vcFiF6fLmEuMbDe5v5vRvnPIlMUzWMSvr7DuM4NOhzmZR1IoOysCPTPC4CQWh9T9PlkdJgHLI7I2VMeXSH/4LirdQHMF1sEqjyNsecqAYTS0UVWlEC3bPhApNyAZU8jeoWwU0SfroJJotCnOQD3EjWqyq0hhltjpIQgbfQ8Lu8lhBosjnCvdIKn6pVEURvaBtO8SemEGpw7rzkI0qhyRuFqY2grCRinjGh/r/Svrlsbmy29mOBnHsEdojpg6lBHNrbew+FM/Rf/wCRxaBG9SaeoUHSBK03OYczgfyL0e3jLOGVb1MC8lIuBrsnNIt4O5Lr0RpD//ADMbA1QCQWfxKZRQdnZYTpAGmCZIXZIZcYtXwZToU6arpbyyDgmvhiQjrGeySElTeaHKgZAzMwqYw7lz6GimEaHeu4cKT0dLmyPz81Q50BFw8/O4/l6yBPLCHMP9F9DvLSBOEN8h+w71rvOIBuQVqrRGrHuoCpYGJCtRrZW/+RhH//gP7o8QsN25L9sTU2fgDIDPIDGydOch7nz3+8inVnEJKi2LahTFNRlXQy8L3Sh0Mpz81OdZetPbiRsDUgaGNTJYxcuQPF+x8MxnkDvdslp7gbV1uvv2UT39GnIngBNUlWwZj9FdH+LESD2PN+hkiBZxgxq/ssbw0/9Ap0lIo6VHvLpNfRG2INposFNppTrbfu05U03y267oFQcYBiPnDUIcoJoheSQLFWA5MdKIdoTcc2jXkSQX6VwCTnypMvCALxxDT5HeTZKh66DjSF0hzwT8KOOyTco1K5Tu+ghJSlArhFK3zRbUcXi4fWjOBFP8qCEkZenAXax+7O+p1jaKJHbKQIkM0JarinPgy7JmSCHNOkGc534mS4k/JHUwN0s126M5eA/dNiLhnNAzRdtOiOSMNrG8XwQRV9QjZetOXTA0lIhdb21Ef22A14i54hx4zYjltp5HILV9McRBqArB1Vf40AMcmrV07PSOHAStio7JKHikP0P/yot59hv+L+af9VTAU4UuWoHb2Ye1GtuAfl3el9wAHwf4VONHxsn3/CWL7/1rRI3ooXYl0jPF1xZTZ2CbYxJmVSWMGhZWNnBNg2i54U2g9kqSBusq6soiScyEOGTv6hIWh1TiOfW3n2bxgx+gWy+hXajOP4/c6ZWSLYXR6SX8eedQ75gjz/ZADFeI8biYmPn7L2CmWKeaHFfMCddEqpjpRcNnoNFJFHDrLqYtNq/4QiGraVtm1u4cUYHW+co338LotjvoN4YMoJIuzgQnDq3AJNNUGRfaigPXOgMUsqYFJnelN9ey6xU8aEcQL1g/oCnj1LDRoJTYNQ1H3vIOmqVTxUi2HRC3etTlAWh1BqwVHkoa8ZbxdY1PiX6EudoKy98M71xxmrwrHBUM513plMd4h9k6CEUrGpPiICDgg8e6AX/OXnw21DLmDZFMpQnTpnSdjEXOGzVw1jYAarUzt+j4mkByhlli8fo/49THPkWwBK402Qo54WIkWJlulhKNKxwVWo7Axtwc9GZQBQuC+nL+FnyRfBbPqNvBzyygs/NU3/AMdGGmVCB0ZrDK4Rb62OoQGoevPfQDaek+7n7Tb5CP31e0S+6+l90bimWjEdsm8awzD1NnYJvDoGxfvdDNynyTEG1KZxsxctuqWF1C3QCzpjgEAZgxJAyp6gF+VLPymZtZfue76K0v0el2SK6DmC+brrbjm/b7NL0OjW+9AFVES4jRffF2Ot2q9Q5KFxLLRlMPCV6oUNBU5GHbRbQtdd6yGJuUlq+OiWKWChlLBC2azkg2qqYmf+xzzN27SC8bqkrMuQgQQTEkMUKKhGx4A/UBfAUUI2al92spE1UH6nDtv9rr4H0A72mIJWzbNGAZG4049MfvJS2fLNoOpttvUR3X8bcksmwZI+JjxFvC15FujDgEbYsKi5CTa1cyaR2BcZWNlbI5147tmJPQzjcVQed6xNkeAcgY0RlaD8rumQy5gSbhY6KEisDViW6TWjnvrTrCRW7ZRgMW//h6TvzNxwm5ASlOT9BU5qFq0cJQQ2f7Rc652yH1Ks777n/K7NOeilSeJIpJxhttK+QAlSd1O/TCHE67bFRdsu8Sqw717t1kC0h/hjwYQVSC79AEGB24hXt/+VdZ++IBYMBGXgWXEIzKoGulXHqKry2mzsAZAHWCVQ4TQ1LGXEKDlR29gGsU2RhhR09gn/si5EjqerJTQjPCH7idHdmYOT1kx/GTdAYbdAjgu4gmnJaGLoLHQkX0ntzrICIECks7KHTVE0KnlHf5wCgEXGqJXVp2sslKiV2S0hu+SBhtTW+gdCIU1BddBDNFLGLaEC1h3tP4hIUG80V4pV9DyMooJKxqyBWU0r9ylr4pKZs0qMm+YiNUEALZ2irO1oCZayvafZcN34EcSHPzaKeHuIpMKgt704pAxYadg1EhzJEL4dE8skXHdjMm1Q+bOWNt2Zu3hDt6DGvba7uxuqOAtXX/+DDpIOiCp8woaYWfhFBVrQQ0RQnSudJW13tyJ+B8KPr6oSJ7z7rV1JUvXf+aSF7boFlZxXIDFslHjqOHjuBy2sKjKyX8L8qOlWXmRwOcJjAh51bzIidcyogrGo6ye0eRF+/1qLsV9qynYfNzxUH1FK0CkxLpCgG6HutWVP05fAp0qHAWqKtAfcF+1rvzyNwuBmtrYIW75HpdumsjrlgZMnPrHbCxTFPVNJ2MOKVj4FPbIfTJHsKzDFNn4AxBKaxyaM5k37KFKbvLrgZ6TWbxz/6KG3/8/yQtnmyrADxyap3l33oLYbBK1wS/eAo/bPB0y+5eigysaxcBqi6pCuhsYcNjpawxVxX1jnlSr4/6ivVeh/Scp5GSoRonteBqpR688O+2/u1edteOCiOkjKSMqyOVCEmNnhhSD8kdYxjAfBGxySFhaR1N9aYsQ0knxH37yPsvZnDOeez5xudB5VBXRF5c+62GYd7j959H/9teSdh9Dtn1Gfqy4HZjUcfLTbk+xEiwtvdDMJJre8htgzGebAInJWUGWQlm9EYN+g83kXJTqjOcENtol8RWorhTYa5Utoj3JVLQpkjMFB/CJGJQ5qxBGykgBKRtquPMY50enW96AWszPXxjMGhovnCAemkJSzWkmpt/+638w3/9VWw0ejKG6ytCaSFcNghVGlKluqSbkkOjgILkwvVBU1HN7JaoE52K6DxNpwv40nGwJdAUVgVQefBG7HjSjrIWdMzj1dEET7rqMnjZS6A/T2yG0AxxpuReBx+V2RS593/8Khs334mrh0SfECnpoW0wZc9ITJ2BLY/769cL03Zck8ukdMwZiLU16q0ynlKMQcgeT0V0GV0/ycKRw/h6o4Spc4WpY35lGZ9qDGUwHJJTxklpY2qS22irIOogdMg+YLOz4IQs0DjQmR6D8/aQZmdpQmCwbyfzP/AdpaQxp1JfDLik0OSipNd2TpQnK5g95i1MypvLk802qZRiZKpcDMPqwXuR9Zpehspg16l17NYjWJ2ACgs9RB0z6yM6d9zDwiiNPwhDUAmsnruX0xecz8bXXc1lP/D/Inc9BI9ZqyQJIIJ6hz9vH5f8+39F5/KLEQKjuR7BV9jhRaSOxJjK6hwj2SsWE+qMGpC8BXZXkzHd/Lh/Lj+AOGKAtU1tUiaoMatCNRhQOcMLVK2hd4A0CWeGVV3MB1TKbl9aJ7X06oCq2uwMbLqsziHet86AgO+QQg97wfNZ6XToNko6dB9ufUTHSpQCbZg5tkj3jruKxPeThYdYDzaPNVgrQFYURM3aY00OjS0vIxdRcNWEaUK7xakiBFQcRkCyg1gaDpkzLAipCjBb2jjHytHs61N3S9oFHNEZvasu57Kf/Jewdy9ZEww3qHIm9TqYQfRC7/gJ3Kdu44KTNb1UJLzVQfJscXLmmYlt6QxsXlhU9UuEKx78UN3ateyPCGv/1yqyNaY0Vsg2RderBhImpQVupfVEbETbskABhkSaTqYJDaY1PmcMT/bdoqhHRrXBNw2iCarWUI93WJTOiDl0CBqQahZme4xe8c1sLMxg3RnSjgWyd+ASOttrF4KIrxt8yz2wFLCkbRjQWpb+k4gyrKRxQZO1BCYDyUUiGFMyxugf/oG/+Y//iby8hObSg6F3/Bhf/Plfwq0M6WVXBFe6FW49E3/3HfTWBoWURSZkJT71avqvfCV135P6XWxuFkWwWnHOwLVt4aysiOoCg4U9pI6hnTlifx6ZqVi/6y5klMjRMDHIcSLWg3g8k8DNkwijMBlLH4eIMdKMqdGQMdZbgyZYanP91pYVmmJWKk28hUIu0absztVhVGibeqLqkKqKtYUFqm9+BW5hJ6ZSSJ4C5mfBVTizVk7bYXjUV1i3j4UKFcN6wqg/i+29GHyFjdb4wu/9Hi7VVKrUqqANC5Lo2BbwtAzUMnmyPhgUugg1ZX0Q9WgelimlPbCEpaYtmTQa7zBrEI2Ym0EtYFWrnqmtVZ50GKyAhFVdtOdQ71BfoTMLIDUJpfA2PPQ66I4FrNshat32I3CIVJhA45Q5VdIv/zI7v3CQTuqSJZMojmzcnqZpW2PbjnjTNIxbyaaUGI1GDIdDmqYht6phqrq9HYEWZsZ4jWwocqKSIKcMo2Wol0jZlZa5OsS34WZtd/aOTEiGWKAJvs1JF8Pnk8es7EpdGjHfZHxTI10rTGMrTG3MCkO56iIWyDO7qPbt4byf/VnS7l2ozKDVDlIdqRjhcFh/Dz43+NUNJJXjw2ZKAxpRcKV87kldVa3Y0Ib7IwMJK910sTZXbTROCPfdw/477oS1paIxJA5JNbZ8HGmGOAWrGtZmAtabx6+vlnOuKqIXUlb8N7+Uy3/sJ+hUjlBHsA6CR1xViJ3dHphHpUuWCqeOLrM4GjT0yH4WeuAYErKgowyhhHpDyiBlwe2rlOv25A5t4YtYatNJRjbDajBNyMZJLEdSBstS1DCt1LOT24oC2siTejSOMIs4KrJVqCQ8ivV7bFQdRpdezO4f+pfozl0t3T/izbAwD66L04ikhhgq1qpZbMdecmcG8R2cd9TNgNVeH+nsZUaFDpne6hIyWCPkjFiAUcTlAdrxpaT0SRzb4sjm1ggXDsVYHjuRSjolBTRuUBppz4Kr0byBx0PMxFYe21kCmyVpBT0DS4Wo6ShpJwxrKnKMJD9D6nhS8OTuHNbZSycPgIx6V9gxzhXhLe8RL+QUGblA3/Xxqsyo0mlqZpaPFgJzDshgjWplSCdmtpk26RmBJ31j9lgwjgbknLn11lv5sz/7M+677z5ijCwsLPDUpz6Vf/yP/zF79+59sg/18YGABnAZ+hQlMDTQVePo710P+3Zx3iv/CcQaKmnzpw5RX3KqZCxnqljRa7ognaIipkpH21CrGS5pcSTqhBNXGsa0QXyh7bDX7bDe6cOeBTRU2MwO1s69gLUVkIsvJB0b0d9YL5VcoYLFRVY//nH2WAJRcm5KxzSUsSvQfsGTAwUvRqCQzbIAVtqvNL4kW6o2LK3r63TqBlk8RdcV0lk/Q68xEMVCuTYdHFW3jw4MHwKalNNzPe47Zzdpfp7e7HypzPBSxsB5Tix06XYDWUuev945w/rCLIRWLlYMqoB1Alo5zBdJ2ZQiAJJSkcttpV7Ldb+frfBkwJDSrMaXHHVHyy7WGcjB+zh0/Vs5//t/hLB7H1aF4nBikzbQHkOrTFJBoiEbuTBjxGGWCUSSNqSZGUb1CILDuhWGkDwQWnJrqIoMtDjUddBzzmHpKZdQXXoR1puhv9bgcibtXOD0rvnCQVArUXRycfSy0lk8zelf+138F29ndu9e5GEkvr9m4yu0EsxCcpBpcFIR1DGTKcQ/A8tFozKRMKtRIlky0mQ6ySFaeD9eHM4b1pGSilFrOx+WKKOTimG3y8ldO7iSHvgOS3sXWDh3L/sk0GkrYpIHq6ryec7hc8IGoxKyqDzaNEhWOmqoy6hs4G/9B47/379IrM5h3w9/L73LLwDXefKjL2cRtqUzICI458g5c+TIEb7t276NPXv2UFUVg8GAO+64g6WlJXbv3o1z2zb48QDUFN2ZoGCqaFBkMODEO97HxuX7Of+lr0XiiOwNdYLgCofAXAklaiZkTyd1yC0fwEnGWywdxkxwMeMMfB1LJ8I2qJIp+uaGYXN9nv7P/zmy9zys6lDtPYdL3vh6rBbc+efijh/i2H/4RXyjiK9wy8v4xUWqYGhSKO14oC3TkhK1ffIgrcBP6/goQogNUo+QuR5GSW94FerBkE7OyIlTdLp9ZF2KYWulCbOUHbpVHuvN4k4DCrnfY/4VL+IZP/YT3D13PlEE7z0DrZEM6gJP+z/+FcsHv4Bpybue99JvYs8znoIFd39uvQpYpwLvW5lZSCmVpkWxDZm3rZUneBIdLaONnrS97ZwCLpGlYnjPYY7+9tvZ8+JXMdedJ8902m6FMBa28JSWuS4b2hjULROm20VSLJK5zpCdc+zctYvF1Y2WmJqJTorioFEIhG0paCOefdf9Y8556uWw0MWkx4nffSdNcIRvfjEv+N9/ApmdZ70b2BuEXtPA2jrBC9xzL6c/8in2bizSefk/KsbtSYRJK59gJc1VNUNEEuSA+OLGq4Hmot/gSUg9oKproleMhqBC4RVKeY/P0HF487hWq8IBJh7Jnp3XPp/n7tuLc/OICNf+2A8Rdu3DpINTAd/2QOh024pnoY/A2qCUwnakdJKkzIfkIXmlf+hO9E2/xbHubna85iV0L9qH63ae1PE927BtnQERodPp8A3f8A0cOHCAyy67DBHh1KlT7N+/n0svvXTy2nEkIcZISokQAlVVTX6nqnQ6ncnnjpFzxjk3KVH6ao7Xvpp6ZLu/TNC3xnNdlNkqcm5WTo4aGNWoJhpVjGKknLXcdC9Ey3RMGBebmeVCDnRtNboKIZZdm6szIRT1PC2bOoSiW0C3S/epl5N7u4g4ep0u3ec8jyQVIy/M7fA03R6uGqJdT3awEDM1StdTGMPoJCJgkw6Hj218x9fmsV6jkpbW0nTZKsCwO+/mrj//IBf9i++F+Z2l5t8MayI+Z2R5jdDrk5zDcivukwsPos6ZwY4+w/5Oekc8HXOY61Jd/TR6z3o2dt+wlLF5h9YJKK2MwzOvJu/dgyTBnMeft5dqz07wno5pSUE4R+pUiAt01CYtdmnV+AKC6KZCzcfJEXisc7cw2ksayKTlsTioe+B0yEWDIet/8C46r46ElzwPSXlTfbkirhURMhhIRqtI7ho7+h1GjSJi+JyhO0N3935s/S5wHpeLMqFaSZX5yoMrP2cEt3sXnd3PQn2C5GCmKt339p3Lzmdew+joEnUo986OE0vkL96OGvRuuYX9gwZvDRsdV0J1jwMe69wt93FLFI6Z49d/kDw3z/kvuY6mI4TivqO5KI72UsPaX34M+avP0s8ZXNM2xypVR3VXaKrS+VGsCIfZOC5oIBJwu3fS2fN0JFYgRu/qp1C7XqlIUAPLE/XC0sxQqGLi5Mf/Dhuu42YXkI0B4FARnCpBSpSgE4esVg1iioTwVU1fM/uq1u7Nc/6rWru3EbalMwBMIgM33XQTR48e5XnPe94kdfDBD36QH/uxH3vA60ejEb/5m7/JiRMnCCHwEz/xE6ytrfE//+f/xMx41atexUte8hJCKEOiqjjnSCl9VSREMyOlNHn/eIJ+pRNMysEg3qFWQvbqHCMRKg9uJuByKkYhJ9QMybltEVrYuVqFErJ1kWRD/GCDEBuyJWpR5hGcGS4mLCtOjVAV0pZK4Ss4L8WzD57c6dJUVSsOk4E+eI8TAzqIVuDGufDS5MQu2EU6ukTKmRxjER4KFU4Ntbbc6zFg7OhB2SV77x/VzVsY/oongYaSHvjsjdz1trdz4ev+KWEWcGUXYymSYw2rG8jOeUaLDueF5LR0Z7Syyw9f/wz6L34Ng//zdkwjueohvTm008V0iENJORKsaNmJQRM6qO+gowiWqSVQdfpte2gD9cSZPoN+DwsdSEbHC9YkaHvPa0xFfdDunzuFoP/Yd7DjubrZMf7KUeaVUWxu5SAhDMSoOka3GbH8x+9hzs/TefEzWxKckMfdAbOAdnFWKglSHjEMxuzcLLERXG2IQt2ZJS/sZdQ5AeJLSN95RiaoF3yvg3gjo3gcdCoaPA1GX43QZFwjsPcC6Mwh+TT9Wukko7+yTnPr7QTzVBsriEDynqbjwJc21I917o7Hcry+PLq5WwycSls1MGg49Xvvwa5+Cud/06tYmwnMWZFWRh0uZqo4ZO2Tn2L2c7ciOzqQNsihxP3EhGHP2PCK+R5ZKzT4ifaFN2NAJjqoxTMrgnPG0Hki0KMQEckNZNCqU+6bCoIWZ2B3biBAGNSIGlk8lYbSnIuid5CdYc6RcZP05WPBmC8mIpMN4KNdwzdfn7MB29YZyDnjvWdtbY1erzfZHfb7fU6fPv2A14oIIQS+67u+i9nZWX7lV36FgwcPcuONN/KSl7yEZz3rWbz+9a/nBS94ATMzM5gZo9GI1dVVVLWEYh+ld/ngHWvTNHQ6nYnx+oo/zwyfExpKsNVZ6WMfCmkf7XdQKe1xta7RbFg0dNS0lQSGCz3cIKGdAcEN4LY7MeeLZDGu5R0DdcKcoK0gUNZMpJARS7a35KVzLP3KHUpDg4rHcjFaNlL6KeCkix86FjSQfBe76DzC0dN4c1jMaMpEiVTZMHWPeQswJovGGCfj/qjej8OkQbSGXBXS1eJxzh2tw/oqTdyH823+NEZEM2wM0JkutYe5DEEVq2usSWxsjJi57GLS1Zdy18IC56aarnmC79BVxeVITCNEFCygOUHMeAE1oSuCxiGmHWozOloY9k6N0WyX5X5pDuU7PTQ2RcVwVKN1g6WMZiWnVNJDrk0vPEaMHYGUyvx4cOTsK0Fp+6sMKkdHSySj8hWWlZAi59qQcPQIqkOwTDbDq2FNg8sB9XOMWKe/NqL+/O30GyVUHVJU5kfG0AfWQwe3azdr5+wpe2Gh3AOprbUPnqxNccBScf5qr3j15JioYoPzjvVOn4XskY2aWW2FIGkIwxGqAXXj8/HguuSY0fTYywtzzhNj9Vg2HIpDSMVpiiP2Dkesb6yhdV32BlY2CaqlNNATmdlYo6cNmj1y5z1UGku1ixNSyzfKUnFipssOb/RyxpvhmoZaMjmmoi9gis91WQfUFy0Rl5Dc4JMBnqQJ0USVMzuGRkcyG2Tk0H3lnvGJKB0q64CMyAGCF9zaBlFzO/6PfWzNjKZpgEe/LogIMcbiCPknjyj6tcS2dQbGXICnPvWp/Jf/8l/odrvMzc3x/ve/n+c973kP+fo9e/Zw9913s7KywsUXX8yHPvQhrr32WhYWFgghUNc1MzMzHDlyhD/+4z/m9OnTNE3D3Xff/agm02YnwMw4derUhOcwDl99pTe+YHQ0shb6GBWV1XTVaDQwX6+QoxJ1g6W7DhKXl5GsnLr7MF49cbSOOcOrR+5dRBvB1yvc/p9+HnvxS5j364hSiFVWDJpUHhVYXlwkrDQTfXv1AIm0ssyxO++hDjtRF8nVOkkCLvfpJVhYvoc1HTKzb57VE/di4jBXcrY9PDlDffwER+68k7VuRQ+P6lcXyz5x4sRkfMe7ga8USoXIkK4OSbqbQYBzjt1DHJ5m7eR93BcqXE70FTqnTiKm5PU19Jy91M7YmxydtRHrh+5h0TlkaRXrGUgivO5VxHvvJfz9bayvbbBy6CBrKw2HDq/SqQfUgw7HDh9ivdpBnxVGgyFeHEv33M2p5QWidgkeJEcqn2m6jj1ffw1x6RSo0RNhcGqJ4wfvQg4fwXLmxL1HiAfvAu2SXC6x+scIEeH48eOTKNmjhYng1QhZOdXv08mJuVFNlA4z9y0ylyPODVg5fZTVowfpHy5GwGJk49A9rKvSf8U3kk4dY/DZ2wnHl5iPMOgJ6fKrGR1ZYl2VmYvOp7nsEnww7j55HGtWITr6y6vMZFgfDVg9coiVlR4zwxmSV5pKcbkL1jC3cRq/e4aNfTu4/chhzj12hJqEBSMqVMMhQR0bldE3kOxplmsW7z7MyszqYx7ftbV1nBNWVlYeU9QlSaCyEZ3YwTfLRF2nScssHbyNpaV51q0hLK0jKujaGhpHMHREa7ChEn//T6gGg7JOZYXhkG4WlpdP03vtS1jveY7edRfdnMijISdUkGYZyKSwg05eJYng4k6cGzLqjlg4eAQbRobHFjl4153sPnEUGY3obigWSpqtPnSYeSc0XhmFCE2XgCegzAxHpAO3cviyc/HSf8xjq6qcOnVqMncf7bowTi9vbGxMIwNbHd57cs5cfPHF/MiP/Ajvec97GA6HPPe5z+W66677kteLCIcOHeK3fuu3+Bf/4l+wa9cuduzYwfHjx9m/fz+qOkkRXHTRRfzUT/0Ux44d4w//8A+54oorHvViuNkZMDOe8pSnTD5/fDxf+YdlsrjC+AW8ZogemgGnd86zVq+x9+orOPWx3QwR9l5+MdXFF3Oi2y3EwF6H2coz3DGLayK7Fk9y2jv8rj2tMmDGSaDjKsK5e1hPI8659Ono6jKrzuE8DAWydOjvvZQ9V15TmL4Yo+4GUNG1bimjW+lxYu8MC89/JnPPuIKTXUc/eujtx/lb8S7T3buLC6+4itztYmYECRNuAnwpn/CRRj63jO4rrrhisnN9NGNrVnZ/WEbp00hmbWGGqtdl4bJLmbvoMkBwyVid31He4JR83j5ifweWTjMHzO/bx/xTLuH4jnn83CznPO+F7H/m12N3387hH/xxdu27mB1XXEHtbuHSp1zGXb1zSCTOv/oq6MxAHHJodo4ogb1XXsbuhd2YOVRK3CagVBdfCt90Laff8ttEKdyAam6W/VdfTTy1xpCK/edfQPWUqzDxhUA3Kdco4zgWf5rUpj1C/eF4Dl988cVUVfWoxzYB0ZRuypwfKrJAJxUi6/rxwwy6XbwafmaGSy+7huAWOVIJVdcxd8l5DBcW6P3vP8KsKEd+8P/DzD1HEGB0zjznfMe3s/Z772G0oVz0ipfR/0cvpn/vEeb2n093xwzBKk7sKQ7bzDk72XnZlZyz0KFKs4ChVSldrDEG5+2nvvgiLr/uFeR9+7F6RN2fZSCerjcsriMkTBzZFEfDzoU9nHP50zhvx9zDnr9iYCXSVpPoWiC0aSlFOHXiJE5gz5495fo8ijXGgAhUFhGt0HqVIztmGS7MsveaK9k1u4CThN13D/c4hd0LWAh0UhEWTRj9U6fJ8z3yxioheNzsLkJvg/3PfQ4XfMPXw8wCTZghSGS4WuNXRlxx0S6EhtrN4S3i1ZHxeEkl3RZnGfnEzAX7ufiKqxAySzPzhOP3YQs76S2cx4zppNLFW0YkFp5AVnwwZirhmvPPwao56v4OumYICaSwIJzRztuHR13XVFXF5ZdfDjy2qFbOmdnZ2akzsNUxDrfXdc1wOOQHf/AH2b9/P/ClN5WIUNc1//7f/3v27dvHxz72MUIIvOQlL+E3fuM3+Ou//muuvfZaZmdnJyGh8U5z879fKR6cBpCWPb45YvCVfl4J9Hq8KSZSRIBcRkJRCLM8pBuHYAmfoKOCBS2d7CjCQVYpw4//Jb3FC6iyEXPCrEFzaG8yxUUhnTxNvnBv0W13C4xsFXXGsN8n1yNicNCZRUOpYZcEQhejrTxwBuIxUfLcTnx2VFVgrROZm7uYGiP6RNfVIK6EaT0tQ3FsuMpmtq2Kan/x8OHCzdGWSZOaR7MDaMmTRgUOuhZZy/9/9v473rerrvPHn++11t77U069veSmJyQEEpqIhaI49saMOsogDg6Io2N3nK8w6gzYBhllsM3YQdBRfghioXcCCaSRRpKbntvvPfXT9t6rvH9/rH3ODdgGx8AEWY9H7s0553PPZ+/1WXutd3mViA0FEoRSA404vAixjfR8C2FGmO+xumMXB31NsXKGB1/zWs77xZ8mpRqni6B9/FIf2TkmlIpIBakgmIRNJXLwcggjwCG2QdViUwfgshAl2yYbyYeKRdGqh7BECNmi2KSUBWQM0ObfoTEiYlGr8PA1+En3TBYq6sB9/9B8GWO2n6lPa26124SNUASlKZRoAwNjsi6+LZEUkGQo4gBSx3c3iWQaoinQuV3Q64OU9LXBGIfYAttfBBFmxqHVkNQvGM3Ps+AMrqOH2j27mSzPsVBaoKBzlaaMhkjCSE1PSyahR+gtIs5SxkBbDaj3HiBsjOivr0Azw5rIoC2zUZWJJE35s8uT8jfuXcl4AlUBjYhMQeZyi4FEwmYAHfqPmlug02TI92TU0FOHjxacIbgOGBhHpDDDp4aYDINJZg/gBdWGaAa4LiY0WtCWJbFXYQa7wFgKE7v1UtDTJosGkR/1SIFRmDhhHoONBlIBeJLNzpm4HpvBMrdximLPTpxZwMSGZMBFqBKgvlMcNARNxJWTPPR7v8fwnAvp/at/g7YJU8bOqFu7gFb+j/aFh++1n3aL6x+RXDyax6M2GNj6oG+44QZuuukm3vve9/JjP/Zjf+vBvdWbe8UrXrHdB9q5cydzc3O85CUvYTabsXfv3m2Qyf+bdMRPtvPZPihVsyyqagcey4Y6Z4dgXUVx9DQy28DGBGIoQ7YTlqQYk1HJoZ3hrVCIggMjJX5xP/WXPZ3iTX+K7Byw6+ufjboapAWjlGkuMxK2+ntJmMbE4nABwTCzsPBvv53++U9i9PY/xEuR1d86hLfthMgNWd43XzEdT/7s148UPU63frfKFu4djSlX1ztkvuummaCEkGgU5IJzueobv5H2p/4z9rb7OPnxWzi3rXFNwC5m+p9TQdMyBfOIC6jrkXQ3QQZc/IMvyuCzskBliopDZMsRxp299ySUZpbZHDpEUoEpsj1sitKxPBJBI1EjaNgGtBkUEwVRc3ZSTQahbum/P5LbnNVsOrM1gcO2RrUlOoc1FldnB00kA9g0GiRm2WtJuX9tOkqlsYapg/5wHmN70BkSESxqBkCV30gLIFPSdn35lzH6+m+iLucIVYXYhNMI0Wa9IJO6A1XyaSgWX5S4Azt5wi/9V2a/+Cp469szMBeDM5aGSGsgbqmA/X1DMw/HNZ5ivAFLfWJhsSQsNlMp/9Fw+a3NWwhApYKj2Bb8cXQdoklLAWhIuKTYps0KH9oFa9bmdWcsSQyurHIKIbZj/0bQEpggdgN0N6IVrms/qdFOQluINmOrohbZa0MEoUTKPoRELMCUgppc7So+BdIiCoMIdmXK/e//ELsvO8llX/fcvJ8nJdluF5SzlMfPj3+68aidz60+0MGDB4kxcuGFF/6NDPHhrxUR9uzZw7nnnsuhQ4cYDAaklNi5cyfnnHPONtXw/81A4O8eSVNHL1OIMWMAPqmqJaRqgGsSZZMwUdH1NWxdY2ctBkOK0EhCHdTDKlcA5i12boEzi3uRp30hJ/cc4PShQ5hd52CkJEqklcm2idE2ct056sVlfNVDXUFbGPxll2GWd2LdgEoGGG+JZKnZrJmar/ThBWul26wewQrd3/arRcnzqGRpQj17bYRIoYIPCjt3Ul55Bc3CIlYNw1kDvsHUDaYo0cJkRLzxSCFQeGo3Q6XOB9GhPchFuwhua0dMWdFOS7IqoQARiYrg88wogCUO5tmQ3K5IoYUUiclnwFiHRZFO3F22fv1WBKAdCES3fBQfyXBAUZMIIogPrL35rZz4gzdghdyekoR2DotZRL9DvicQrxnYpgpOSJVhdVAw3buLWA2z+FJqkBDBVYh01bOtT0sUWe4zuvByJjv2EgpBRDFkq1zRLbvkTK0VhCSWEcK0P6C46nJWz9nHqDckisEf3MfI5nsiZeW/fygYyL4BifDQUR589e9SP3SUAFnx71OC+093bAfMYvLvRMC4rDpqZPvZmdz/EMxmSBsogmKaNge4qdvnrMsWxtaSjKGoSra8QgQyMwhB7RSVkMuBmlkcJiliWkoyeDcgoKFzc8zVNAYDZoMhNmb3TykFI5kibR4+fSbvZYUqdtqwa+pZHM8woVu8xnRaoJJf/M+jcv8ZHY/qyoBzjv379/PCF76Qsiy3D/KtPvLDS/7Oub/x/a1e0MPBOzHGT+rt/z87up0kxQQxZbGZpJlGth0NZH636fewzqGxe7BCQ9G2mKbO1EMRvM1cdjfYQxrVaCmUT3osl7/6FyjOOcD8Ey6HpRKzaymH5eJIYokKRqRz2FHScI6n/egPY5d3wvqEYE02khnMY+d24nbuQdp8yGZwYspZAh2IyZhsoyrghG3ho0dqdNLrebaUPHedAU4+WLutURWbIoWPaDRQDGC4RBwsIFGpYgv1jDSbkXqOmVVKVYybQhGRItCoYpLDtEpblEQbsWowOJJGjCGXkbVDL0tApUTV0Ugfp4qzwq4vfwb9Z3wpk3e/J6tRxkCMgRTD2apQd0/Jdqp+Wyui67VuHSbbXzwSQ5TY0QldCBz/s7dgNqfs++4XkKzBSLYj1q1IsqvEpBBh0uZ7EwFraAvD4PJLcY9/Es16wvQKTJjgtEFLC0REpyQWiQqWBIVn33d8G9qEXKbHoeKgaDE2p5epa71ZBBOFUhxBLKE/zznf90JoIs0f/wnhK5/N7O3vZnjkOEYNI43/oM5AxsIkRkcf5MHffR1zz3wm/QvP78rsypa25z9mbFW0FOhMCElFbn+opmxdHiPjoycxrc+CYilB3W4bmyURMBYlV2GwFnGuu6aUAzZ1GfeUJaDyA2lCbq0JKAFLCzqkoMDEKTBGfJmDlWHFOd/89ci73kML2e0wCS6Zs5gqgWgSiNBKxFrFaaC0EbWR5ITQtRFdlkPkUZazPSrGo+DU+9vHFrXwT/7kT6iqiiuuuILhcMiJEye45ZZbeOpTn8pVV121jQp1zv29Wf9WNWHrNY8W0EiMMcuihizks13i7lrGCUHn+rRAr4PtJCLSzJDW4SVlKmGnF9Auncfq6hrLgyHNwoDyCx6PxIq0/xDRKJaERDA4jjwEKU248KIhxuQNzpuC6pKLwRS04/uzImJVoKXDzw3YPLiAKxVJLbVxGJcDMZcMRnPWFMTkeEMha788ctnr1qa6fTAmRZq2q7SkLqnO5EpJHus9ZhZAephiDrewA4iUYYrefBty6iSx52gkWwY4HSJmSErw8ZsSD97guHyvwSzCfXdFnPWcf2GJSEBNQlP85PsVmDV9PnzrhC+4fMD8QDAH92Euvoz0gQ9QquZgoGkoRPK/l7OVmiAhA9mSYsVudblz5eCRnVqEXI5WyTzYMm3Qb9vu8DfZQjd1Jfqt7C+2lMmiN92KHY/AFuAcTeno79/LxqH9bExX2OMc5sxJSmuRYQkSKE0WKooBxASM9XBoD1ENlTakkKtexs4wUTFqSEZIaAawJehrxmtYUyKXXkR9ybls9vqwZyd+zzJy5BhWbBbZkb87GJBuYo0oQs2OekrZ+hwz/1M4SEm2bzAxUzWDRIIJmBiRkPAxUUnCTFo0RVwIeZ/wfht7pwgURVdRsKgzYF0ODFRBIkmUydTy8esN+y9UQiowUoMpUUwH5kuo5mBKtWVzYFg0PQQhzg/gkguYFlmh0LqKJH3GJlKaSJGy4ZPXvFi8U/q0pCLRuoiWineGILmlaFW3q1z/TFr5n7HxqIyvtmh5bdvy9V//9ezZs4d3vetd/PEf/zG33norz3jGM7j88su3I8/PZZ5o9hSIWZgodhngtlhGVxUe9ME6bIwZUS6KndWYyQxD3rB7Kvh9e+l//dcQv/bZyNw8DouaOdSUbFrL1FiQFlVDjPD7//PD/MqrbyOEs3tbwpDKfhbGSQab8iaPM/i+ZWOnxTcT3Kimii2RFotifMz68zEr2p+1t31kg7KtCvp2ppz0rM5/zP4JkAF3mjxGA7atc6WlKIi9HmISbv0Mx3/+v9Nc+zHE5kBARCEOSKFA+vP8xqse4DdfNeLMSUUi/NzPfJxf+vnrSNHik2BcQrekmgHIlM87bl3he190NffdMyKJJzohMYdIhbQBfCLNmqy81xnVaNcKsCFQhpZKU2ahZDf6fD//BGfSPzS5JkoWxSkSbTUlmSbPd7SYoNg2bWsDkDyFCciD93H6V36V4doaxhXgSpKtSMU87mlPZf7ZT0dMSVwf41LCliXgkAg+KDEKmhyqBa1xRGOgHRBbS6tCTZ9Ifws/SRTFVkVWNNTszScIoahIc3NsLM0zuPgyEAcCjUiuxMW/p2zVZd+igloPpkFixGnWtgD5v2yB5cqVzDzDVjEmYSRSrk+QSYszQiLBaIJaRWK2M1RVtky1jbGIK4go4jKg0fV6dGEcUJCM56EjDT/+gzeyMZqj9p1jJAavJstp6xyNFBAV2b+fHd/xbxicdxCDEjGU5x3CPeEKkuuRXJ/xjl3oN3wVzfIC0TqSZpSHdJodNC0S49nOEbkVWZITDiTl9ft/M32fH39jPCorA1uHvDGGpaUlnvGMZ/D0pz/9k36+leE/GjAAD1/U8vCvP6X3f/ZPts9J7Q4vjYrEdDYY0HzMqYDt93HG4cIUJR9UdlpjjcMktq2Kw8XnM/flT2fpS87DzS8iHtRlG+RFNrFhkVBXHL6n5sJLBkzrBVZWF/Pv0HzAGQcRm7PiAEOf8oaflFQKbljiHjpF/PDtmHPmGT7uPEQcfnPG+NQqy+cfoOhMawywrWP7CA39lC+25pMu4yZtUUQhpRY1iWQCOMXbBIVBUqA/3qC8+z6a6QxB6CUwEhGdkYoWL5bpaMhGFGpRgigrG3NsjjYJHopCclRmwtneiFqESL3u2Di+jzhxCE1X2p6nlB6sbJDWN/J1+vDJB0xSTJvYfOhB+gvzlDt3ghjU0KHZdbtS8EiMJGTmyXYVIgc6GRxqiChl1G04gxAorFJOxwyDp9m1IwcDhcNKAVqx90u+GE0F8uFbcAL93hzW9jKzJhRsrgfq6RqXXLRErQOEgiooRx70pCoyf8hw710bXHbuAsWgO/RTxBaAiahoR+FNWC0Q02O8vMjgvEtpZvna1ysImiB0boFbq2i77SLbX4tCLCKty208ieCd/RtunVuyv39jN/gbm8PZlo9JkdEn7mHu/Esph4rVSHXsNHpsBbtziaQJOxpTDPvE1D2DKEFy8yt2QkeuqwYkEaQqzyYTCImCew/XnDx+kI9+qM8F54Gzhs0N5djxlidcKqxtFtx9uuUpFzrSjj2c98IfxhS9jM2IBvbvxT3jSUyuvZ5hMUTPv5DFn/gejv3AnZi1CT0VbGc57axBHzzOwqjFBnAxs1FEBBPzdScTCFJQPKJ4l39+4//9k/JvGSJCURQURbFNe9oKDrYofP9YqtlnenTQsQxF2jrEVR+2weRHMnal32wD3AF8kuK8YH0NmvtuKikrkilkJnKJ6c+j1qCatjFktpmh7QzvJIOQFMq5Ptp3zMrlDGaTlDEFNlFFsF64+66G7/8P76WeRtooYHpE6eg+aim1Cy4S0JVeBTB792K//hsZ2jnMn7+NIy9+EXf9wq9gUoEkWH33O7n+v/98xn6RMQTbh8gjOPlWt4xYOrtWDdB6XFCIGdwmpGwP3GYfi5Bydi0iiDVghEETKEdjTCox1lGIz0FE4YmxRgvJB6BRzIB88GBIAUrN72tFczYnEyQmOv9YjC1w0WZTHrUY6WG/8LG0z3gqpw/fzfSGm4htg2pEQxe4CPnQn3k+8jM/w21/+kYSAZVIwhDIXgqPdGkgEZkYJcWKKswhxm8ffNqtc7UWlSxJq21mIJjYIsbkNoE6rGaqWpQ5fK8HkhUDyy/4IszCPEhCbMXb33Y/P/VTNzAZKSIFJkJs4Jd//Rb+4HW3MV5r+b4XvZFbbmtQAkah+sInUDznK2Fu0LVYMjhQkkF9n8L20BSy3LMKNkZSavJcqiepx2tAO0Bh0lx/SZLbXhIyOFmtQYxkDwnJdNwgiUQ+pFWVoIlWR/gY8wGuEYJSo7Rbe4NGVD26doobX/bz1KfvJUpLESLNdR/j/r94CxI9zjcQFOnvQFLMGIXtaDFmJkoVEVGayZhgDW05yOZHnSR0ioZ3vfMoK2vzvOoVR7n3nhqL5S1/cj8/9iPvIwbHLdeN+U8/9kFaE2nU4Adz2VBLEkY8xpbMlpYzjbhINMMe7F5ms5qnPbA/Bx0KVCXRR8K111I+eAS1FvDYmB1YE4omBeEzsHb/+Y1HbTCwFQRsBQKnTp3i1ltvZTQaMe1UtR4tHFHd+mMLwIZ+8kJXT0t+Pkm5JdAKJPW4Fgad/GwA1ORgQDSXDVULKOa23dyg4423U8RPUYWaAisOkwLOthgGBAOUeaOKAGmBWMDaxHF6bT/aSrZ4FUMrCQgZlaxkhLiBaCNTIwQH1eUXsO8//BCFnWd45jSHTh9m8eQxgpQkDPNnjnPo5L0dUDgrJAjyiK5QIW8qW8zlIFl2WdqGIim0LdFmQydpEzIL2aMgCVYLnBpCknyYNSlTPOkjlSWZBqQA24eiIEgGesbgaD04bXOJNaWsJ0AklQNqNwANpFbZ2IiEKLROMaZkszFsjvv4WNL/mqdTPO9bM4nuoSPEEMEmUuxAtJJd+xDYf9cnmHvoKEmEtC0ypBgSj+SOajRhNFIDUStsnAfjs55+VIqQcET8ZAb1FGY1Ntosa40iFAgdoFJb1IDRCoNFnTIZLrH0r74duzwAaQjaMJ3u4OjRg/iQbamdyb4Ix1cXGY/n0VZYP3MBDzzoULI52e6v+mp2Pv/fQX++M1cirz81aFky05bgGtaaiEqPxRAxbUSCRVqg1cyAOBvVZ/HuDrxj2gKbLMmaTMfl7JncSupi+0zPDb7Gjk/gfMyQhFz+Y7YlGQ4ZLKAeOXOG8x96CLP+UC77RxjOJqTNE8hsDKNNvA/YcgkbfaaakiuDRiJRE9G2WUgpBqJ1tL1hBld21yhRmEwMPg4ZT/cwneXeY2x2sHLmACGWHDtasHnyIJtjpScJIw2SLNFags0gxML3sDESmaCFgJujtXOYyy7NdtPGoHv3EAvDoK3pNdMsWTzdAFWimjyfkpUGrMrn6wL/xONRGQx86njwwQd59atfze///u9z4sQJ/viP//izfUmf1rDa6fWkTK2SZLYBSJlhJiSgFXJanyxTCUTb0rRTijZ7khfBZMpQynRDTSnLCFcFjogJHpegKCqMesrZjEErhOEcU2tJ/X6uHrAVoOjDxH8MYmBWQwwFdQ2+dUiylJqR2WogGs0mxZIrCqGzoRNrkX5FqhzRZppX7v1tUZgU87Cqx9nxCLcJJAcf2u3OpvWYpu5cDMM2BS2X4TPFT1URk6svxaFzmRQ9ooPgAlEaKHMAZVTQwjHat5tYzWM0Sz2TsoWrOkPtuiBOevjzr2K0/zKS28G112zwbd/+bu59aMrHbpqyMnL84i9t8kM/fA/3PDRFdQ5TLtJLgZXrb8BOxxj1JO1EpLoir9pIFROlN5hUYVKBCeBQgsTt+35ERlfNmlMoQgdy1a2+b0RTwgZlrvZc/3uvZ+32uzEdENap4oxmpso2imTrWhWS0vpEmlumIwlhLVlrwVb4lHER+USDrOFoMU6op33++DV3M9nsjKKkxMgiGreK953WgwSMHaNliwyGjC65gvXFHR3+pkRTn9iZchl1OdCSfH8FqevBduvqkyjP+ZqilW29BxtBomLOrHPHL/4m/t4T2JCrB9E09EkUQQlKbqRPGx78jd/BnTqGrG5gU0GMFmsEDWMeeN+7+cQfvBY3GeOsor7JM6hbXSiDqkOTIarpAq4CeoN8HwbQHD74NmNnfIw0EmkE+st92uCYjmFlbZ0H7rP8yWtWURViMqhAi6WmBylRhCZXR+pJBgVLD0RIgx7ewKSAZnmA14hqpKcJd/X1nH7dmyBGbFIKyZeZtsqHnx//pONzIhg4fPgwz3zmM1lYWGAwGHD69OnP9iV9WmOrZ5pMdzjpFlB5W7QzHyyAYiEqAw240TplCpmrbzJ1yCUDbZ1LiVHxNhLmS5JLaIzURpjN9Yl4TDvBW0N81pdin/oUamszqljObrxGc5aUHz5hPMkmKHUDdW1pZ5oR4QixA2Nl1ACIRkyKSOp4wZpQCzHlcjuati1rVbsS4MODgUf6ge/uSbrQy5CQusWOxpQakPE4WxZDBmu1bbehdjgCgV3/8utxl16OUcGLQhmxCEVnHpgW5rn8JS9h6covwc8cTehTN4Bm4FajmpE71nLg+S/ii370x8CV3HmH59prh5w44bnmmoBnF+98p+VP3xA4eiZmvwcsThv6x45j1jcg+dzmEO3mH5BEkXSrC56R/dC5RT6yvM3UlcN7sykyneZaT5d5q4SOMaIU4xHrH7wWf+9DJA14lwkGxpKDLunWB9uYP0BJHTjVWMmqiynXE3xbUDfdIbdlvZ3RjDQholJx/XWbzMY529aoZ0WztrQDRAk2UoSWhUaodhziCa/8Rcyznk4IEZc80owyJoRAbGYENK9/VUzsStnd9f5t+ifedpRGyNm+KM2pk0ze8i786dM5UPABO8uZshCJCiRDe3qV2XveTzVZJ41mSLAkn0WarJ+QPnYd6cPXUozWKHqGMrQEVZKzGGM7B1SDBLL4FbkfT69ANKCdEIDm7hgYxUfDeCZsTpTNNjCeCqfPBKZNy3g04APvCjStofEVSQIF0FOAGdPejKlLtLOGlCykPsEmosuU4toqaW54lu0SE8U99zK79kYwHkymYm53QB/hJOGf4/icCAYuv/xyPvKRj3Dbbbfxm7/5mzzpSU/6bF/SpzEyaqDVwEyUxihquqM45QdUraUU6NHhH1xJuO9BHvrl38Q++FAXRDQgmWt+5qYbMnUw5c0wVS7zzQ3MrKXpzZEkIb7GG8f0wD6apXlSWW7z0LfGwyFNCZjOsqypb6FtC+q6K3Maumw4UaSE84rzhl6w2CioV1QNhXGkOmMcXFJMSl05MmXqU0qf8Wc8gx8VGzxn3v4eRjfdiow2WHnfh5A2CxBpSgQfEDp2uM2AK849SPOYy1AtsrxqhCI6wBEEWmOxF1/BmUnFvffOOHXScvttEzQVudxJ2eErgEP7KA4uk0RBK7zfxx03eq6/ukFjibBMCgt4nz8jayOlTbjD92DvvZdCYqa8PUxrwCTFIsRCiTbRSMI7ICVcB+Z7JIeGwO1v+FNWP/wBSBFRkzedFFGTD0/TzlhYW6doW1oHjeu0EExXgaGTleVsMJBCxFoQ6xGTEByaSlQrjh51/MLL7mBjrenuTxBjUTWIMQRtibFP64WoiSQzsC3gc/deFSVjG+LOg6wu74FykeKiC2iXFjEOBsce4MzP/DTHX/ZywtUf4bZf/V+40Ubuw4shGUP8FDzgpwYDolmFb7teYBPOKottg7gGX4BK5P7feC1r7/0QSZq8+lTxK2ssjkfYUGfRMXVIyOyd8qabWXzL29h1ZgPnW6QymDaQRGjnByTr0LJACLkUL4Eseh1IhSIaEGnA1oQIPpQkqRmNDX/wm6t8/4uOcPOtSl3DxtoE31bAkBR38frX3Mlv/8ZhIjOsNjg/Q8Rn0RDjqHftZG33HqAkVFnXoIfDBMFqSRWyaFKyUGpkEGY01ATT4UuQDL78PGbgn3w8KtkEnzqapuFrvuZreNaznsXOnTu58sorP9uX9H88RIFmSmGUUFQ0FIhAKflnqkIj+cEwqmCyVG1z/xEmf/gGdp06xeySc1A/IfkRqGftxps48DXPRpJQJChTn5QgqtArl4i6QPQtUjd5E52fp1mtiGIxYjHqzz5n3Wa69f9NEwjJUNc5A2vaDii1Ve7MhQHEC9oq0ZQ51zIZ0daXApnUnWkOGQhhFG19VpMLZ7PVrWt4RIMDBULOUEmB+ta7WVgfU8SG1bvuQRuP9AekpMQYukw1AwdVhFDNsfjcb2ftnX/FzpmiMoehx8QWlJozP+/6nNxc4cixCYF9HD/eaQFEQ+kzd1pF0R5ZJ8BbJlOHYcAdN3tOHu3BNlHL42tyg7gMBJPor2ziHziS+9Yhb+yZj5XLTSEE1LXYNMWkkloqCk2YZB5hTIYgs5ZT730Py+eel4WA1G6D4OgEHjXWVN5hQ01TWKJzJMm2wtLhHrYQ5XTPRfIRNKKmzagWLRAqQoicOVnwljef4j/+1DnsWSy6heTwDbRtV28Tx/pI2I3FmoLECNECTUPUKD4ZSgrcs57FVY+7iHbnIj0RRB02WfonjzN5/WvR3XsIS8uM3/x25Du+CRaGqFiargpiyOvlb3MpLZPmx6K7vyiCFAaxSqTGG2VOAqM3v43mgYuZ/7KnYYsKSNjRlCJkdkaKmdsrMeJCZOHjd2BbQ3nF44ihhbk+1BFVS1icx83WoCqx0ZNQpibSd6CFxVcFJINGixpLE6Fpy+zHEOf4qzcVOON54hcEQnA0taeuLUkjg3l49ztOcPrMkBf/QAlGMA5sM2S4sUirQ/a98N8SWkUrQ+MMakuiCuIqtBpgsMQUSRasgRCabWG0rdbLIw4s/mc6PicqA2tra9x111088YlP5Pzzz2c2m322L+n/fKgSHjhKe+snGISGkvhwxVg6wS1ymSCwpbnhQsuO2YRe8pTBox++DjNap4yJfQ+eQDfWQTrsgZb5l/SEdv9BmnIBe2aMX9lkPBgQ5+eg6iNFH4J2oLqHh91dYVYgxkRKQtOC94667jDU3TVvUYCSQBgMONGvaAeOVOaDxyJIyNUENdqp0kBofRYUSZ8i5PJI4gXYQt1vlRwjvbqmFzxOE66poaNrquZ7p3CoCEjn/2cK5IJzmO5cIgzmOTIY0u7YwxSLJnAx4SKkJASjmBiJJjMXipSoQp6zKJIV2pJhsiHceHtDCMq11wyJMpcZDSSsOj7+scS4TqRBxVrpMFJgvOas228V03PPXNWBCDY1yAMP0V57HWXshH+keEQnWJLCiTPsu+8++n7aOVNucewFk/KBmWIDYQrRY22JpegwKJI1Kjpq5xa21mwp1233DfJaVRXakIimJOgyqdOIQCFFw713VDx0GJQ+PsKohtEocf3H1olxmJkxKkQMd93hOXI0EHcvEq54PLOeA5nhi4raLRJCpG8DTsdIs05FIkkikKV6C85urn+bOZkomNhl+YDaPCcSwYrBTGeUJBINy3HKztsOwx33ICmAS8TpjOS3goGO/aABY6A3C5iYgcQBReeGSJ1L/nFxkVSUhH5JxKLiSMs7aI1jUvSpF3aRpMfKuuGaa8aMauXE8QGQzaKEBaJf4r7Dc4TW0DaRts0BvU8wGi2j8QB33Npw/GRiZAbccuuEzfX9jIsF2H8+8aKLSVUkBCX1hoRyyNHK0S4uohR53+o+a9dEipC2d6MtCeOHJyCfH/8043MiGCjLkne/+938l//yX3j5y1/Om9/85s/2Jf2DI6/13H9+6PV/xnWv+HVkNKVMbQewYrv0nul6mmmDmpHLyQWSy9a75bGTnPqvr0DvuY8+ifSxG1m/9iNoTARRws4BTWGYWUt65lW4r/pSTBMJsynuG5/Jvi99GiKW0EUhW5irDCKUT7roGBNJhbaFECxtV0bfUrBRhegcUwd+/07Of/6/ZsdlF9A6SDZnSVv3H03qSuIQ2zYLFG2hwR4GFnsYbGybcKFdwPL3SY88/PX52nQbRKXdN71ANNk8BYlIzBS9nIJ0/WYlS7ymBGW5naEiZP31oiRKwezQAfo/8DzcxRfSR3AaMNLgQsxAxL5nUEAySjBQSIvrpGNbJMsvBcvNN57kjW86hnFDPn5XzpLUWiKREOf57684zl0P1cyffxG7vuSp1ElQnzd77UB6oAQ0l6uNhabh6Jvexo0v/XlYW0OsIW4xC7bn52HV1y0sx6fM79k51C7DP/s5bM3t9gcUE+vv+wD9226nX09IVghbJspJzgJlg0dji8RAQUGpXTZv7PYB+rddR+qol+j2sYsPDerqbBPc+TfkSxHe/+6WP33tJsHPEVPJxkS46aaaH/3+v2S0BpoKEGHmlf/56sO85y8fwEvORhd8ACYsffOzSd//YtaXd5LUEEKBzlI2TEpCQrLaY8oCvgCE8EnOj2y1mjQDbnMtR7ARqDOC34w8/ZmCNHgzhY9ex+Hf+mM0RaJEkq+zah+SWSwpAIG2c6tsrSVowquS5ua2fR/SwgJaOMKgh3c9mN8BT3oqYzfP3id8Ibsf80SSq3jHex7kP/7Ihzh1Zsqdd83QNIAoGBMwdsrKmSzD0bQJH2tUlelM2ZzAbAa//LLr+OBfnWa6IfzEz7yVO/aeT//rvhJNjp4HSWN6OJIpGPcr5r/5K7AXH8pttWSwSTAJbJ1wM80lbBWIeU/02ws2detQz+4Ln28f/KPG50Sb4NJLL+Xnfu7nts2L5ub+bo/xz8bYUiHfWrSIkFRJBIrJmOHh21k4cQpaOoBV6v5Nwo7WSbfeTXrsJTA/RxAlWUHpARUiI+ysZYc/jd+zCAiVr5ncfhdDTUTrqJcKWlPg1FI+8xksHLyEjT/7XegN2P/CF2AuvhzXOEIv6xmQhCLanJGJ4lRRDDEoobEY7xnFltZXhHpKiAFSgTO516iul298YYFzvucH0F6RUdAGTGUzklwLkoL4mnjDdVTXX03dGS5t6Q9aMkUyIZ3DW95st8hxUTVrsf89Mx/p5jQKGCUZzbQniTRd1z47sGX0pkogWhBXEUwGWSUPEhuICWP6GWBV5k3JCqgpsKVhc+88537Pi2HH3qyqaBTEg0mE2jI3tBirpDZnPr6DdgeABOMN5f67PaMUaRpF2mxbfd6lwn131DhpSRTM6j189KOBq77jIMvf8C3M/voWjJ/kbup4jdmHP0hx1RWk5T1YMs/bBoOZtcw9eC86m4Iu5yl6mD3kVhXKPCwADCZrRbAVo9lERLu+rXlY1JgImK6642Gyjn/vh+lf/SFm9YTUKwlJ6PtA+4mPI6eOYTDgHOVMGVhFU8QgGDGoGKIt8nu2Y4JkcGtLjdGCRMFq2Sdah0SHN3Dr/YaTZwqsaUkp4CJINNSNcuzYmDbu54GHArGdowmWq68ZcWi3ZWNtH741KG02MooDRuvzTFcrNEwQN8DUDrEVc8/6UoZXPonTN34YOb5KFRQzaajaFmk1z13sND80YJNDE12nO2fwEYsBkslsmlxFyLwdjUqIHkILRmiTYqyh1BkyXs3iYmVBbGdAwCH0P3En6WMfgjCmTSbLjpuEibOsWDmc3wZstsOdVMUxcBUxBczcIs3FlzC6/SSXfc1X0fb2ctONkeZkj/FoL1GFoAZDZgiEtMmuuYL1cYNEyzhAEwKGxL13O8ajZRYXS46fKWlqw9HbPQ8c30/9+As49BVfQBAwrYLUJJegWGBjfpELX/AD+FtvI0kNErKBGolitkG84UaKKx4He/fk1le3HNOJk3DnHcQSiiufgg7mEDJFuP1H7NP/3MfnRGXg3nvv5dWvfjW/8iu/wo/+6I/ymte85rN9SZ8ytoKBswSp3Kr2tPcepr7hQ8zHBqxFxWbQYMyOZ5s3Xs+N//6FzO68CwkWTQ1JA0aHEMucYWAQrZEY86GbWgZnNnB+iialt3c3iwfOwanFFMsw6BFNJMkAHewjDQY4VWw/QZWIdoyNCR8SQSJET6QlpUhoSyQlVmcNbahoa1BMljAICe3oSBUwNKALS4RyniKCMYI6gyMhDDBRkc0xd/3KK5EPvBfr24yqpgMeqIL6nA1ppEmBsJW+xpy4nzV8+dvHlqBLTr9izq4TaGiQ2GJTRMgCQASHpgDWIDLIfVgNnXXqlBme5AZ5Ey/zOVpGzUh2m7BFCW5IMEqStjNdMng8vlb27R1z3mMafN1igGCzN4SKUqhy27Un+dGXfIgT40Bp4LxzhLLyPO4yw7n7PHv3r7O0u0HU8L9fc5r1UYUf7KG1iputUgXB3Xs3d/zETzL76DUUjWLUE5IFO8DO9Rhos800MFtFge0/PrkysJX1b1d+up+lTp9g28VXO3c+tlpGyvTwrTzwgz9M+2dvwGgglhWGkv6s5epX/gKb130ka+QXBaZVerP4sA81z4mvKtQBOkO0wESHiCchlBedz7nf9R1UO5fR1jAaR1760zfwljdWaJPfy4RMzbnrgcDd986IYvnwtSWz0RJtM+R3f+swRx80TNsFWlUSNUZqtFVmkz4jX2ABIzNSD9T2oejD4hC7cwHVAMxgvAJpkvEu29INEUmTXBGazjA4SAHR1AlOZ5yIVShi1wc3kFKE5BH1xCLiUkU/FbR4UtHmFgKOGD0SGmwI1K//E07+2isxkw3Kch5XDqiiUoQZQoBqSDCBuFCy/OSnEEyJ9ipS31F8xTPY+Q3PZs/znkfYtcg193l+6MduYjru4bWPKMwvKOcerClosCYx36vQBLENbPqA15b5fuSeuxInju9nfWOOY6slqzPHb7z6Fh66q8Anx6xXEMsaqRwy2M3yVz2L4Rd/CQsvfh7uwsvRuT1A9kPIglNCOVvl4z/1UqZvf0d+oE1OBiyRM29/J8ef/3xu+P7vYfbQkYw/6gxcw+dbCJ/2+JyoDFx++eW87GUvA+DYsWP85V/+5Wf5iv7uIZD7npqFP9ra044n2F1pW/oWzX37GCJt4wkrK8S2AVHKpkWnU9p20qGAJWceAsb73EYAYug48gJmcR658BDxzpsYiEWrkmiycE4ql/B1jyg1rVmmbgd4eQAtZsQwYDxSFgfZwnRaz1G3UAc4fqKm8QbvlaYWtARLmYMCt1VFF1Q8QrWdgVpjMN01mhBgPMWubdImmzPhutmmGxKB6DHrK8jOJaIrttUNc291ixT4dz/5W0BFzco+CIo3Sn8cKHwNi0t4YzGd7avxOR4pq4Je28J0Ay0NOl3PgURVEFoBk3v4+WJC7ttL0VGgUof6FlT7aBTW1lfYc6Bm315DGz11o4g4RGxuARFYXRVOHh+yvjLA+5ZnfPmAD3zgKF/4tAG2dUzTmCQHeffbIhtndjGdOAZaEZNi6gn92GLvfZC9p0eUZ6bYYIihU7hTsK5E1EDs+vHkqhWdGZSQ+f1dFIQAVg3BgnHbZFPcVnk6W94jSbed/1CYihJSolyf4ZoWWwjEhLOW2Hp0Y0yijwkROz/AGw8pYurN7G3r8oyoM1CVECuQPi4UuOgITkgX7eOC7/+3GFuhNiFBmK0ts3FmMR+Wss76pmXaCifWama1xTlD2yaMWJxJ+Fmf9TVH0n2Mpo6lep6qCASESQOrm0IKPapiE+MiiR61FJhKYFBhEWxMyHiM04CEFiO5t2enDRprKOexTYs2becoenYT2JICp/skQLJNdlSkzUFBeWbKQnJsWM4akCUlhhaTNDt9TqewMUZU6V10AfG++5CQi1K2Ttiix7iwDC84SPXFT2T02j9C5yrqxZ0sPeer4NJL2XHVFxGrAY3xrG0Kp0oHlOANS4uJSy9Y5aGjc6QkDJdaOF2gKG1U6naTK6/awTUfypbdo5FhMj3IbDxkbbUFlqkbISg0E0uvBIpFHvOCF5Nkjp77oixhLpbWDGllgsaIFSH6gASIa+ts9VYEsiPoZEZ5egWng237domK67xBPj8+vfE5URm4//77+Z3f+R1+53d+h9e97nUsLi5+ti/pbwz9lC+MQqkGfMpAopQ6Wl0nwxs0q2wFpfAt1gfUKKPb7uLeX/o1io3TOBOzln4nzCJtm8V7UtYUyDsH4BztfIU3inHZvxzjmPXmOHrK8vL/eAfT8TLT+nx+9j8d4dSJc/Gxz1//1Un+3XfewtvfdpK1seXnX3aYD7zf0TR7+IP/qdx1R+LMGcc7/uJMzvKkBRO6wzB0AkpFRpFLBuJtmUYlIYv4jMeUMTHqzyEUSLtFBwMExnfew52/+Cr8Q8cpYxbTUQOxIAsq/QPeE7YLiKLL74kqSZT1D17N0V/7LXQ2I2knrRQTNkCwFl9AtbnJmVf+DyZ//CZO/cKvsWN1Svb9M4goIi2YFjEBayoMPRDffYYlOdUzHDvS8KpffZBe2afnEnfc6vn5l19HaAZoNF22DadPl9x/34A3/W8oix7nXhI4eOAY+/bPKHs1RW/C3LwgGrnvgcjHrhsjscgtp3oK4pnechM6XsXrjFgm6gceIDXTbi0UWSs+dId9R10N6HarIutb5HoKmrDJ0pqt78Wu7ZIbNUFyTWWLmm+7akLd/f8wKkWUXL1R0BQpRZj3UGxMOuyFQ8OMohlRf/Sj+GaGdwZEMcahrYFen8ml5xMuPb+TLrZ4W+AHy8SqR+tmJAuGFieZItj4Hq96lecPXrPJTTeewdfzRPUYGxCzyaB/BmNazqw3nF5Z5PV/OOGXfv4I73nXJk1KbMw2+Yu/XuOu2xUbHUJJEEuLkOiRpI9RsCkh403K5NHQsXCMsvaha7nxd18DbYtMZ5Q+iyxtxV4Pwz2CdBa+KOp9/n6ISGhY/50/wd91N9hOWTRlTEJqG4iRSgypDRSzltR4ml6x7d1CSJgGKAfUhaNdmoP5Eu8cvjdA3YB2bpEN26denMclofLQmw4YbzgMSmoVZwILiydZWl5DpGV59wylIeEYTyyqDQsLDUXZsmP3Ks6NmE6GhGhQccRQEXzFPXfN+MWfuYPRJBDsFK2G1JUDN8CayHSh4O6D53Js5+7tyXEKtm4puorp9mYaAw7oxcgwdOZi3Y9NVtT4hzflz49PGo/KYEA7g42tv8uy5KqrruKpT30q3/It38LXfd3XfbYv8W+MT8pdlWw13PViJXQ68SmdfZCN6crgQpU0bwApMDlylPFfvxNz8ihO267U26UMIWCTYkTR8DD3O9sjYYhiSVXu03rrGH7j13C8HfD2d2wyrmtqhfe8t+bUmYo2Wa67/jTveUePmz8+ZTS1vPH/t84N10F/sMjh25appwtMxwPuuQOCBpLMUBIhJqDpTBdMx73u1M+MoxXDxJIDlukUJbL8XS/Azy9nzXVRVAS14I+dYPbGv6B56GjWAshNFFqTaAwE83dXBc7WDPJhF5FM0JPE+PBdrL3jncTNDYoEto1IDEhIUFYEZ3Bra+if/RX6jg+S/vp9LKxNMGI61MLWL8+BhDEOoQ9EYoIYLRoFI3BqZcoNH2/YveMc+rLAkfsq3vbWhvW1Ah9KvDckLVlZKfHtbq65uuDSS4V/8/w+v/obT+Vrvm6R0CoSB/SdZ3F+hrWGW2893UVVgvqIOEvcWCGGMcQJJs3YPHw3MtlENOKcw4rLKjIa0dojbSJpzICsmLbpfokMzpIAqgFpZtjg830n2X7NVvtr+5DTTIO1raeIKevwK4gPIIJJkfkmUK5tZsnksodTZT4GuOsBqqKHnxvmiklUjOmBG/Ckn/lxzvnB55MKh0WoKChwWU/fOKIqxkCINVEbUlrgne9xvO9DgaNH5gn1IqoVPlh6c8f47dfMs3v/GmsbgRD73HC98md/0nDr7VPa4FlZ2+DuB/pc87FVRBeh6aNJKVKiDPn5xRiIEXP6JKVvkJhxLUIk3XWY4+/5AGjAbowZaqYXKmdldBUhGvAG/JbwdwpdIJ8wsWHzHe/HbY6g2wuycGdCmiYf1pooxdDzigmZYRJS/pxSTEhQKEtULFoUqHNYU+GooA3YZFiioCJbAusE8A63UaIxg2atO80P/+Rj+amfW2I4FzoZ6wJlyHijRz1NLCwkrN3kh358kUseOwYSmxNBjEMR2jZx7EHPO/5CObMSCeKoQ01foQoJscL8s57IlW96Dft+4sfwxuTgqW0ZRDAh5oZfVxkwtqvOqWA1W1er1YwnPasL/fnxaYxHZTAAOSCIMdK2LR/96EdxznHFFVewc+dOXvva1362L++TxrYiKl2ly5ANbrpvZIZALqMrZH61oUPe5564qOLEUKTI4toq5eF7KFJCcah2r2vbXGLXhHqfe7kI2F7m+xuL9LIEa3AF/sJ9pKokJYPGFlUhpMAsTbj66jGrq/N4nePuexaYttC080Qs55zvyQl+QIxnXNtMyUoD7rq95rabanwsUOPB1ICSjIBa1JSEwrJeGqI1hJUVNDXYJz+Z2aBPpMkgSRE8gtNIv90EE0jOZgBhgkIThSru77aU355xJVcDkKxUVyCUGumNNyA2yKzm1MduIm6so9ETEpiiwE3GzIcaU2/SSw21TgkDR+MsmAHqLaQKUYcaxRU9VA0P3d/ysWtXaHw2HvJGidawa/ccPWPZXO8za3bTemiD5YPvX+NNbzjMeGIRGWDEsHvPcfbuUq64Ahbm6yx7G+coXWDXjhHD4SauVxAteFFMFFw1pO9hqU3wkRtp3/R2Bvc8RL+Z4DQSUiBsyUemwOzIMUa334WLgYDftjSOWYwZgEhCQs3mjbdQP3g0S0Z3w3ZEwa1Zhlzlr1bXCB/4CE4nHWujUzyUHND2a4+9/0HWnDI9sB8zi/THNUtBwRWsLg6ZWYft9TrBrIp4zgHYuwPXiRaJChqUkAwP3a/80e+dYbJ5kJTmEYaE1rG+YTi1ZpiNSkQbUthAk8UWNRdeAvP9go1VR9SatU3DZLpAwjGdwfqqI8RF7j9iCVFIVhFNFN7jZITvBxpjMZqQY8ep6nqbCaNO6c+m7B9NYbqJPvgQrs3y4NCpenaUVm8E39FaJQZi2xJ8FjzStVUWZxMK9fSSdCDOXFa0dZ0pwJL3F1fnQDYUhtZ0EtrR5/J5VVBoxo2o67NqHNbO4STijFJFxaVI4xKbGjnTWE4cAbEVChS9KXsOTjn/sjXKYoNhKRijiJnjmg+UHH+oZDAIFK7h4suUajAhJcv99ydan1thsxmcOSNMZ5YjDyof/pDDFVWuJloIzBHmdlBefhl+3znk+qKiIWJ9RFLaXmVb9ttpO8HIkWjsVJUfSTry5/J41AYDW5SjO++8kz/6oz/i13/91/nZn/1ZXvGKV3DRRRd9lq/u7xgdNzZ1tEE9m7puobiADoO01ezuEN4quUSoJhI3zzD9i7+iTKnjUned83p2Fmnf5h6lArge5VWPI5UVUvXAGZIRYr/KbQOxhOkc+CEins3Nmh940a286Q2LpLTIR6/1TCZKVIcPsHPvBDFjqmrCwXOVWQzU0eJDwW/9+nX87EsP0zZF50TnUTUEDIhBi4rWOPpPezKtMzTrqzTqKQ+dS+sKUmqhA61JNxvBtsQCZsaRur6g1YTdQrn/vRXBnMNapSvfG8okeSOtZxBq2vV13vXfXkl74iiqLWJsBmqNNknqCWFGE2vqoiUUIWu3tw6hICRBbInajhVAxev/8F5++qV/TYgKKSsERPEcPGQp3Iy1UWASKsZTmLXCz738IV7x8xscvjvb6Q6HE77iq/bScyA0CIakDWpalpdqDu6dcuXjFR82iSYSDRmgVsxjkqXyAf7kTZz+vh+n+a3XsNDUSDMmScwSv7MppMCd73gPh//0zZjZBEtXDRAh+wfm3dUguNmEu371tzj6/muya6ZkoKDVmINPwzZwEALNTXdw5NX/C6ftNkNBNftkRI1UTUBPHkcvPgfzDV8JyWKagCQhVn2Wv/2bmZx3Dt5ZQpmR+JYK1GbtAk2oUWyZ2xrvfMsaL//PY04c30+ih2qBJodvYWMFmpHnmV/W8oxnb4CZYcjYg4oDrK8qB84VJtNADBVKZH1FaMbzoCV//Za7mLSBmkTyFYXkoHrPV3wz9fIeUENaWaPyPgtUieBtwo3X2LE5gsmIjfsfoCBu+1xsyRRvqXRbhVITtBGd5eqej5GVW29n7f67MAJF7Cp+5MPQTmfY1LFlAJk20Hhir6TWLKFtokd8A/0CkwzqKszOXSx+17dAOcjA2NKw7UEgSp0SKy0cfnCKj9DUgug8RIf4RKhHPPFJNZc9dgNM4v3vTdx525CFBYeVQNVTev2AquPwnYHVlQEJS9sIx48nJn7E9dev8d9+5jBp6nCZJ40LjsGsDzOLU5exF4bM4IkZTJy6ltbWU62djkoWITK50iQdoPrRe7R91sajFkC4JeRx6aWX8ou/+IuEEBgOhwDMz89/Bi/kYX8Jf+vBZLTbJ7tAQOgyAzkb5W6RvBP5NXYrYOjUtxCTDxarGJOoJg0pCVBiJaK0xLrOVDeE1LYZhyCgtmBw1WXo3ABsCWWmDVYU2Oz8kbXKYyLEQD0VZpvnsLE2IKlhbdVwz52A9qnbKYO5BqGkKiNPeNJObrvtNCurgf07Ctp2nvvuHrKyEin3F1TSJ/eXM4VKXEUsKvpf+jQmH3g/JgTKuSFmx07UFqhvM7iymyNJXVlUhS3egEpn/Wo7itvfmwl0wQD5H0YVbJbvpw5tZmY0NXtGU8xsRNKWJIKWA8L0KM4V2HtPIHXA9y0mdERnO2AyC3z81pN84QGBIiGlRaVitLGDyXgnKnDvnZE7b3EkdrBvd2LtbmFzs0ZNhfMlhesxm+5jMmnY2AllFbnoksBzn3sog6DCMGc/1f2YYoV9B4fsWqq54knzbEzWQCJqsjlMWNzJbGNEKSPMeMSeFEgbjtYlfPT42BKSR32LeE856oBnvsN5iCO3VKSjuoEaRZqG3SdWsetjJEXUOiC7MEq3hlNXU5Dk4fa72T2aEDvDpIzVSDkTjgljsndF+ZjzKb/0C4i/XCF1XvBt2WPnv/xa7nnPh3PQ1gXBTgErRJuteA0GNS2VsUzXhtTNgBafg2gVHAZNwnSzIM1mfNM3LbEyPcMH3zfCWsUgWGNYXR3zrK/ZzdXv2WA06WOM48QxkFhhkjDenOPkaYEK1o9MefKVQ268z3HwnGdSXXAZ8cYj2JRwMaAdS6SRhJtuMqgbaBsk+jybnQnY1h4hZJ+GAsV4ZXzb3XDkJIVm4FwaTTrzKZ9Nx7YYH6LY2Wwb/yIqMK2z8VG/R9IcJBTeI76FfhbKimWJ7Fji4Pd+G6duuzdX5myBSxYxMIhQTBImFjQzGE96vO/tiuhuJPQxbQ8nM658Anzi6AqfuAtiuwsjC+zbv4CVhsIKzuUo/fiJHpVZRuOIphaOHo+EULGxGRjPStpoGMSCNhg+cv0ZnnD5MgsDQZ3HS6S0oCltY1u2ySuyNYU59TGaaa7bOAw12wHX58f/+XjUhk9bxh9lWbK8vMwb3/hGfuiHfogf/uEf/sy7FnZ0opAL0rnv2kXtnXdcPtCjwaZAIOIBSUVW3EsWkxJJA4GMLhepiRIwyVIki9hcFZBOoS9JFjURPNG0gKDR4M85SGuVohlhOytYTCJWczSVhV7u4ak4pDBYUVI1ZO5bv5LBF12Bpnm0HTBLtjvAE8GX/Narp9SbhpAiVWExzvHFT7Y8+XHK9R89ybvffx8RCNFy95GGD33oAQot0JC1CipqJNVYk0jLPdzB3djCIHWgLPpgBwxmFmmVmUSUCSbU4G2miLVK1aHWc1XFZMz+3xMI5FqLhWgxSfEEPL47xzIArpNVpJyMEB8woWUynCd97dexuTRAfY3efzdzvqGt5uELnspoUKGF58TJDV76n97M5sghUpCqHK4EDy191MIrX307//VnxuyaL7jg/AG79ymxneI3+9R1xakzixw744hiaNvAV/0L4fnfadi5M5A0ogiFEZ73nTv51996gGd+yQJf+4172HtwxspGi1x+BUvf+q1MludJz3oWzfe+kOYpV6HFkGAarEyxSXBBSaoEHMwmqB1i/BqSNkA9QQ1Np8TYI1KgmS0gHlIFyWPxiN8yizEQcl83iu30ER3GK6vXfpB+bLBqQBLWDYgp2xaXDZi2IUok9BdgOKB19TYItjYOqkXo9VGJGE0kn0jqSdKgqSVoyqrV6hC1jJqawAwlU9KswKWP3WR++TiziTKdKv054cqr+uyaW+dpT55jfoel6EfWN4XFnbA5ndC2liYoGxtgpIcrAtYuMFoT/vfrH+D7f+SdrM0Sv/LKO3n/B04xHRSECMZZEgWp8blPr4lQ17gUYJKz2qZXE6SFZLvDLONqbAITDWjitl/+r4zf8jqIE1xQYiqI0oLNlt5lZ76UYoGpazA5SBJJmNmEGMcYM6DAMj50iKYoEfVQFNmG2w6JUtDOL8NjHgNS4JIDmwg2oQn8bMYgKiqWMyvz/I9Xn8k0Tavs3V3y0v98Hk/9wr085xvm+d4XlzgzYtgfsrzsaF1LoS3f+FXLPOc5NU3dsLk+xKhl1sKpUyN8s8jq+oDgeowFkhGOnlB++Affw433rjApPWoHgCBVL9c8pcU7wajFcVZobAsPpFvNrS3a8cN++vnxfz4elcHAlrSnMQZrLTfddBOXXnopl19+OT/5kz+53af8jI0uo/dkJTGJGSbkOyqRaiSiaNDugI4kQKJBkwdbYKzmviZ0gKwGJWGipUw2I45dxIaIJMW7jtdFQE3I/Xj6xIsvoh72KMZjNCR84UAD0s5lc5yiB9IniRKLChRq5xg+59ksfPkXErQiNhYKi9qafjFmzx5YOTGHTUM0Qr+0KOt89VcoF5/fYmSOlTMF7SyS1DCTgpiqDJC0CRHFdjr4yTrC8mOQXU/IysMtYPowILcIQkVKQpytkJpRRxsQ8AkXO6lmAwVCD7YEZ/+OkXNI1CK1UqxvUjZ1V4URBlExHkgBYzMAM0VlY24Z943/inTuQVQVozWikXE1wH3Zs2nnlqEQpjPHaGM3mhxOhpkznxKlNUSdI0bh1PocJ08ucXCfcv55A5Z218z3PWHmaFplPBowngSCWsajyDd+Y8WLXnSQ0vkszm7BWfi6r93Nv/yG83nCVTv49udfyM59JSdP9jk52M/Si1/MbO8y5Zc9k/0//VKqq76Q5Ae5KmSU1pTYaYtbWaP0AdlYQ9cCVb2Cq1fQ2QwTU9e2ShQaKMiGWVYSqiU1Dd5lnWlP6ESkLCqJiMFiMiEmKtpsAImYClSgWtyBFgYTIz2vuLYBEtbOASXYjv0iHUc2DqjmdtBKj/E0twS8a0kxIm1FPXOcXmvRaIlJWJuMsJVDGGJTxBB4wpMN84vHGY8SaxsFvWHBU56yyNLgBE974gL9vsO6Auv62CKhOiGqMJ4Ko02YXxhQVJ6yP0Rb4fQRZW1zmZlCvb6E33CojiitoZ6rYH4BHY0zbTZBrNuOLQMuCbYEZgG85PXXRiT6LjAHNHLo9P3Mj09RaIRWsb7EmkQkUReKaRKmDtgoJF8TFuYQl/UL8A0qEYr5LEP8uMfR9CqoZ0hZkUwiFBXGFBg7ZP7Si0mVQ0JFMuCloTHKOAZ8bJmoElOJ93uxAmKUXbsN3/cfDnHovIpve875fNd3VjhzBmegX7isqqnKC553AT/0IwtY67MZo0RGMzh92uPris1J9vlIAaRr99nJRayc6tFqgpCwmtDFPVDNo76FNEOOnUJOrUCdfS2kC4RyYBU/6TD7vFTxpz8elcHAp47hcMj8/DzD4ZAPfvCDTKfTz+wFyFYWmjNN7YxJChGMDxQhAB1vGovF5E5/B4JpCsmWoaFFguZ+l+ae9PZHpLm0ScySxFtD0QwCN5ZJv4e94jFM9u+nPb7GKFkmlzwG03do3VCmFi0s0Rpsr8Vag/ic1bWxR9OWJCwhJExSCjtm3/7j7NgzBRJFOUBMj6pK4E5jygnLe0qss7zqlTNe+hP3ETvN/hhjh5GYIamGUBJsxbi/yNX3nsvv//qDGIZQt0jZBzdlbVDTKsxPHNe/6g9Y//gtoP5vzPWnG/SrBLSuOfyq3+P4X7wLXMS6wNLaBs2Hr4PZhEBABWauYGNuDp1bIhULlEkpFFQNySrMLyCzIVoLo5klyDyaDKJ9bCdUROpz8qElbr95hrMW1YTQUBawY3fDwYtOU9oJVeWZG4JJjvUzFUePnWHnvogrZ2hy2Y0vc/1wLpdfjUmIgYX5JW786JT/8d/uoKl24t0cLA6g0MzLth3Vzwh+7yLTD76f8g9fy856jfHb/ppjv/kH9Nc3MR+8hpW3votBTFRbNeytMqyCSfkQzweXdhTRBiTibSLk+jcu+E45M4PgolHq0tIYJSz1gUAwgRRbJEWMCn3pISk7ZeZeb8ASwVXYK57IdSf383P/+QZmM0OSkmQK0II/+v3TfNs3v5tjRyakBM2s5uCePqJr7D94iqo4g0TF2JLRWLjr8AZlCWUhnHfhlH0HEk6gMo6qPM3FF0aciyCGt//1UY49pAznoLANQ1cRPGgqsDqf2RVRGI0jNtSYQlhdGqLLQ9be/94swiGQYkv0NcQJIbQ4M8A0LlcQVUmmQLQkiuQ5nAXsLFKmHkWwBI24uqFKObgIFkLRImkGGtE2UJ93PivDBaa2JNoCV/VIg2HGfPQKbEroeIwOejSF0M4VGAsmRWxhmJUpW2GrocBgE/gGYqqo4xbKUXCShUNiUooi70fWwsJCwQUXRcQ0FC5jO1CHK8FaIWp27ozAvffA8YeWqSrL2mokhRVcmxDvcBgkzfO6317FTAY4GqBH3dtN7YZUoY/cegP3v+Q/8Imf+HH8sWO5DdD1ZmWLCfPpbw2fHw8bnxPBwAUXXMAVV1zBd37nd3LppZfy/Oc//zN7AaIdnaUrY6OZMZAS2gbc5iY2hC7zcjhszmg7QFAg0Yse2RxTxSyXCyWKzZgCgI6rvbXot4ZKhstEUdylFzL4rn/D0nOfh28Fe8ljuegXXokOD+FpmBQN0Xrq2OOMXaDcscR4pvgIPgptAB8j03GLqmHvXuF3X/MYLr8ysbIy4ylPaegNJ+BGmGKCrQKPu7LHlVcts3JiBx9434DWL2YeeBRCgtG0ysAskzfB1pbcMTnIB28S0DmSb7GuJIZ5Vtw+fDOGMzXh6ttJd96FysMcFD/9j6UTXgokP2L61++h/ejNJDxIy1zT0H78FuTUaRaaKTIa4QW+4PnPpVhcRKshSU33mXbUq9IyLSpSiswaISVHUskIbwdIQBE2V+d56L4RiIWkxNDgBA4eSvzqb38RF14MC/ORL/6iLMKU4ibf8E07+OKnDxEzywwFNQ+794jQYouEdYo1PZLfxZ031zRmyGSwg1A61ClBDDOburhkyPy//GaGMTB/7AjDuibefhvpppspxhOqEyeZ3XMf0maq2sN30y1gFrHFhmYb91JOx1SzKWIEFYNJAVZXcM0MkwLWR4IIdtcOvEDqlZnBoB5iCzGSDFTSw8SCZC3edq0vo1AN2Pf872b5G36A975vnpVVZTQxBDzJeD5+s+WWj+9mZSURVWmbGYf2J+YGp3n1b+zi8Ve1OJPVE8qBAztlYTExHDp+43e/mm/6lt1YE+kViac8eYmnf6nFkeWcT5/cw0MPQNUPFMVx+mXChw61HwtiDcELa5NIYyp8VTEZ7iDMz3Hm9puQ0FKFljRex4aINmfQos1tjzNrsD7GNBNamwjGErYMqmYe04DMMnAzmkQ009wyTI5BAPETmK1AnJFOrbD7O5/P4AUvoN6xA9l7gGhLmrJHsCWxMDiBkBL0SsRYpKgyiFdyto62CJmfH9qKzRHc/6ASKQhBtw/bkHKrQ7r2xtYed87Bkn//gxfQK6f05gJSjEFCdhMy2klvWVR7rJwxzC9YLrms5vRJR2rmmW0IIWTZiyb1eOABx2RFmE0XmTFArKdhRhkcetutDN/6l/h3vIuwtkruYT0MsPVwV8jPRwT/qPE5EQzcdddd3HzzzezZs4dnP/vZ7Ny58zP47lvoQO1AQoBIlm2XxPTkSW5/058hp091LQPBaod1FUWSUhhHPHKC+978F+ATMSles9EI4lGJ24eCeP8wOlfO/IyxaOlolueRPbvQffuJYvE7drKxeD4/9V9v4HTd5/S5j6NtB/zqqw7z3tmXcrr3WP77qx5iUhe0bd6vIzCZZPGj4VC48FJLbzBlPF7h3IuPMdcP9AvFhAHWQa8vzM2PwMxz5MQSH3j/AiRHXResbLb81E9dzQ0fP0HURJE8ZetppMTqbkJ0SD2jF0uuuxn+8rZF6r/8c479zv+g1EARG6QOZ59t+fSfcklCUsDOWKRhWNeYFMG32NDC+9/H2kt/mn3338v6//ptis11ygM7MH3BDiuCtQTjMBgkKmnOsnreAL9QkOos8BQRoqlJbpLV8FzGNEzGJbNJVkl01mIFytJzwaXC8g5PaT1XPXHMrp13M6gm7D+nYX5Os2sfHShK8qed+SIJ7QwsrHWoztG0iyQpWD2wn7g4ByK0u3Zl4yWUthgS9pyHJIuNXfn1+AmSNBACBQHSDJzipQP7dQEmClEsSotr6+yM6JVPvOHPOf7W9+CahFMwGxvc/Ru/zekbb0SamrKOubd96BBqSpwZUDRgGo+JHkRpqwKKMvtEWIvfAssa0KIg7FriHp3j9GbFzTdt8v/9yEc5dQxCErAFTSqzkJQF38447wLPgYP3cuGFkcc9fpVdy1kKeMdOKMtN5uYihW254GJlYRkKJ1gT2bdHWBgKCwOPKxJNs5e//IuWPXtbdu97iGowpgmJNigxBPwMvFfWZwM2znkss6V9WLOTuLCTYqlEzIz2oQcY3X6Y+aZkcu/djO+7Bxsi4fV/yurLf4nDr/41XDujSZ6iA8vS1FlzpM7GX1YbVGsiwnR+GaMli/c8yOmX/gLNmQfZWFuByy5ndmA/7cIccWGONJhHdu6gEQOlI4mQnIGqogqWMvZQtXjJ4OthFCSM0TTlne86xgte/An+6H/XGAZIkynLitDUbWfBfDYQAKUohHPOVZz19EtFfZOdF/G5gmRbql5Nb1jT1FCUifnlxG0395is7+EXfvZW7rh3nRkwkz6xcNxyyzp/8Ien2FzeS1GNsGaK9ROqaWBpGlmIuU2sCKTs9SJ/m9vp58enPT4ngoHhcMiv//qv85KXvISXv/zlvOENb/gMX0HaRgZvTWgH80Ojp7rnHsz6WtYSgIwJIL/YRMWqpWoCGx+/GVLsKEdKkSLWzzIQCXJ07gNWDEj3UNiMqE39gtYK9IYkVxGtUg9LVuM8f/leAwcv4Jmv/A3s8gHe8OfK6AkvYH3xHK69KeGbPjFkaXUfhfU1IUahLAtcJfQHSll4nvCEJTQkbNOnZAeVqzAGyiKgsWE6hRMn5hFKQuPY2FT+/M0td9w3I6hBYsLMPK00eNNQmApVIaZFPnrrgE+cuADzrvejH3kb3o/yQ+7zXP3foEBUBcpE2/OIRCQZXDDZhOnOOxjcfBO7xyPMDbdgV9eQUqGE5LbEirIND3ZA/9BBvuKXf5Ly0stpJ0JsLMkLrnJZ9ERLZn5GspHTq5aUKmxhMKI4ydl2WXh6VaBwnud82xJvfOvjuOySkoVyHqMRKBEbuwyna45iQYocHIjinEWkpPZDopR88ctewtxjLkHosePrvpZqcQ6LolWJ6S0jtsrAyySE9TGmiJiQHRGTVTDSuefl9ZTllLMrY5KA9TW2TeCF8cdvp/3oTUidMFHQ1TXW/upt1A/cj3iPm0aMcdgrHkt76Dzaoo/MsrCTDZ7CGvywh5YZvJZMfnI6/gLJwNTB6RQZtzW33zLhz17fcvp4ALEUcw0qLXXIOJQYPV/+Lwr+x689jX17evyXlz+FF37vIUwSdiwqC/ORuSoD9awIRg2ilqocYAvPrp09vuWbr0RljC2Eelrw+McX/PbvfyX9xZrpFKZTS1KoZzBrDSttn8f+fz9F+SXPwjKg/5SnkXbOg420J04zPqXMwj7Cao05tYJLid4t11K89vdw7/kgblpTWosdT+m1NRqafKA1NeKUYmMDWzc0lAye9zzsrn1Udz+A/MXb0ZPHSaGBpR3E3gAvSuyXMOhz4IufRn/PPoIIYoRoDKno4aNAKtBoSOKIzuLrEvU9Ukp88Op13vpXPU6c2MnCsCKNO/aSQtvkVp3IFl0qOwWSIr0qUlqlFIOTeTQKmgwpBEwSLrlkxr9+7oDQZMbK3LxhOmuZToe87R1jjq94vMl7D1a458HATacuY9fXP4+6v4ta5yFNSFoisSSogU46XLrWqaqCD58vCPxfjs+JYOCKK67g1a9+Nd/93d/Ni170Ip75zGd+5t68W4GGTg9bO4B6xyxwGtl3+hTNNVdDaDoA3NYDlTpbUENpwDU1EPIhFRVOrSLXfJRhCNtvpiFQuSI/jKK5eSeGNCxzFiAFYgymCIQ5R1JHK0v4vsFduINQwUTmqffO0w4FW84hqSD53OdPWrC22idGA5pd4wZVyVzPcuG5C/TL4wzLln61wrAPziQqC9iYAWUohSuJjSJSYuQSNjYG/PmbV5nMCmYMOeXmCbbCJWi9cvOZvRy+pcKGfbjVMYvHj7LvxHFkpmSdgq5k+WkCQ7doiCKZSplcrqSgFm2z1kDPB/op4hIs1IobNWAsYi2uqnBJEY2oUabMoXP7sBeez613FVx/nWFzdIBrPpCYhUXELXDqiOW2T8xoNXHiROLEsZLFpcwOEQMmFfRdgTGnqYqGxaXIFU+YsHPvKapqS8o4s/6zNnDT3YmAVoDNPgBOsEaJWqAW3EUHoT8EqTB7DqD9LBgj2kBpc5Yo+bAvolDUY4oHHsQkQZNAylgWs2VF3S1qFYNowIY2q+5pYq6eUp0+2WEhIG2OWDxximFTQ2ixbcIniIvznNizi8niUnbZ9QEaj4mJVDiSRKg06zoklyvMxmZNDYXYRKZ1op4ZjC5l0HChtH5KYgfXfsTgW4itsrQM51/g6RWeXbsSu/cYSlOyZ3eNyIPMDyLOGEi5/ZIQpDCUhcUaYeeOEluOmPpVVFp2LScuuLAEY3jHWyfcf+8AHxKzmdJEw91HN5GDe2HvMjM7RXfvoNYS1EHqczeXcLWcS+3mIVb4foWWDb3ZKZZOHyeOZtgUmH7s49S33QGzMTRTtJ0RYku8/S7SaMaoGOKvupL1osJppEhjeNuHqFY2UVvmCg6R0HeEokB2LBFMgRGLqlBoAb0+x3cMGO8a5iANQzsccGK4jw9e3bA57pOaOUzYiTXC/HKibQzaWUNn5UQ9+1SJZgxTTMzPNbhijapvsHZMUUVQR2HBscrCYMLFFxkm657CCYPFBnWnqNtI3eynbksIIBpJHtZWK+6POzh24Au4ZvQkDm9empWsph6NJbFrf3XUJVCwYpD4yZWBzzMLP/3xOREMrK+v87rXvY7f+q3fom1brr766s/4NWxxXLf1BLb47aIMRqvc/ob/nRG/mvNM3eIjxiypK4VFCV3POSIhsnr7YR74vT+g2g4GAB9wZYXGRIogzqHGov0yH3Qpi3Co8fh+lmtBhaAOvNIYQ20ji3OatQx8ykjwADG2aHKcON4jREsMuTjdK/q5xHqh46UvP5fnPPcAP/KS/Tzti/ZjkcyTRhCTUJ0yP6jwNRgRjDXcf3jMT7/0Xu68P7D7C74IufRJTOwQlyKhhdd/bMYfvuUIRQKZVMi9Jxg8cB/GO6QZd2XA9HcHA59qZL5lFU0ue6sI4g0FlmRtnq8oGPLBEzuaokmG1CjYCqTAuAFZr1FpCmXXlU9Be8sEFnnN797N7/9hy5nN/fzqr97MbLIDMbu44dqGa68ZIabHjdcn7rrDs3N3xLgasYomxRjD4x+/j7Ic0zOWggHf/p27eca/sBjjQC2kksxPyQqO2zQKzVdknGJsoGkyCryxGSuQxKC9HrXpLOzbCdrL1s0GiGLoK/RPncYcvpcqCLY10BrKkCtVdNbQ0YBVyfr7MXQsl4TEGakZo2XO4sOJo9iNNWwzQZPHKkRriQd2s+vfPZfisRdni8SkaIzEpkGcI7Q1FDGzgpLL92gyRqOMYFtPCJ4YDIJDNH9GMfSJ7Q7+6k0bjDegnUG/MBR2goSIM4IRxdBw8OCMF3//5SztMIisAw2YSHKB5DyDIRgTKArPcLjOc769R1VMWJjzFA586PGHv9dw/fUWjKNulNZHVkaJWStMqakXZ6T5gnbqIA3wssSNXMjbZS8TWcLogOj6tAnEBOLd9zG78wiVT9z2v/6AB9/yNtJ0gkst0kyonMIHP0K64yTm8Y+lfMrj6X/DlzPrG5yvWX/172PPrGOtw4bOi2SQnQilKknGkZKSTJ5T06s45/v+NXu+6UvBGkpvGFx2KdVznsv3vOwW3v2hEbO1RJVmWLfOcFdg3AEITQYKdG0qfdh/gkbL3v0VL/i+vRy8yPJjLzufCy8domadCy+a58dfOuS531UwPy+MVysqV1EWJUhB3QjeW2IruDZShAnUMNlInPQbTJ94Bb+z8YW87+TFiDqkrrP6pa1R9UgEDbELeBXGk4dvCP8EO/o/v/E5EQzceuutPOlJT9pmFNx5552fuTfPjfsObJX7nlEURycGYg1SFlkJDJ9bBdFiou30Mz3JWpJYrAhooiQhkrC1Z75pSXhSMkR1SKukfqawxZjyA28LtBiSbAl2glZzJFehhUOsYKi49UblzjtK3v8ew3SzjyERa0VVaGPFW9/bcPi+kqBDHjhaEkOBUyEmqCpHrxSWdxme/53n8IQnOb7vxYfYe7BCnJJs4NB+y9wwW/dWfcPHbhjw1rdGxhuJ9dN9xnEvMRXMXbiHuUsuIEZP3DjC6CPXM03zTJslFINtRhRRsSah4w2IExZCQ3ndx5HpBDSL4mwRiYIkrJ/SXn0NeuQ4nqYTaAEdbTB9//uw43XAIlqQxOQsOWTq5lY5PGJpbU0bxuDKPG/O0lohFIaJlAye/CRiTxgFx6kzu1hZHRJCn83JLtZWh2xMz+dtH7B4f4B9ewbcdhe0ybJ3/ybWnkSDEmUTT+IJT5nHFGsM+4LVin/9HefxhCdUOdPr2kAmW7HkddZpsndKVRgLWKHxkQ++PzE+M8dHbvTc92BLcg51LcnBVHvcceR87k9XknSA0QYJLcXaJJvbkDAhB1tyep32vW8n3X+Y8N73Eb1HJZDUohFMaMAkrBlThFk2KdIppvYUpkTHbV7P2uB1nrCwg0P/8tvonXMOzFo0Nhn1UCamvSGtzIP2MNKndUpjKsS1GGZYBR89tH0eOFbgTcj0uAgmTDHJc88DPd73Ps9DR5Wqr2S7TIukAiPQK+5l/85VvvfFT2RuoUApSWJJknvfZakYozmLLpR+uca/+KKSwQB27FnGSKTCUCeHj4611b387msbjh8zrJ6Z5/ZbletOX8HRuBeqFhoPHpSSTbOf0+lC4mY/fxY9T6EFUUtIZdbcaDz7Nk8wnI4IjaX2CWnX0RgpU4HxHvfkK3GXnE/5DV+Nn1ugnxI7ZhO0mCNJANNQqpKKAb6aQ8s+AQGxNHaRjapE3DznPP+5zD/uIrSIWdRscS8XPPdbWasv5Y/+pOGjN/bw9KikZGkhEGWCcS1zw02qYgOnitpVAoaAIxmDrQyHDszxXf/uEg4ccPz7Fxxk154KYwxLS8KP/sQB/u0L99MvhJUplP2CeWvRsIPGF/jg8SniETQ5YjScGQs+OObOW2Rj6TIe4DzGuohOZznWT7bbaxMmTUhGkUZIp0+iKWSLd4mfdyb4R4zPiWBg79693H777Rw5coQ3vvGNXHDBBZ/Bd5cMbOv+S52OTUGu8oorwFUYsdne1kJrTHbKE0uygaZvaK3Jr5UsmYsFNYnWhG2aGBFsHZBB2SnhJcS6LC28sERjS7QUlq96CmbXITQYvAghFPzmbxzhlb95Nf/5P62yeWZIHXKyHYFpXfCKV97H6193BpUBqxuGGARCS4wgNmsbWJdFiKw0lALGRdQqUkT2LAd6RQKEsoK3v7PgP/3HCZvrJRvrPXxqKAhoAaMYUBJxska89S5sEowMaFKLyISkLRIF5xtSbFiIgdU//CPCsWO5nAjbSHuPgp/yoV/4RUbveT9RfYYnR0h3HubGn/oZZkePIxhsT3CpxsQxRseoUZLV/Lu0h2rCBEHsHBiHWksUqHvzzH/bt7P49KfgXItvlROrBU2dY8H1doHVUZ/3vm/Ea95wjKQDhv3E+gagkWc+y/Id334lziriWqJEpEgMhjHPqYXKCU46KunWcsIhDEDilvYiwpYeOygFvi15yU88yNXvmPATP3IDv/3bNyLiKNWSksFf8FR+4fdmXDM5xASHNQZ1jrRyrHOV1kx9FWVy4w3c/rKf5PgfvY47X/Jf0I1xFxA6jIdqNAZa1BjcJGJSQNRjxtlBTtbHCC2GKVYUygLcEOkbqMdU9QySwVhlxzd+E/2v/WqQHq1W6Fd/Gf7LnoW3Jcn2ch+5MRR6gPddPcT0DIHMUIkzj6XgoSN9vu/fr3Dk+BBT5kNYxaAIVd/ywz/6BXzT155D5RLWBpQ5tKPrFlhKp1SFZpXDEirbsugSRbHG8h5HUQgXH1rAuBZjG0h7eP0fKdMNcGaRd77vOC97MxyPV6G2YhJmEKcYqzhXMY1zzMIcaxecS3XpuaAFVsjUOTMDSZTtGYxriUmzqVftMVpkx7/QIL0BiEOHe0mmhwAmTEjWIP2EL0q8H+CXlqiHFgkWp5aiZ6me/x3Mv/BfITvmGJd70DiX24FOCQa8KZBmnr98s+WmWxZQV/D0Z5RcetEEawRlwnO+peH7//25uRVoGmIWViaZkDUwRCitYI1lzoA1FsMCzih9GzKewDhagbIvzFcJtjwFjVC3MEuGNg5pY2KjNsS6JEQlOsdqqhjpEjKdAdm5MW+sQGxQA4No0NW1jA3q1vTnKQWf/njUyhE/fFxxxRVsbm5SFAUHDhzga7/2az9zb76FrlXZpsBnI5WMZM+wQkcpWVs9WsMU6AtU5IWbio6aVlQkV9Kagqrr16mkjDieTGG0iZmOoNfrLDxTFukwQt2ztFVFMsuwe0iz7xxEeoSQiBHGI4dvS0JboZpoW/BN6iSNLbE9h9PHYlem9l3feoQxS6ANRTHpSvG5ZJvB5pluZ2xiOFSq0gMNZWWIwaFqKbTPiRMlMN1iKmGMMqsDm06w0iMygKiUtkSbhFVDEQSZtRg/QCKkSUNocha0BXATBYftAgePbkyoOqEbEUWmNdXmBGYKM0/RrmPaKYRN1mKPk/ZcdtojDKOlIvPpC6TzTQVfWsoUWZsbsPTd34pceQ6iFjeClZUZKS2AFcbtkAfrgpPrFXVTobTUI9AYsK7g0ssc3/x1VyDGY1MPpwXDXouk01TFw5YR8rA97GFk/60X/I3qp2W8toCEPkfOCJO1OVbWWryWVMUuVI8xfcxlHD26k5O6hHe9LETlGty0hvk9VNN12tYjKWFmNYNxTTyzRn9zgouKSQXR5ZL9YHUN3VxHxiVu0mLWT8PugzBuKVKDPXUCRZE0w5gVDFNInrqsKWYrDNdnlDpkY1qy8JVfTvklX0j7iXs4XTkOfNfzcCdHNH9xNasbfYodYINlYUk5c6YhSZbFXl2BtbVFFIckQ1vvxFYnqEoDJFQCmIgphG973kV5zZoRQoOqBS0xYggJhEjpMvDM2ogRj3URU57BFYqzhksvr7B2Hdcv2L2gPHSsYMfOCfv3D5gJHNH93OcX0bCD2gkaZ9jVE0jrqRGmrmBj30XI3grlY1j19FMCLcFDihHjx7j1Y5SNp9ULOJKGHDCBcr2mDAqUmGIRMWX+2DWwXLZYFdQuonYHze6djP0JNHkaoxRVxeIP/Fvm5lsmxRCl6jAZSk3LtHWsTwAZgB9AhPkdE77nhxa5+SNraOjjpOEJT57x3O86QBtGmLDciYdtaU1Yzu56D98Pt9Zqrpj2SkU1UpaWqirYbjqooZ4aRuuJGEvEB+qpxbeRSYhsmAGiFYECUpa2zibSs+59SkQDVZgiszrvteK6132eXfDpjs+JykDTNBw+fJiTJ09y9913s7Ky8hl9/60u2hZN22z1dzt99BQNfa2QYDEqlHRgwwR2BvMhf92jRJPL3IRksS0MfKJIwsaN1/Ox3/9tdDbNZkMoEhPaq9g4dAjmF7Msaipo+9Be+Rj0vAuISYjRYqgw9FHNrQHfKN7ncjpiCb7i3rsLqipirdIbnuaSy1uqSjj33IrzLp7k0jQJxW4/7AaDMYHeIFJWLYuLwnkXVBiTDYpaddx0cyC20LSa6WMhIK5HKnfg3TxNNQcSiIDHYMQhySJNg/hsuuJM59ykuu1dgIKLymAGw1lDFS14Q+uU1rRoOwZbQ+kZX/NRzO2HobGkMOAjo8fwV82/YOovxIjD9lwnzTxBezVIIkkPK47xYEjcu4dJBYQCP0msrU0QI4gmxlrwYH8HD/ghaubZuXODx1wScC5TR61R/v/s/XmcZ8dV342/T1Xd5bv13tOzL9JoX73Ilm284A1jYxuMgbAvAcISIEAC4efHIQ6beTABDCEEyBMCIYSHhxgMTgxe8QbebUmWLMnaZ0az9PT6Xe5SVef3R317NDK2jI1lyY6OXq3umW9P9731rVt16pzPYt0QsRMIGRJg0A8c3J/60p97CPXYMa49pyaTxE+nIGRK3VaQtbS9jFYyRnYvm7suZW1uFrVJwc3v3oNaizEKGshjpGxawtaQvG0Q9bRS470SpGLtLW9g+HcfopyUDD/yQTZ+/w/BG0JV4fJIWF9DNSdEN50e82AshQTiR28gHD/J/fv2cPrIxbBnH5M8R2e6HLviEO2BCxgfvog3H8/5rV+9EethpDWzuyJNq7TVLn7iX2zzrd94nL955yb7D3pcvk70iYKZ5UrUgLEBlRYxHlcGcBNgcm7L2sndrYXF5W3m5xusKPv3Ow4caekPoCgmFAVYIxy8wNPtbuKyCd3+BEGZW9xkcVfLSIWJGfD2j1/Ax+/MoPW0Z1ZZ/53fwFVNEtdstiitR8gI4qjppVaBZEgI9KKlfO9HGP3Kr2E2xnzIHOXP9VLOVIr/23cyUyQ+S5Z1kSwlA1YMtqphUqC+QZziF/ZR7z8KPcP2E4+gl19HM7NMZVfItUe3leQrYSo2xi0/8mMf4zW/tMbWltIpxxw5OMLKBv3ZmvnZQKZD5gZb7D8Eea/FmojEAqNTt0o1n7E1L9MDTaejIC1FAUWRncsXfIhsrQu/+ss3MBxFRDImY0PTBrYnkVGwtGIBi5HENkmgYEkYFBUwkcK2SJ0SBLVMQbiP4QY+2/iSqAy8733vY2Njg5/4iZ/g5ptv5g//8A/5yZ/8yb/3fTJVBtxxPFRNYK4d+eId8yMRSQp603/zD5M3/iTF7GnTKmrSwTc6Lf+GQNckffJgIKsDZQBiJBOLbaETQJpkBIMVpPJU99zNJIywxSyx20PtVKV7bpY9r/n3nP3r/407vZp+blFz2b/8Z2i5zE13OVQzQlCqKiOoQ7Wl9Ya2jagGRCLQEEJgdlbZ2vZ85UuEn/+5i+h2I8953h6ue9xuBnMROA/MGJNegs0CRdmQ54bHXd3jyU/JeNsbJwgOIxl1U5OZjMZHVKGta8q5BWZXjqKzmyz2L0HfO6RVobIWjYlHHEKdHurpgdnEJPO80+ffATZJDGSxRtSDtbSxIq820e1NijCGsyfx9xxjZmNIu12jay13bu7nZp7CS/3diD1LzPqEZsjYWXCz0GYUsUCi4L1BdAYXM6pK2JxEqjqpAQZNLn9Hnv91jO49S3hb4Fu/c4EXfbnhXS+5H+ICEj3OphKnRMEZuO66vVx+0Tfgsunceagppg8ufErC4iEKbd1ALqyORhgMVZtRB2gzaE1EvaekwZYD9vz8z7H2gb+l/a1XMysj6rke9n5LjFMCZWYwUy2CIB7RCd3N4xTlXqrQYsYVo3vvxw5HdNrThNs+DnjaOCHOdwmjMYQuMQxo2EtbH0TJyYPDTWrCgf3s/43XsH//EfSCw3g1FHv28Mxf/QXcwkGoYf6fLPKX/2sEYxjFNjEx1CA6yx0fj9yuFSbz/NS/Lfiff9bwd++IzC0UdHoWIcncGhxBDUhDkVtgZpqsW8x0EK31fN8PX44LBovn2c+d5QnXvpiz9zcQIt1SMUSe9NSS665f5n0f2WQwKBEi/+SbL+ID72sIPkOZ5bbN3dxx7DZ2Nw69fxW9+UakvRxMjqtqytaicRlvMhqbEaXGyJgYWlwdKY+dpL73Xpz0uaU8yEeLS3mJPU65eQc+G4KM8caz4YS5IsNWsDZpWZy0iGvYyEZc+vwXcMHKMk13nit+5pcoZhcRLLkYNCSJ9EmlZEXETwx/88acMCnxoeE5z/B893cs8K9ecYZOmfOVX3GU3zx0F8970RzPfc4iRiagEGNATIToES3BPvTaqDG1swYDQ5Y3ZHmgLJIPA0YwUnDqhOWOWwRrchTPeFtoW6UaR2xUnAZimDKKsKiUiHSIaoixoc3Azc0SmzpZZIuQE9BPoTvw2cjUn/+9X3B5+0coviQqA91uFxFhPB5TVRVlWXL//ffTNEksQ1WJMbK9vc19993H1tYWMUbquubuu+9mbW3tQW94COHcvwHOff70MT3mT7+MmvTisA5b5DRLM6y7kOyHbSDg8RYmNhClxflIrFtGWYsWgcYobeGJmadRxRhH1zcMNs/i6wpf9GiNS+593S7m6IWY7jy0HqOWTAKyPM9kfpatCD4qMQrDoRBi4h83jaFtQYlEjUTGKC39QYORCQcvHLFrXzLJ6RQtK3vBmApjPeeXs1UBE+kOlMHshIVF6PaSs2KMQlBLjA7xAWPzqfWtYxwDsTtL3YVhL1J0hgQTmRhhR3o5hIq2qtCYkrSdX6vnxhxaA5Wpac2Q1kyIRui0gRt+8Vdo/9db2XN6xMn/3/+NfPBjlBqI7/xr1n7u/2Lt3e8GyamKVapiRBwkY6axK9GsCzag2TYQ6JkMqXLMOOeXf/UG3vieNUIYMddPp9IokdvHOWejolKz70BDvx9wtkJjIBJRCjSWUxXKmm6vYXn3QzsrfLqZBlNwfkxU0BAsW5sNIczyjrdY3vrXa4wOXs6o6OC8xWqkmWkYPeEIv/OxGU6EJzORkjCX0RrBq4fM4hOlBCYTQgz4rQ02f/v/YfTej9BRh3VLuMJiGBJMRWMrsA2t8bQzM9R1BROIbcHZah+/+dv3cPz2McbMMHGOtcMrmGsvxx4+hNgiWVA7h9u9P7W4BgWrs4fY2J5nawShVeb6OUYjvW5Nqy1RekRpWNkT2bc3INowO5eRdy1iSbRRnYomYVI5PnaQ2Em4nWmIeOZmx8zMJWWFTtmye5/Q6QhlXpBZMKJ0usLCUkvmJswMlBiUXl/Is4COlLrOaOjz1r88Rgiz+Nf/L8qTm7hoyWMf9QPuumuGN/zvDcZSIGwiboyYLQgVXkHjmE6+wzjuE/KLEDtIuJDeDFiHloHR4T1of55aDaMLjsC80C4OWL/mMmT/LHFmmZ/71Zv4n+9cw5cl2EBttmizivXW8+M//X7uuD8nCzmunmFzy9DpbnPppVscvUCxdouyMPT7loXFMfsObFEWqaURvaAyQWWYDg9eeMjKwLklUZmdy+n1InnuKTsu/XsNKBm+ttAu0etHJuM1jt0TCUFox4rUqQponUlHLTGAQ71LctNNxdg6qpUD1I2duho2QIuZJio7H5/1c6Z67kD4f0p8SSQDZVly88038yu/8iv82Z/9Gffddx+//uu/ztra2rmTfwiBt73tbbziFa/gve99L03T8Lu/+7v8/u//Pq9+9as5efLkg6oAO0ZIO9WC8yfWgz/SZqpT+ptO7UO9JEcutzjHyvOewpAWmoDXlnaaAJQhoDaSt4I2ntrWwAgTh2Aa1MZ0So4RMxkzqMYwGWMXlqFIPPI2KyBPXHOfFWjIgC4q83i1tLYl0lLXlqrqEENCGldjR1Obc31IUYdi6fUDzill12NMsus16jEyBiQhy9U+cM8A0jI713DkSMV4G5xriLFBJOkOYITYttR1QoP72uKDYaP2aC5UtLz4RbMYUWzuEAmY4HGhxcX4wInYSLIk0eTAh0aCD7i6oteO8M0IHwOyNYa/ehf6jnczqBp6N30Cc/udtBFmNk7An/wRe8+cxuoGTX+LM0VBPVdiTAvWot2GaD2TsoMvIq204AQ/gfe/H26+vaIoJ8zNeUw0oMLt65b71mdQHN0ykueBGGsUAVG8LxEpwST9iRDH00WRh5hbiYoYNVUOdCpaFaeS1MZOqYhRaLcUrwPWTg9473tOc/H/9bPklz6REJVtW3LkK5/NeHGBt992mDv0YiZ5h9hJG6SRHMSiQTFe0UmNs5a4PSJ787uYvP/viOVuNo6+mDp4nB9hFWLlIbRUjYfOLNqOkdEWWRwz0gVe/xHHrbevoTO7We91WPz2b4DZZdqyk4C2RgnGIrFANNKgrI0tw6Zk2Co0E3YtwqC7xTOfYej0GtR4XKYsLUT6ncR/n1/oUXYydhTyQojJvVs8GmNyzoygNKiG9LUCjJBYT4tdFTAmyyKwTVmkcba2YTAAl0OnBCtClgWsmcDEYengpeHW1QGTOEd144fp1o4oCSBsGzjeHuKPj+9mzexG+7P4bJEYp2qAIRKc0sZUBC9CQ04XKKjiAF18HOr7FAd3c/XP/Tzx8MU0RcbBV/wU5tAyB7/8BVz/6p9HlmdpGuHP/1x53wdrHIlpM5RNKmrGHt701sixk0BQrCZ8zzd+c58f/7ELmekqZeFxRWKvOmsocg8YondghWgCnpDAjvKZ5i0gSow1MzOW/sCTl575uZwiN7hMiDFjvG2oqpKFpcjewwNOnYooGZORJcSCFocnQ036/Xlski3zaEJcXWdoFqh7FzPx89PkoyVG8PGBtTuEcO5Q+A/92DHB+1yTiS/G+JJoE1x22WW88pWvZDgc4r1nMBiwsrKCc+5cduec44UvfCGrq6sYYxiPx9x555286lWv4i/+4i9473vfy0tf+lJUlfF4zIkTJzh16hQhBIbD4bnWwqcMCdNydeJ5N5K8AjJV8rbF93LKM1s0b/sw4csvJ3Yywg13IlrBxhaVETIrdDZGxD97J3FjjD7xKHE0JuYldROhGpOdup/Wl2RHD0DZS6Vj6/Drq1TtNlvW8a53nWT3FTMc7uZktmY42sZLYDx2jIYd2jYtjMPtgk/cdpbJxJFywi5GPJgJIkpbZYyHYzQaVAQ1NWh3esMtIslPPYQciNhMyTPYWF9DdQEjStCI2GQ/W9c1H/zgJldcnOGbjCa2VChx3FD5yBWXBrYQtE1KZblA3FxnrW5ZnIIx2uGIajgkOIeJEatgGyF/7y2sHFvDnV2nGY0w6xssjYZIvcmkbNHRNjbUWCPQRvoSEJdT2w5r9YWclqtxk5a98TayuksYVdx/wvK+mw5znd9FlUdqu82w8ohfZmvNIGZML4cMQ9SMe1Zhe20WQsBqzXjkMbbCFkOamDMe19Qe6lCzuTWiyAziJ4gxyW3u000tQKVB8ckRT8GroaoEkZayp0zGDaMNT6OOEFq8t4x3zbO9MEvjAsNshhsnu1l4W0us9nC6gJGUdGQOEx3tpKHa2KbeHGF9xIwqvArtqKKsPbN3fYwT7YBbqkM89aO30r39blzTpViL+NUtwsTjbJe4fhfNO/8XhT9N6w5zRvdyssmoFhYYF12a2VmGw5atzoSO+sQMkTyJIIWKOhO2t4ShV4brkTacpTc4xKB/kiNHemTv9kyMQWVMkbdkpsKYGWZmoG4mtE3NcLQBkkxybLaODV0IPTAezDZoAbEH0uJtxNURG1uatgEcdQXiVmmahskoEmlomoQrcEYQGdI0KSHe2jC4EKiywLbOEltLt2mYqDKxkrj+x26grCvOciln4l68naHRPkfHu8iySKFCfmbrHMq/Kiu2xNPg2JZ9fOjWjKdvBdysp9i3n+HcEk3XEfftJbR9OmrwczOEbbj17ypObiwyDBPGW0OsEaSMuBbcWo2r+2xtjBnNZ1SSrMVnF7bpz2WcOm2JjTKpK7ZjRvBC0wrb2y2FKJN6wqT2bG1BJjU2JuCOfprygKhBNYKbEHRAbxCI2jAzm2Fdgw8FaMbNNwnb2xlzKw2DOUeMYE3GLbfC2nBAXzJGtktDhgljSq1o3/NWfNFneGKdsdnHiWOWXZd06W5PiPmQJnQYN5EQAltbWw862P1DwxhD27Z47x90KPxSji+JZOCWW27hd3/3d5mfn8day1VXXcVLX/pSYowY84CKlrWpTKiqDIdDer0enU6HxcVF7rvvPiBNgvX1dd7znvewvr7O6uoqa2trn2EiJEqd7DgWGoPXiFGlUMXEHsWxY9z8G7/A3qt+jarMWHvtr2FtxcGrL+HU0b3s23Ut1W23sfGLv8T4zhPIj38Hdm6eqGmjsNuR/K51xiND90lPgTwh+htV6uGQ8vpnsbhW832v/ADP/aaDfOdLlnDRMT7bok3LtjjM0NDGbQwFZ86UfOzjZwl+wL59noWlLW7+aCCaMUa71JuWjbUxYx0BRfK114bUqE/YAUGJ2uHLn7HE/MwcbWXZfr5wbLVCRJkdDPnKlxve8c5Njt034I//x928+PkzVHUkhIoz1JgTkTOzjtmZApVRYlHYSCs17fpZxpvHWERpwybV+jajs1tURYWimNihv1lz7H+9gSvODqk2txitrsPWCO8iIdS040CpAXPmXiSYqYiKQWNOGwomHOWNW7u5Y3KM7zYObQLVcMy7bhvyR++aYY88gZwPs7Vxgq1qD80osna/oTOI9LojjORYEc6cgfFWjtWGybBlZqnkVf9uBd9aLj4CG2tbSIyMR2NG22PGAL5CLOhnQj5LIKlBxiS/KpZ+p+THf9Ty3/+r58abR2xNAlVTgzrObjasNg2Db/s2Gt/Dv8ny+jc4/udfrDM7MvQuO4o90SPkAhKpwpit9TPErRPQNjC6Dw2WemuTLHrkne/n4/o0fu9sxhWn38P86jGQLluVp7u6xfroJHOlo7znGKdf8xpmfI2JDTmO9W2lvWSewY/8MLpyiLX1LeoxjGSbqBaHxYWcrA1M8sh4o0s9DKyeCWyMtrn08sD3/HCPq682fPTGhrvv2eDrv32JmVlHZmBpeY3nvTCnnZxka2MD0VlqvwnBYVyNxBa0BvEgFWiV/kwgmhYbpm594wpVg8l6fN/3XYqxNRtnKhoTqMKIwAZf8cL9PPGanKuvNLzrXRXDTVAsJuSYrMa029C2OANNrGnUsPbhd+PaCzDFAu3Rx/GmO+/kTNvhO7dz5swqGsEOW6KxeFFaibTO04TITXqAf/ebJ/l3z17lkovH9OI2xbd+DfF51zDuzTFa3aDjFaxnUhf8wq8MObtW0VSwtrFNYSZMck9dDRluAl5ZPztmbaGgRUA8qjX3n10lujn+2Q/uAn+K9eE83te0Y8PG2RFiRtTVOk0VGK6NMBoRItF8+jVR1IJGvF2n8oaeGxLbDvMHDT/+Lx1/9Aer3HbXPB+8saWpLQec0uu3aHSo6fH2dzScXS2Zn99L57t/BC0/xtof/A6dEyf4+G/8Jhev7GZ1NObYhV/D//jQNt9VzTJzeoO69BAalJatrS16vd7nvJHHGGnb9v+IRAC+RJKBra0tnvWsZ/Gc5zwHay3OOUTk3Oa/Ux1o25YQAt57+v0+4/GYjY0N7r77bg4fPgykCbBv3z6+4zu+g5MnT/LHf/zHHDp06HOfDDGy0dtFVz0xbrK0/xC+zMnCCDs8gzSH0X27KL78+TT33kdvtIarzhJLS9afpW2Tv3zMSroVTKoh7vLDtHNd2uMwmemz7+JL6A7mmWmVyS/fRFnuYeXiRXRb6N9WYfU0kzYjq7v4oGQmIyJ0ejmDmQkH9geuf3rFrTcps/1Anq+xOOc4sOcoedYiklTvPlUoyoGDy0hMNKJK4E9fN8HpFrP9Tb77exfZWDvL8fv2YVlmz94SU3pEFznVHmBuWNM/uMAVjxvwtyJYX9Ji8AZ6mzVL6y2tVbSr9Fb2sLDvYEL8ExHfR8otzkqLoBSDPnMH9sNglnuKpFjXjw7amqweTfOYHMRjjCYOfWhYY4U7yBi6RUKvT/fAHubaAWu6yHpY4qLQpT+4FO3NocWQ8cgxO99n11xGq57BwDIaJTXFQrdZWgpccuksRy9aAFXyTJI2gMCk3uTg/oPnWk87yeo/NHYWpgPA5RfDrTdU3HBzxmiygI+WqIHerGXvkRXcwa/GbSrGniL6jI3xLoxkdI88n+0zr2dwcDfSySlzw659exi6nImW2BqyaJlVi/ERGwo2ZZkzeohsO5KsiypCmTNz+CgyKGBpni6Wua0x0QqOFheVQgqWLr+E5WsP0UrOAhlOI9u+ZmOcsdxTsmyKs1FFmk0k9Ig4jN3FBUcyXvL1l6ERbviA8jdv2+aH/8UBlgbK3pWWCw5P+JqvWWHv8pjjOGYX5uj2+8l6+ZNYb/+QsQXhwouvx1kLbbIOnlusKYqcl33dPPOFxecNf/DHY7ZPByrJiTHDaEtWlPhG0n3YAd47MlVC7KL5CrMXX8WZO4esmxlmvKE/3+cWezELZkIup8mzDpnMM7A5mRRsuF2Mq2ux+QL7llbo52P4ustobcTFDhWR7eOeXs8Sl5TKzSPxbuY7DQcOrSDZhFpKsmDprDVgV5mfW2JpWbB2jLPbLMy27D90KTbAP7tqFrUjts7O0u1+hPm5Pvv3LWPzGYbbju31lgP79qfBkpAoGZ8ukhgIwc4xnnRZ6t/D3GDA1U+Y4YpLDO/46+Pcfs8CVS1YlKVdQ3o9j6FHiCV1XUJwVIP92JdcTVz+Su753x/lqvvezspmTaepyETgmq/kjhvvZLIwYN+BFdrSIBoJYULwnoMHD6bL/SzX7x3MWFmWn9W/+2KOLwnMwJEjR3jd617HK17xCn76p3/67xkVGWPw3vP2t7+dt771rbzuda/jE5/4BI9//ON5zWtew5kzZ3jyk5+MMeYcm+B8xsE/KhQcASOerA6YUcSJQTTABz7M9m//Lp3WE7pdau+n2kVKOxpjmkivBacB9Zuo1MTRNjLTZbK0wNmZDvWRCwl5jtpIG4VITjQFVWt485uO86u/cjfed6hGwvZqhzDp4zJDXWVsbTRcdNk6F19+hsOHHfMzDbkYLrtqk6OXODInEKYOep82BDEWYwVsi3MBo4ZOp+bAhcfZtei4YKVh4IbEpqGuoc4cn1jLeNNH5lmbLFBQMTunWEpG9Flb3os/fIj2plvpHt/EqCNW0wxdp2I8mmiaYuKUs8y5DRcEDRGpG7JIUmvc3oYsT97xgJckuduxHss2x9qcE7ufSnzc0zGdedoNQ4WjNlBUXf6/3znF//dXZ9lWYbhumXEZ+/evUpbrzM3CeHOTLF/n8ms3WFmZ4jCEc/7vZnqKOn9+fS4J5rl/o8kmu79rCyTjpg8XnD2jGDFJO8Jo8opQg7eWyfS9GoYZ/vBdkQ+ODxDm9jFeXKLwwKRh++Mn8LWlXO2QnbyX8b99NdmowVshdsCoghdGcwuEKPTHFaiQhRIbO9TdDEHJgxJw1NLlt39ri2P3emLwYNL8Rg133er5F//sb1k7FYkxgb+axnHDjaucPgs/+IP38e53KJ2OwVlwDopC6ZQVncSsZXYG+l1L6QzRd1Kze2dJO6cN/tmNrWrE2mlJ2YBIoF/2ce0MEpM3ghgoishWE5OrozE0OKh6ULXU3RKf70LUEWJkqCVDP4e1A7pxkW5bcN9v/nu2br2LN0yewM16IRqENl+itvOYCERHkXdwtuBnfvIEf/Lf7kb9ACgR0wUDq2cN3/VtN/Lbv3UT3hnyfo1ogTUWjIFYIFgQCN4AnrauqSrod+HKqzfZszvDaBK+cgYcBXkGF18RmFtyGEcyx9LsnL5IGtvAQyIIZcr0oYOzlswFog+IGpyDLBeIAWNq9h9Y48jFZ7n4oowi38Zg2dpQLDCe9Hjt79X80CtX+ZN7jnK/Pczs0ON/9t/T+fhtxLbGRGj8CJURNjYoSZpbHqJy8Q+ZC5/rM/rFGl8SlYGbb76ZL/uyL+MlL3kJWZaRZdmDXldVsizjmc98Jtdffz3WWvI85wlPeAIve9nLklFQnhNCOJcQfN6AIwJYJVrI2oBMEm9ejWewvglWIICKw0aIPoCBZjyGGJJFrALNOImkeIudmSN/6UvYxnPp9/84W6FDNxpGY8OkLTizqlSNcPe9gdtu7dA0PWIQ6nFEFFZ2t2xulayfKfjqlzm+/duOkFnh2M23c//pIT/8b57Ek59coDFO5UEeOhRFoyAmgArBjzl4cJv/9idPYn7G8hM/9kRu/Ni9rK46ThyDST2gYZHj7SE2szN0nSUrDRQdzpYz7PrBryVbyPA/+kqyXQNsVKzWRGlI1GVHFIs40gomJEAh7GQDKTMY19AGjCQnCHfpUep7TpBtbdKSFBMkBDKUY7LE+Ku+kyf9yPXQ69JsKiNd4M7yIAezk9z+CWXUidRtl/W1Dhcc6fM933+YD75/g+1xxFpl775T/N4fXsmhgyQFPplejn5qqtI/pvwo055vd7YlKGgzjyJE9TSTPhvrytI8aIBAS5MsCWmj48OrXa5YOITfdQFL3/EtbL7/w8xbRSYb4NcwWzWFGRJu+ggGoe6U7N67Qu8OQ2MgPPXLKd76BuLWGqydwbQRl/WQogsyImiXbbOED12OHxdWV8fsP2qJsSWSY9SwdirwgXdlnDnZYnPHYNFNbbRzfBxw7z0l1s7T7U1nYIAsG9LtbSU6pgrdXkWRb5DbPVixhABRdpg/O+ofnx1j41x/2EzfOFH6hWJ1Azd9T0FxkrO10UGsEBsFddhJxLY14cAMe5eexE3vuB/xhi0xbEShLoVG+ogo5e03Uq9ex3vMlVyW3cWV4f3U3YKmNsklUz1IpG0DH/1Qh49eexL9NkkujapYiRw7Efj4JxbYd3SL9aHBlC0Eg4nTe44OsS1iIMbUXtpctQy3lF0rwu/+3uPZdwByAcGDBoSMModX/btnk5UgZuqnIUW6JkhtQvEkndVPHwlPYLAIzq0zmZwGFrGOJO5kDeiQV//fSzzjK/exvSW8/r/fzn339ji7ZlFRYih47/vhzntKLh9fywvcTeyJt1Peex9Ff47MWCwRPbtKOHkCt7CL2J1DMecAt/+Y+D8pGfiSqAwsLi5y44038r73vY/3v//93HHHHQ96fecNzfOcwWBAt9vFOYe1lm63S1mW50AmO9/7j80Kzy3+qngn+CJZ5hI8JniUFidgg6feGGI0I68C1nsikeAbIoF2uqHIOGCjoTBdbGeB5tAlnDl4JTfcH/mn3/MOjp9o2Jwokxr+5A9X+c+/+QnqumB7UuBjAXgG/VV6vRMs7N5gY0N5zzscSzN9FmaFmT5cePEaB/ZvsWvXNkUmWKvoOde8TxfJWS9tegUGy9Iuz4EjZ5mdjRQ5zKwYZLDFXccs//x7NrjxA31CzPF0qaKQ5w7NhZhnTLo5fv8iYWWJ6JQ46xjOL9IGRU1FcIr6gImJ3x9lh+JkiPrARmAQaFoIIWmZe6XpdZiUOd46tCyTeIl2idrlrF3i7KH9hL0lQ29501+d4XS1hz8bP4V3yrUQc8YbBm0qJlEImWNx3jAzaJmZiWR5oNefsO+Q0CnTmcjaB8+lHUTzP+bUcX6SakTIMkO/HymKAGoQKXjTXyu/8LO3M6zTQo7W0ILVCCYyEcfEzqCdBca79zLJe2nfky06cYipzzAGXGwhVjShpddZIpPAxAT87AIalfre2zn5O/+BWG2m6lDeQxAmbp5bWCQwSx0Vj0HIgIwQImIaxGZ47XHfiZYf/8G/4xO3rBMRoqzRmx0BfZAi0VQVnCiLCxP27B3jpMEILCyPOHDkLHZ6pBHrpyfWpOcwzRw/pzFOA5xotBcddVx0yWmc0ynFDaotuOvWkjBRHILFUIzXYbzBeM7guw6rBUZLJiawLutM9g3YmptjA0MWGyRE1twcG6YgYHBuSOEmTLTEm0gMAdTiQ0bjC7wNU3fQhhgCo3Fkq1buOrbID/3AHZw+GXGSU7gyPRIRAiMiNW2E4TDnP732Xo7d2dLtKrt2K/2eYE1SJhRJIml5DvNLQqcLIhEjBtSeo/eec2N7yIhEqZNfALDvUMvBw8ksKwKdviMGT7es2Ls/MDuILM3DhRedZjDwbI2UIIHYtDSbDvHzbMhuznbnEkNAPdhEpbYq9O86yX3/5pc4+YY34sSQ6YPX8sfiM8eXRGVgcXGRxz3ucayvr6OqdDqdB71+Pn7g08UnT5jzT22f7cL9oBOfCI2xBOuIRKBFJhWxmqBEaoRh41np9OgGMKNRUgprGqIEGgvRC9lWiwZLU3awWY99113J3kuv4/Xv2+BNbzSs/VTEFhOq2nDq3h63fHSVqy6fY9xOiKpktuWH/vke1jfv46a7VunO7qEelZTGpUkgLd/0LU+kmng2h6dTliiKOI/saOZ/qnsl0SvTtxuMKE/7siWuvPRpZNkGVhcJGWgZqNoBN97UoxELtAQaGjFQ5AQLuRU6xmAYENlP28zjijnW4yLabKGVR7TGVFWiwuUF4psdnljabKfDbhSsKoJFY0CtYVxkTPIC0++w/LjH4f6mpaXEyyxVZ5FDz76IcW4YDYWb73BMZIkPao+j9gS7aai3AjIWRhPFZtnUU0Bh1tPrKp1cpvx0kHMn0wfmzfnJ5kPNvYeK879XgNLBBRdWLC5b3vbmFtWSs2cX+dhNY4YTQ1tFJChZmyohQcaIVBhpyYo+tulCyMEb1OfUfpFMtpmYnL5EjNYQCobFYQIWnxcEG4liMHWFvP8d6NVXYbIBweYEYEv73KW7iSEHVydVwlCAmz4XxtPGQJCK+88E/ubNju/5wcjKQSh6Z/n2f3qU3/wNJS+EvEiVASvCV3/1RTz7eRcmvQt1PPt5F/H4p15I0WkRY1GpUSmnYyMPfP4s94K0VmhKLtXywhcu8Yxnvoiio4kuq0o1nuArgwUyDQg5ud/GVkPO2gVGkqE4kByvhqHUdB5/KVduXsJbfuf/BddFo0kqonhEHca1SIiMtE+TLVAHQxMjJo/4mPxMvNYExjj6+Kah1Ql3H+9z5qMlu1dKdi0EOuX0VK6gmtGEBBiMMse9dwv3HxM6PShcm8zRpo6QiMHane26xjk3ncE2UfwlsiPjqOoSGPchsESCT9RaCl75qq/E5cmREgPdfo41hgsvLDlypE+uI2xe8trfeCrf9q0nOfHOItF5vce2kVI9tcDSVVdRv/cvsaI0NhIkQ7Wgd/8aM3e+h+HeXfByTbLJD/HsfDbxf0oy8SWRDCwvL9Pr9VhdXeVFL3oRa2trj/QlPSiiycnIseqBCe19G9i7jhEjDLsFk0GPfKYkM8BwSIGiW6MkACNKRDGnTyPiqXoGkxvibBfpGyJD1HdRYPNsQz1JfuoKtEFpG4OqkGUtR492OHl2ws331azsrVm7/yz93q7UhsDQ6ysuC2wNSQ88wtR/8SHW06QKpgZEU1shz4SVvTs+4xFnDMZkSXgHwUpqk/gYmZBhuy4pkrmIi0Ls7ONjHzU03Sezp7/E3w1X2CWrrPgObK5y33/8Pdx2zcKzn47f2krWuEDUxDPf2YKNQrRC1umyUVXExXkm95yBokSX5hFzhjo3jKqAzwfU1hAnljf8+TZ3nyySsBMDmlgSfWRjM6ce58RaKSSjyIWi01L2Ar1epHT9JKQTBMx04fwsy9SfbXRypVOMWVioQSIxlHjpc/sdOf/zdTUn72sYbQ0wEbxxYCyuFXJa8sKRV0LeelDlrD/MR+OLucDdw6SpmeNj5OIZ6xx/eazLdnRQ5/hexvEiZ88QBj5wWoW202Wz4ygMBNthGOeIWNqm5E1vbDl00DJ/MJ+ORs76ZkurwmgieL+YTp4BTKzZt7elLCJ5ESkKh0wFvTodQ95VVJPVdtEpWO4LlpjK41bQ5KU4jcjUKeSzHlclbaSI0O1ArxtI1lhpyez2Wlo2gB6KEpiDaGnqyC2rl3DjGccEqGOLYYaWRd76fsd9d1kyOjSN0A2OwmvCYmiHmBWM63lOrmfcZGcJWDQaPC3b247X/8mQ576woDNnScIiAdGMzY0B9STn7OnAvoUNRJI7pLGGre2Sv/jjE1xz3QoeS9Aup08reUcxxiIaUNGUuDxw5yAufZrKqkMEaRIDYTqmD1UfkKlDACo4q8wvTdcTScKqYiwhWoo8UBaK8W4q9WzB3YfoPI6MtvVsNpEmNhjbobzwANt/G+hNRYgmxjLRjEk1YdYpfrKFxNSufCw+u/iSaBN8+MMf5syZMxw7dgxrLW9+85sf6Ut6UIg4Ms2wMUKoqT5yI+GeE0RxlFdcy+Xf+s2Y+VlqY6jrGnCEYQ1txGo62chkEzUtvgfkkcpGhi4mFziJqImMx00C6aAYKzRtcggDQ4gVedZQuIgGQ39mwoUXWa64cgASEbWpTMh4upu2SdFLi4d+sDSVEKEF2QTxOGFaOC0RqclDxIUC0RaVs7i8Ji8zKukwpksvVyxCbT1beUbozPKb7zjOx7/qB4gv/z7etL7CfRxEh5b1j9zA5m//J9rf+g2Gv/QLrP/hHyIxic9EjdON4IHrbUXxnQ75rl0sPOvpNC5jbCzkGc5YalvjTUsVwCJUpxte/dMf4sTpDKWlCMrspIPWHc6Mc7a9IKGlpMAJFGVkdl7pdoXSDZKrmgqpffLwipWIQKeosLGll3s0jhGrRK25/7Twk/9ylV/85W22tmcxWLwImC5GCwyQdRzBKsG2aBm4Mxzk13gBf2Kfy7vDdYwpUBxrccBfr5YMGeD8ALtrltkf+D58PovtzdLEgrh8gN3f9k+YzBV4iSQjC2U07PLaX76RGz50P0TFChAtZ04rk6pgexSpQ0UUjzaQ6YAyNxjx9LolztlpMpB635gWNAliiYBB0ZjmYPDwAFYgPHCS/dxGFzCItFhbp2csyjkxnRe99ABLexuCEdQJ3s1T64CxON55+17efWvGxHi8NeyavxjjV/jFX2t441tq8khyqAwWFwRRS9QOE7vASPaxNSr42GSZIXPkHcFJzn13d/m5f3M3x++rgB5GDNooEnpsbXZo2patTU9RVHjdnro3wrH74JdffS833NAQp/onp8625B0/FRpLm/6OYGCUaTKlGaibGpYxHc92SoNNcuEPtS4oQvAZQo6RiLVbiGlBwRoYDUcYlDKPZC75HqBKVigv/poLsHh6JoLNOTUyDAOM3Qzzj7+a3r4DEFOyNiSwjXA6jqmKgB1vom39KTtEqVoyFRb6XKfFl3B8USYDO4qCIQSqqmI8HjMzM0MIgbvvvvvvAQgf6RAimimEDkw6uPFpnEyIdCgf9zzaq17IH/xBYLy+j6Ht8nZ5JvewH/EBSKh5ox68JbgZjCmIcYJISA9XrAkaaUm0LysTPJ4qJlFWouJwSBYQM6HvRvSymgsuFJb224SaNgBTRUJJugnmvGLrp7+3lG4IBiQHDFbSAw8Zah2tNRRxGxdHtCZiiki3aBFKzhRLZLZPt1HUwXsGF/E3H1his95H9uQnM7zsKu44fTEmLsBb/4T8LW9h79kz7KpGzH/so3SHq0gEdY4g27QCGg1WLeoiVpNQTX30asy1z0RdS1SlcVC7ikolCZ2IQEj938bPJpU2SRCp9U6XM6ak2bbUNSCGzFmw0Ckr5mcauqXB5jl+WpL4Qh1MXCEY28EVEWtSq0ZMRtsKk9EKvtlD0JJ6ukmKFxTDvfkCoTPLrWv7uTFehWeWVe1x0h7lpL2Ak2aFaCxqLVXeI4Y5WlV81mBmDrPy8n+KWT5I7M8zckt88JbDxIu+Flm8EDGRmbBJxhjxhkm7h0o7ydZ7KgZz8tSYtl7ine/qM4lz/M3b4b77Ao16stxS5A259fQKg6hNp0wTcdbgTBcRcDbpLog4MEmsxmmJIwll/WOXNxE5ryaWITZJgBvNeNITOxy9uAHjMcaxHbvcVM3SmFka9uOZxWjghLuMW+tlRC1NM8dG08FrgQlCVOhGJdDD2xwTDDZYeljW7ICx63BwuWW+r4gGJn6JO++D971lwkSFJoJqTl0rMQREA2VuiF4Zj+Ev3jDhzJmWYbXEvfe2YISmNtxwc47LsilIMp38dwr+6anfYWTIeZtmkgKW6dP+Gae3grUOmaL6hRyHSQ6sHlTvxmb3kznFZYZoatSOiRJZ3mUxbkzR2aJfTAi1otrFe09xyV78wUtA+5xor+adH8uYaIk0fWRtjXjD+2g/8TGin0q/C+jUqj3o1LNAH0gZH4sH4osyGYAH+vh/+7d/S1mWbG1tccstt/AXf/EXvPjFL36kL+/BIen0lXQyHVJ7rAYMNWHOccv9Q376149zy/FFTnCIP2mfzK1hPxIVDRVWFSOBQMT3BpB1sVhcSBoA1nRBLHVQYkzCOi3KxDuEYtrbM0huuO7JR/iWb7yMXTOR3LZgp5rf04RASDx8wSHTYt9nevLTJLJANy0mIskj3jgiBrKGb//mPbzipwq+9ltyjFOsiWQzsyw86WroFOROkMGA7Mtezmv+oOX+E/NM6pK7TnlWR/sZT3qc+YPfZvs3fpvZSaBoA3J6E9e2ZD6SqWDrBtema/Im4jXDMo978tPIfuh74eKjzF9/PbkfYCclAUXynDJfJGjEawIkegQnFfNzhqDC3qdewmaZ40dCM1YUi80EsfDSly/x7Of2KDsTXBEITolmCARU3cO+4IhJpzBMwIiDOPWS3GmVR0ATQoMQMR4aUWauvpruyjLvvRnee3oBH7q0Qel6cE3AiiNGwfe6LLz4JXgZoGIZS0UUQXb38M+6mEaEzbbLr//P+3j/B88iZgENUqxdiQAAha9JREFUFR3X0MvS6bGVguhMOuGrASynzm4Qmg5v+WtDiLv4lV8+xvvft4mKxWUWMRO6HUPpUklabEoGRDKMlFPti4CV5LWRLO0EE/N03+eSgc+xTzz9ESIufZhE0TMGbLT0S5jpaaIJu4h3lg8ODUNdJExba04y7gv7+ei64rXGEJL/hVhEJW3a4uk+/Wno7hmstjQywU9bHbUa+nkkMx7JGoK1vPXt9/Jrr/4wwwm0qsnhVElsBjF0O0pTe44da3nlKz/C2lpNpYHV1QkaDPicj30skDs3fd538FAy/c/A+Ripc8OX6IUpIQBQHqqVngDZ03VAEsUQNcl2XOB7vu+JvPwbumR2WsGxAUzS47AGVLb4tu8suPjIBKtANLjYQKHUZY7Q5ROjvfzvDwQCc/Q7K7hRRbz9bk7+7zdgfUR8m6oXYXqAJEmY81hl4FPGF20ysIOqHo/HHD9+nOc85zn87M/+LN/+7d9Ot9v9zD/gCxbCyO+jZRmNI5AWM7TkTYGRGroTIoaRX+BM2MUwHOY4R4nbJWY0JDftlIoItVNGC/P4fI7h0GKjJUZLYI4zpx2nTzrqkEqOa2szDIcOVTe1KTZsrG9x+dUrPOv5eynLQG48TuK5nuynv4OHeG2H7bADJhLhHOgYwRDpUPOyl+zip35qmR/4gQLLmKCOajBLdmQZ6TbErmPSG7Drygs41c5wtumzugWVN2zLPJvtPmaGGTMTwWiW3AyjgcZg2hobW6TNMQGk3sKp5ywHuCW/msmTHod5/nM4ZkrkqivxXUGDJY+WYA3jvEM0jsbDJJnWUZYtF12YTtFLe3oE49ieRCZ1BE2AVGPgGc+b53FPyhkMxnR77fTeDVD8I0rU//AwUqGskdlECxMxyV1O2unme/6blYbMG+HAJbPkMzn3rTvq2EUVfEhVrGy6j7aZYWNhluJbX85Wfw7FUdNHgqFhQHjySwhqGbYFq2EXYzVkpsWawCQqvdkSJSdK8noIIgyHyuppZWu7AuPO9apb3ycEg2iP3FmKrGLQi7jcTvv30w71ubZUan89nDEthp+Lc+xQgSwT+t2MwnlceZoiV0Yss5XtYaIZxgi1ZFSmoMKke5UH/3Q79pzNLO2XfwWre6/ibr2U426Zswi5ljSmwBeW4BxNdMRgOH3KMKpzYsvUFyVMxyUjqpKXHpVA3RjaeobxKBJixub6BKJFxFNPNrF2kjbznURg5znm/GrI5ytSE0IJ00OH8Ixn7eUZz+pQ5iNslGSPHPtkGDICuT3Ji17aMpjfIEoNMbVMJUKeTQi6jrd9RnGBynbJVpbxGLLG0Nmq8WfPMvAK3iNRU3V1+t49Fp86vigBhOfTq7z3vPnNb+bjH//4ORT/VVddxcte9rJH+CpTqAp//raW2bsv5NkHW5AWRjVZyMAExA7IG0ElEBF6avEqlB+/lbh8FidmarIS0bygypb58E1b/MZ//xCv/cUX8ca/HjFpZ/mxHzqVbIn9LCrK372rx4ffn4RTDAERz9JCD7E1eWnIck8n10Q3exgXVcFhYgdBcC6w1K94/ONWOX5KGG4bTtYth/cIphAm1rIWLCPp4oxhNHFErdk2Pe5q+zhb0An19OSXcAnqlUhFbSKj3DGXN8jJO+md3ea99hLeXV/O97ujfOQvR/zpH9/Oq67N2TAjYmah2EV5+FrWb0pVAgU2x0nXPs8DvZ6nQRirZRSE9a0MP13RXZb81YvM0rSGQacgM+nER5wmo6bl/EbKwzC4zM9bjhytmJ01RK0SwEsjg7lNRlszhDqHneqOCIrSKDQOGgP3b5cstD2iT46WVg0uGCYIm4sLxEP7eNuHR0zanCwa2tDD6TK/919O8P7/0fIze0tq7XGWvajpIi5iOjl5fzfBR6II1rSoyWgx/Jf/eg833dhQVb0k362JCKoUjCuLzSzzC8oll62xewm6PUOisoUHj6EmTMLDu7qft/tPI8akQWAsFC6wf09LZ+4M1XqXrc0F1pb3EzYHtBtKqx1q26UVS9Hv0A5juhdNFZMQPSGU/PW7au6/7RCn2338XXMEyRfZGmSsHFwm5gJDqEJO0JyTJwaEJh2mNfqpVa9MaaVK0WlpoqfxQuszqsrgG8No02BM4EnX13zk5g0K94Cd8xcqRJQYPWIcTiJ79ozYu/80zl0O0SRwcaYc2l/w9Ge07FrJmF2IiExQKbCxSEqnmQEbaFRo6YKdMO4mM6VOMLS33so7X/VKrvyub8ddQqqWRdBz5bLHcoJPFV+UycBOi0BVsdbyLd/yLTztaU97VPJKFfj4+hzLzWU8NR4HHSM+2fVuO0s362Fbg6hnZCNiPc6PmRmfZXRKOWav5ZC9n1lOkGlyyTt13HDbzX0mY+Hm28DrDPfcWQIWxRGpGQ9nGW07MJs4N6Q32Obyy+ZxNtBqS68jtJvm3Cn+4bt/QaclSRAuvXiB//rfFvne77mBGz+6xMlTWzz58fN0nADK+kYkInjf4psKiAx2Ocabc2yEGWbCOmI8UgC1R3yD8Z5oMvCeuHkau3qK/jhyh7mQd9un8fWjI5y5z3HzB/q0RwJaBMa6zfjgHM/6mev5pe+8GdPk1BOBVtGQURaeLPdEsVQ+SRYMR0oIgNXkbqdg8XSdo5tZxsMGozsnLJ2CrR4+NoEAz3zmxVz35Iv4T791LLEqosFmkRe/bJE3vz5y/P4ppHLaJ0WEIIZxFCoDG7Wj16SNNagSBCIZa1JgXvLV7P+KF3HHGys0FmSSvDdaE/jQrdt8+GNdwkqOj45t28OLJToodu9i+bJrMB8MiEm/OEalaoX3f1C47dYO3YVA5qBp04AFFVpVmrjG7gMtv/W7T6B00OmmixdJAjYPzNYvVCLwwO9JtMhpedwouR3y+Cc7fuLHruV7v/8U2yd6HP6Wr2PmXfuRvzG0FLSa0xDp9oThkHPlBVXBti290OETt/RpJ9dwG5ew2lxILTWDaw5xzTP38+E3ryPWUfuc0DhOn+qQCcRGiSESaVPpG3BO6c4E2iicOhVovWNrC1QL1s+UzC9l/MK/7/Ov//UpOpl+ATfEB94zYwVJihI891l7efaT92Jcg2iZ8lWFJz5hhj/4719OpydkWRI3EpMR6THcdGi+mJgjMamdKoaxpjmS+ZZ89ST+3nsoq29ENSA7QhQkoa5UC3m4588XX3zRtgkg8cqvuuoqLrnkknMiQql8++i5LRXYKtLimuhAFTBhfc8M4+uO0u4ZUIkwYcCppYs5c+QSmmwO3xo+cHPgP4+ezp0XP5tRL8caT8dOOHXSMGr7NAJ5p4OKQ0mfo5pELYxu2kvM2LtnyFOeCjP9DBNMEuWwYJxirOXhXBZUkpVqsBXYFmOUXYueHopMClbvm3BgRek6yKSl2kwLXWg9eANRmV801HMznD58Jeu9jNZ6Yq8LopjxENdA6C/T/8htfOTH/g3VvccImmHqJe7xV/Lzfx6466ywrh3yfZdjrno2TW+O1dyytWLYRshMJEMZjoUQHbkN5GUAEZrKELwjBkHxIC3WRYwBjdtAxeGjDQeObGLMDjRJp2Crh5NaqHRKmJkVioJkz6sRkZrd+1uMHfHg0+3OMihEcXiUurFoK2hQmhBSJSTkrPUWCNdczeqeASqKrScQwRtL6LQEZ8g1x9QevKfViHrLOLPEuUWGhbLvQKDMG5xarBpuu23Mm94auPm2eT74XmFxPiISiVEQmzFpBHGRrIC9+5WlXR6xYETPlbA/+f4f3ogPGr+dPriIIhLZs3eTi67YYO8uIc9rRh1D/viLybolGgIRk8yPsPS7ifkQCYToESx2oyFvPNtDZdsIw6xgW5LK4/ogY6ME17EYhNWTOdXQsbFWMGod0WuqUkzbcuk6J8zORTY24ZdefQtbQ89opCAFH/hQIISahT2wayVQ2PCQPf/Pd4gYjLEY2QErOsrM0J1tMaZBbEBsnGrCNMzNT7AG8qxAVdCoVLHDv/3Zm7k3P8poeQ9Bm0RnDo4m9mljopma6FlqPFlVEU3kAcmBnYboJ3OOHgv4Ik4Gdk7/+/fvZ3l5+RG+moeOKA1RI9nEQhVoY8Xs1381F/yH/8L8c7+asUKTldhnPofHv/bfwN5DaJ5xv9/Fh7Pn0/2xf8fGJVfSqKBV4NgJx6gtqWOk103ArEhEqZJNrzrShG8xKC/6KsOv/PpRFuYFS6IM2TwgxY6878P3WCS4mAAZgYxIWhxpHPWwy8ZqweKKI8vAUTHeUpqx0FR9xpOCamwojeD2rXD0536J6rLLqYzF95eTnPD2Gt3ZRbovejmdu++h8/b3ETe2GdqcRgu27D7edHfJ9rAhmIru857O41/za2zNztE0MzTe0EpktmvRCsYj0GApM8iy5IUQJuDriEbDTN8h0pBlqfRuKMizlu/7saP8i399RaJPnaMXPvynLyMBayfk1iGaxKHEKP2ZCufCg99aBVRSeyWkQ2poItYHNJCEgMiosj5zV13H4Rc8h0m3R2MDhgkey1AyfBQsJZCSCBcSrqX1lkl/ht6Vj6OegeueoBy9tMFoA62wsd6ysdFlUinVBJ7x9BrBJ4CtFyZjh4YZTDRYbXD2gZZQivOrAjtkuIc7IfhUPz8CkR/719fzA//8MpxVxAmzB1fY++SDNJklmrTpGY1YjXSLZNlszRSpqxZ3epOonq1xhVBT+oqAR82Yxli21ZLnFgOsr2fUY2F7M6cOkaiR6BWbmWRVLEqv68hKy3houeeuHsHP0DSW2EZoZrGSYzPodT1lFh7eNoGc98EUhKJ2CiA156wjRCKJs3P+Bq0ICfXfLbqICkLAa5e/+fgmB77xm1n5hu8mqOAlkucDRn6JLTugNYKaDq5x2LadCm098JPDTvX4MTrB34sv2mRgBzPQti0xPjQA7uG/GKblv/Nn2AN/NmpxcUC+2aW65RO04hnuOcLZuSv5z3/asHa6xsZt7rGBydEB9HpYv0keWppsN+860+Pdw8OM5QAmLHDiuDKZ9PF1ZOC6oGmLFYTZ2TGdToWoJu0AWmbmRizvBrEVREFwZEWNK8dE83A/Ex7LBBuTUEqU6QNZbDCsG3xW0F3qog6itEzGgaq21M0MH7mp5uRJoZ857h9W3J7NsD23SCMFsbOboBBGQ5rZAWHfHiazOTZvkapiEg2r4vCqOC2ZnIAmKBtLgl+eZdIIp04VvO0tikpG4QTxMBo2qIciE7I8gal8DbGOCMqe3YIxLXmZnPYIXYhdul1lMBOniz3T1WYH5PbwrLppjUtKb5lxKHnqUaiSZZaiY4FkSnBuXdYEptKgEJRQt4j3ECJtVGqBrSBsFl2qcsDb39LlnvuWCXaBMYFxYZAswzSWFqEJERMaHJ6mtYzLHn7XPsZa0+9X9HqnsdRYhBAsXj1qtnGuYXnZpzaCRERy3vTGwOqpecQXSMiSMuKD7/a8+EIkAueFynm/LiUivVlltlfjrJIZw9AqdQmVZlPkfABtcXh6ZaL+dUpLt9vBR2B9FY8QJjka9lKEOUoVOmECGmmiI7MOo4bokyXwpNbpO6oEr2QOrFOMRLp9h7GW1ncJ7S5E+qyt+ZRgqMWQ44ww040PJFoP34Bx7j3SneT4kz4EoERJ0uA63Y4kFoj2UYWiKBCTqnFRMqpikXbXLO38MsEUiGmYWcj4yB1jtot5qtwwcV1izJEwTQam71u6Gn3g8h6LB8UXbTJgjME5R57nOPfIQR8e2PIVpU20lQhRGyITggSMyQg2o1nuc/+NH4RGsMFx153b/MS//hg3frRBbMuZZszEBippMNGlXpgo//a1G/zZXVdxTA5jKRluBdpxBxlBPlA0Wydq+t1PevIWr3xVYNfiNlYMaiCzDmsU65JKW2bg+V+5h5d93QVk8vnHDp8f6bE3U6JBPLc9vvybL2Bx9waDrmGxX2ByaEzN1sQSfcKDfOQj67z/ow3kljPbhre9u6LKi8SKzrqoWJiAKQrqmZy67LMtDWxu0wlj1E2IagmtcnIjJ05yQiV4QNuW1bMZv/EfW4ZbM2iW4a0waRoCkJURWzaoiWy1ShNSgX1+V4uVEW6Him1BxFLYPAlLYc/DCz686oNKErQOMceagJU4PVEFZro5P/mvu7zgeasYu06UVCTdvVJjs4ZgAzEIVduh9jVxAuPJPCOTse4MbVNxerPi53/pbt7x5gXytkNjDJuL+5m/9kmgNcEok+BotUQJrIYOzexBgsswbUsnM/QywWNpo2IqcCEH6bGyVzh0YUZgjNgRaI+P3RAYjXOii2lgz42f4e+BMP+R1MF/WOyo733y77AgFisTrImYzGAIhGyGKEJs01zxMqQqhAsOrvG0JyjGDPnqr62QwUlwihuu042eVruMDqwgy/MoyhOu8RitGNWO3PUo1GBQGmOICrFxRA/et1xwdJNBv2J+oMz0PItLsL3uCN7iHEwmKfmvJVGZHfANX7+HF73w0Bdg9X+I90ZIut3nxnf6eYfWMNUryUuD0dTK1OiQyqJOaMsW71queUrDNU9s+Oh4HvnaH6FaWMHFmpxtTNWmdzAmfE8gCTyh5sG53WMBfJEmAzsAQmPMp9V7/0JGSgYi4M8rDkQCDUEhBsUT0ccfYrh1Gm0duTfgDZNmme1xhtcu9bAl94JRh8Y+rSY/g8lwhWPtk7kj7icUOa1vqYYGXwWCqaeYpAzVlrmFVb7tO3osL65j3AixkdztqAimxVUIXHPtDNdfvxvzDzId+ceEBfIpcKdFNCkYPOMZe9m9LzDTcyz2Hc4pJodRvaOI5tHYZTLpoNai2YC/e6fnnhN7CepoZ3usF/nUYW6ejx/LWW2P0jMlZthS2ZKROkx0tJSsNxYTHHacqIOTCoSczfUOGgZo5miiUFeKsS39uQ2KUlEVRo3QtOnE3el7nG0T5sJo4r6bJNuSWQfnkivz8PckRQk0II6iMyTPT2DsBs54cmP4mpcPuOZyQaQG67Gm5eKLDHlXGNeRO26uWT0lSdDmnlOcPekJ0eFNRtZ4TCuMq1lCnZHJBpWtWHUX884PdBmNDE1TMBzNoMdbct/jnvsL3n3sYt598wz33T2PUJCLEKetBRPAqYPg2LPPc8llJUYCQoWJgUy6hJDRRj3HFnngJGke+FLS+D68y9d5v/f8P577wmIRlCwxdowhaGL+SIhAQFQZB8velQkXXQRZN3LdM3KqsmCbLqMzZ3GhoRZYK3tsGSUa5chRIbYtt98esLYhz8+ATJI7pRq0LiAKaGRlzyqDwTqHDq+ytOskBw7NcfzeDsbAvr2eOz/hErtFDMEPgcj1T5nnyscvIw/rdvhJFYBP93HuS3kAwzB9zZiUDBBtIsREIfcOq2AKSx4Nl16s7D0auadYged+BWvFPPbESexok82tbSAJiTHVZBA1oFPS6GOggQfFF2Uy8OiLB/cvRYSoimpO21ikclSUNJ0ZxuMJhMQ79k5prRCjYHyfdisjbBmKkNFS4EXJtQFTcH/cw0fbA2wO5qgJVA00rceHgMgAwaV+sNT0Z4SvekkPK1uIQpFnD4CMJIF5zjnnfQpY1sMdAuQ5OOfplDp1R0vj1tTpIU3SoZZmCDMFkAXe9T7lbTfsZ+x6mOuvIP+GlxMGOXW2wH94S8XHRheRTxStAuaCJxIufAKeADpgPMyAnI3tpDjYVF2M5Ay3DE4cmVNCozQjeMr1PX7oR4/S7yZecz2ZXhfQ7aaSrJv62gsPHGZ2FrXzxBce3oFUg4kziLdcd/0ufvG1u/nBH8+Ymdmi4xwZYF1GFMCOMNKCTIgxoLHknW8+wcbJGjMJ3PzO9zI6vk4eM6LPCROwrVBoF41jvux5nn2XZ8xccA2v+tlbufOeAd7Ocv8JobrhLkrtcOsn4L/ecDm/+r4l3vTukqzTJcubhF7xhraFfs/Q6Q+58uolMhfp90Z87/cPOHT4JFYqQlsnp0Vbgal4AM1/3u7xoK8fzvjk3/vA36fHKUdwSQ0xi6h62mSUCXhEu1jfI88V6UZCPoReRpy9lv41T2UjtERNOgr33hU5ecITVMlLoRqX3PTRVfJiwtd/4zK9wYSdknsMNpW/o9DtKVl+iqc8q+B7/vluHv/ERSIOsYb9R9e4/dYKZzsgLePxhKAKJgMJj47NcOcBenBmcO7SytJMn7Mdh5SdNayHbQr61lP0lHGRs7U4x/xVV+PvPUnfdWm6JV6mG79JAE7zWD3g08ZjycA/OnaQMOfzoDVph0eHr2FrZNiQfWyaowwnfcQHxDWoUVBlY72Hiz3qbctwrPjoWe3Aeu6m01fYZA/3ucs405ulUYOoMKmV4COdjoBOsNJi1ZBZ4XkvnGHQB4mGLEtcbd0RodkRcBH4wvRez3vQNW30zkFegEqLc+klZzMmk3SiVk3JgJ9A7sCroYq72GI/tQV7xVFmvv4b8LnFFI7TXMnpeIjCC9LUuN2HafdeTHSCxh7jkaWNhlOrMBrB1lpBVCH41HN1eUAV6knLlVd3+IoXzlPYIc6MaJtI9BZroNsH4yJJ8VrOTYFHJgSJ6bouuWzAN3/XXr7um2fp5JHCJmta22lQSVa8hgZlhLWJKtlMgY4ShNKDGIuooQolMXRpK0G9QVzNU55bc9HjLfbwPFvxQup2hhph7IfMsE0Uz7HTJaudx3Fq7jBeu7gi4IoaMLReaIMyvwC9uW0uvyIjNy1LC0P+6fd0OHrRGhJbYiOEZlrOlWTU9eiNqTSvndp9R2gaCMGgavHkqJaIVbBgbU2vtLhyN/KEKxi6iNEMgyM0JVEKQDDWMtweMNyeIbbwghcN6A/ac0MRY0KC+BCxVsic5cgFLV/x4iUWFgwH9g8oO8rBCxrGo5Ysq3C2oq0sGjVVMM6xXh6toahCWYJzBnE8aC60rmRsHN1MWDARGy1NlpNfdoiWhvmLjrJw6UVEHFGS3oqcQy0+mu/7kYvHkoHPS+wAZXb6XumTtZYzp4Z88MbT3B738z/+ao2t7UVsPULsBLxQRssb3ihkeUFdOU6PPRPnGV24l809BxjHPhHL2BqOd+eZveZxNDEtlBtjiD6n11GMmyRV8ZAjLexazjhywTZ5PiJBKhIATpVUf0vH7y9QXUAe9KWY5GNjbUtemORpAzhbMhmn7zEEEMe4iqgLoDMEcVSuJeAxZY7tLeClizpDbA1NVGJokMkapVW2w/QHq6OqDHUQTp1R/tsfnOA979pAVYgacS5iM8XHiA+eLB9jTcuePWMuvHQd7yOqlm4P8jziXEjJgHzSvT0CIXYqkSuewgTKTMnUkjnAeUzhQR25y7jk0m2e+/ycXbu2ERNpaGhNoN0x+DEFwQhVMceWL3jzO+6jrQwqkbIPRTnijAbGYqlCi6qHOKINYyYm445jgWHjGHuDDRVZNiErUv/fB0vVKkVPsW6bLPPMzgSuuXqdxQV40vUZj3vcGPVKaCOhzdDQ5dHtPpc2lUQQiKhOlQFjBlNjqIoW4xzOGVw0LFslZ8TIVDTOoViCCjBAg0WM4LKcjbWC8WYX9Y48A2cCVhRjlKABr5GmseRSoPUMTjs4lMwF9uxRRGoKZ4m+4uhla6zs3saZDMFM24qP5jPyNFGRdGDIy4DLEoNjpw3XZI5tB9ZYZmyk8BVECLvmqDuGtt9hWBRAGlMEkji6Puhs8lg8EI8lA5+3mIKcFBI1Jj1qMSgTr5wxh7lx6zDoPGydRXyOtoLxOaNJzuGjyq49PU5vw9B2ue57v49rv+Xb2IxdfAut8ZRH93D5VzyF2gc63cCoDvhQMDNQhDEiFnwCkx29yPAr/+kSVlYaymKqQscDfO3kIcAXIEuWT/HHdLK2Np53+lOszWka2BFmESxVnRgIYFIZNjbgDDGAdHuM+/NsxYyiqcl1E4xH/BainrYOoIYQhFFlibHk7Kpw712Wpu4mg1sRbJYWmbY1DEeJKWCxfNO3XsaP/MsjxJCqFDOzHpdHjA08WMrikVtWHhDaSoqIuY0YEyg7kEBYFis5hY38+18/yA//2DLf8h09BKWqBBVLo5FuW+HFIWVGnOkzosPNd64nrARC4QyDEtYaT0uVbIQJEHPa2Ec7BaoltjGwIRTR4jB0BwUhGJraJd8Ma3DWk5nINdcu859+51msLBl+9F9dxY+9oocxGwSdVjN2kutHbQQgCSsZETSU+AZaX+OylsbUeNNgXYZzkEXLvBVU19nM5qmYTYwUVdAccEQNZKVjfd1QV46q2aLIkzqcSAtR8D7Q1HD6pGVlZQ/O5PgquYVmVunPTHCuobADLJHrn7rFN31zhmGUygpKyiIf1WOb1qo8h7KMuCwwdWIgVbksVgxZkRPLQGtrJhgWvvybaJYvwC8uUnU7GJ36LEiSZDuHk3gMQPj34rFk4PMSO+C889HjiSdr1OBwjCiY5Ev0shozGnHs7iX+9P9r8GaRQMvuXRNydfyPP5hw4v4Of/qelrUypzaQuDE5GyZj6FIZMbPC61+/zeamoew1iASsjig6DdaOcdlZVvaOybPmnIujfgYPgi9kGAMuk3S6lCTTmmcFdWXYORVoNIzHjiyzlOYk4BmqY0vniGKgn7E2t8z7b8vYXM2xTQVtRawmtJJTjxsIFkUZDj3YHh/9UM3WeknEYUwkSMB1BFyXP/vTmttuyciKZLDS7wuDeeWuO8dMRp5ez1AUEWOT4FDqGz+SC6oSaUBaUItEm1z9LPTnAYlYG7EuYmXErl2RTtGysNQSmgptHUIGPpKvb0AQ2lZZ284JIaeNBQGDSKRjlF1FyeZWhrQFOgW6xtgjasnSjIIIdSWMtnu0lBjjyHODkYJP3Gr46IdPgTqcS/5YlsDCrFJEKFzN7EJF3jtOlitqdk6wj97Q8xJsaw0aNG3upiXK/WAmQCDLckwRUdOSl0JTwl+8vcdGe4g018MDBUWjZIUQY0Zki7n5KlXSpCGzI4z1+Dbwvndtc/q4ZWl3Sda5k/7MdvIVFEu/n2yXOzmUWUuvjBw6PGZu/jYyGx8ucezPayipglh2lG4XXD6tDOy83loKX6AmQG9Cqy3/+ffP8F/+aIN7d13N2T1LtFlBPjVHAkU0/YxH+bR6xOKLUo740RfTnOoclzagGqavCaVaNn0L1lO4NeKw4l3vUn7n1k1qDkDeUrg1aPv81Z9Hop3nL98yxi5AS4ZtQSRn5HuMgapqmBss8Ndv3GawENi9v2FpSfjRn+rwZdcNEMkRzSmNIQMW5tPbvMOxVZ32z6Z/+7DGA4n4eYt7TJxiiahO2b9RcC6nngIIMQYNwnjs2LNsWXxSn7s/IQyWLiP2Lye6HAYdTnXmufGOnLXRHKGTtM2bVhnZPnUVsBqI1qBaMTvT5U1vPMm1Vy4QTIPikxZD1tLtFbzudUOMUZ745e7cWHW6MBq35EBZBooCrFWMfYA48simAxYlpBOPmrSxmAndvqIYjK1ZWJ7AKAEKaSyZG0Hw+KoDscF4RbZGiBesF7aD0lSB0FhUkqZ9bgPzRYfReoG0BbGZYjrEIAzZO5hw9+mKUYhQd4jZFlgoywJVw913Nfj6HraHC5ieJD0GBWMaFEfhHFdfu8xrf/vpXHjRItYGxNZA5xEe4YcITSBRkanTnra00SN2g+/6/oO89/UNctrgMoPrBmLZ4EtoZhZ53btqLi13p58jSbZaJVW/ytKQ5RkHLvB87w9dg9iAs0O+7MtmeMu7KoIX/uoN9zEaLvL8r5vhZ37pUi6/vIs1AaKj34sYp3R6hsxO6Ng+L3rxEQ4f6DMzq0kgQ8KjvjqgqmSZkueKGwd2NnIFgskYISwvdrj+ugWOLme87o9y3jN7hr989Xex8oSc0xgsQowJs7HzsEbRRwQ4/WiPxyoD/+iQT/oMSUTFk0qIgItJXIU+pi7QMGRMF9/MIlFxTcR1Csg8qj2IXXws2RpaQis0OwI2kxxfgxGHGqH2Fust/VJYWrS8+GtnufZJyWo2hpwMQ5Hdx4F9LRaD2XHSm5bd0/8f3lLsObwifrphpZ5nS+p/WnVITJSfvISqbVEc1hVEIwwrT6evXHrxHMSAm13k9vmD/MXbDe/5wCwfOvMEQrtAQxdiQd5GZFJz290Vx9fnQQxiAplp2DPT0tSz3HC7EG2ZLPyMpZc1HFneIoaCOszQyVxS9sMyKAWocOUZZme3yWOgZwagLbVoYng9bKP3mUKQaFDGqCQ7atsKuTtOmTUggisaVha26OanMSYiuaWTC7GNjNukWxlFMGNQSZu0SKSJfvpam9wxxUHfJHXIaBi1GUJJxSKVqym7IzJzlhgFjZ7MTCCLFFmJM8qwrqlsn+BqCqvkGURjwBgkB0RYmHV81YuWmesLO6K1j9oVW0mmVCRJ3E62htUJTWsQV/CCZ86ze9Yy1IKQt3SiYAzEXOj1OtQsolqSUrYcCYJRj4lCp2MRG7jkaOSSKy2ZeDps8YQnTBAqYiw4ubXIvSc8Bw4YnvWcJfatdBG1BKDoNmRZTWeQJNsLm7G87HjeV+ymk0esgSRW9Sg9IyuA4NUTbCCPhjI2dMqa6BL7qjEla8Zz8EDBBRd22L1PIFq2jWHumdczOHptur9zpB4hoRCTvPNjG9/fj8fG5PMSU2rhuYXrfM5sMoYRzUBKTNun9QVjCjQ6rCoWixqLurQ4ExXF41uLRkuUtJE6jTQtRCLilCqOqavI3ABCZbDeIKooNdhIb2D4Vz91JUcuzDFTCdRzD8c5q+GHewpMKQQ05/p9ybZWEfWpQ5AuB+eEapJYGWLSqSVoC1mE3BPEcryCy77hu/m9v5rwql9Z5b337qeSDh7BR6EIglmruPFjG5w4WyDGEL0geF7wrIyZQcX6lhCDYCVDUHqF4fIL5snLtDE6k/RQRKDbV5xp6HaHfO+PdHnO8+YYlAXpXRinvvkjiE5OW2Z77uuVJcdP//QT2b1cIhGMWA7vc7zy3x5hfleGSqRwBVp3aIJiRRJ/vQkYayiLDLQlhsCkSsYuRg0qDrqO4bbQBMOkcYkyKII3GbsPCa945Rx79g5xVumYCld4nHUYdUxGGW3TQ8XipME5JVoFMkTsVLdecIATQcQiUj5i4/qZQ0Bsqi4ReeFzDpHbCu+FGHO6BgqJ1ORIpnQRJKTe0mw3J6qnigYkJ2pqS1nA4SiKDOcMcwOHs7CyIvz0T1/KvkOCySaA44aP5ZTdPivLeRI/Skfe5KZYGtpGKYqM3HaYne/gbMRKwIhNp+KklvVID+KnCYEIMYD3DiMtP/mv5rnmiRM8Q1SVoBleLP2uYh1I2ZJqB4J3gCkxkPRNzi1xSQr6UxzfHgseSwY+j3EeVUcNqjmQ+PzGliiGmhEEZWu4wqnNAdEUKA7VhANwzp4rpytC2zJtPRjA08RA1aQyODHDtzNsTjxld0I7HuPHgpMMK2BNRZbBS16+h5m5hBl4QJxJPunjYQ7Z6feZKfHCYbDk2YQ8G6GSeMBZnlOPinOiTaIRocW5CCUE5znZzJEfvoRxeTkfuXU3Z+IB1twMrRjaEHAh4teVSTsD5CRr1wzFc8Glkf5gDWPG5GZMx5G09TPL/gMl1gWMkUQLnSZ0s4NIRo1hi2c+t8MVV5eUeY2LgtX8ET5jRMRMEIrU+wfKQeCFX7uH/owltmB8JM/GvPTrdjEzHzAEMungJwPaxiPaoijRV4htyDsRYwytFqydnklYBCNY6+h2e2ysF8Tokn6G8SATCPMsLBq+5XsGXHj5Kr3eFouLAbShyFusNGyd7TJcX8Aah82mvgnscN3TPNxJCh8wJfoCzc/PNVRQtYiFqx/fozcw+BBRGjI3fW5jRpF1KXNFoyKqzM11iQQ2y1nGxgCRaGSqox/JC0NmlUE/QzXHlYEXvHyJhRWL6jYwpBoVHL2gZmnBIOrO61fF1G4bl5RFSZEJK3u6qO6M8bTaMhXfebSGakoGMttg7Am+8mWOvQfXMNakTV/Sxm+nj5+1CjGH2E9WxeZT4KPO6ao81iL4VPEYZuDzFjvCQ5KSgelGhIAzMtULCUy2xnz8HssHY43HoSFtPtYarEue5FGTuIifJgOGtKH50OfDH17lnjsr5uYyRGcYNyMOHxUytpmb1bTvUoIoRhUxOwvuA5uWqn7BVBtlZ2xEOGcfFlOP9VlfvsSZ43V6CcgLS9QMNJ4zEjHRU6AsLWeotBxbzXnVr29xptnFmbWMlXIPG70cv5GjVrDBU00iMc6wa3fD6uYq48kiuBbTDfT6kZn+GZ5+3QIfeu82440B1kFe6rS1o4hJC6WI0C+Fx107Zt9hpcwMMQpPuV7ZtzsjC1m6r0eq9SoK2gA9HthMR4gdo3EeZwqWF5UrrxaKzghhgoY5nCkSXdI3dLsRo0rbjLAmUOQeyBmtRT74fgHNQDzGWorScvpUhfeKMRbnhCBd8LsoXZ9OB/rdTfauzDCTG5zLueKagssuO8NNt3aJNufQIcVZj7E7yfMjjbr43EM1lZ5VWnCCcS0hRJQalzTAiOS4LKH8E/lAmZlJ+qTjlf2MmgK8QWPCGEWjlAXk1tPpJt0HkQqTKVluMaIYB0RYWppQuIglO4cDigSsCE3V0ikyigKWV5KpVApzXgL26I4is+zaBU952jZzs1AYB6HDuDLMGDkHJxQgsyASQPOpyFB4qB/9WHyKeCwZ+LzEzoMVAZtO9lNwkbFKlk1QaYjGkBnHetjNelwEl0+B84q1hixzqEaMWEJQ6lowxiJYonrGtWVzvaKeCGa2wUiFGs8TrtvFv/rRwxSdJvFop5uumAYjGY/8g2+BjKluL04iaOC7v+eyRLqQkB7o3KZ91SgaPc4quULp4cBuS7c8TTPZyx2nDeMWBE++vIKZX6I9MaYRRSUQjDDWmu/8Z13+8Pdv4c57+oiZkHUVK4anPnWZV//CHF//1bdz/9oAlxmKMo2XCGQuZ2fMdu0p+KPXPQuXQ5EZJIOffc1l5HnE2AYkO+8U+4WNVJXoQnQwBeJZm7jkGIeK8DVfdzkaocxbBEcUS+YsLRNEC665ZpHyJkMMFdG3FEVAxRDImYxmEByGGmMsZV4Q1aNRCAEki1Rk1NEkQSYjDIqKF7zAcNMHzuBMh6944T5mez2++quG+NZhbYW1grNTzYtHban6M4dIwu+gLdYJ0OJ9QLVNJn2ZpZY86WBIWhOih34vyQ4fevrjCa/bhK0IRsgdBCfkhZLZQK+fgSrWFkQtyMyEzM5SVR2sNOzek2NtEiBDIhp39DvGqJ7A2d0U2Yjl5ZAkfS0kXwUe9bw6MYJRePozV/iyJ68k2qTM4X3gjk84jq6XKHkyaQKsSGo7as5kZKYuYo/FZxOPtQk+b3EebmAKgMEo/dmciy6xuGyISJ8MYSxdJgxSNn+uaCU4a4CQPN5DTAI8KmiMiPGoiUQyMgk88bo1jl56K5nboLDQ7Uecmeq1iyTus9lCxE97hA/++MIOzfSEiU5ZDAFnGsp8QqfTnBs355JCGlh27fIcumCLTFq6Trng0AxPvz7QKTZomowQRuzbvUkoKta8RdXgVRAjNMYxci279ka+7GmBIwcrjLFYZ+jkOZ3MM5hVbD5EUayFrGQqf+ux1k43WsXamsFgSLfwqZ9toOzVmGyYSvSPpKyrksZVIWEyIkY7CHOJJWECedFSdiokZhBKwOAKoaVmUjsWlw1oQ2xajFrKwqIxTrXvu6im9oMxhrxIYNMdAUuXeyamIuRnsFlDLnDhkYarH1dzxdVDFhc91g1Z3O3pz54GtrjiqpIYpgnqo10E7zPEOc0OUbCCkYzgE97HOKhNJJ9fJLicaJPPRYzQ7wVUImdyx/jc+xfIrWJtTMJWJpDngnWKaI5EizEGK13QDmJa5pen4ygkrqYKRg1HL+zyzGdZDhxwXHzlJjODgMZU9TrXGniUe/juyHobo5SdZLfs8jEhBv7ja2v+n1/fRMkxLn2vwdLpVXQ6DX/5+mPE+PCahH0pxmPJwOcthEQRmmrWSwLyzcxZ/sk3HWRu0KK+gwlKbSP1OV/thLCPMWJt4r90e4ayEJpGz505VSNtFNrGIMHz/T8wz3/7f5/M/j2B3Oz0Au0UYqBJr4UCprCaRzZ2cA8JOyDqkFhO0czVuQQqcw6kRaPhgosi3/pP5whMCFlk9wHDr7/2Si7YF9AYcHbCj/zwLN15YRQCM90CbzI8wlgH1DLLYMbyml9+Mt/13RkaciQKRR4osqlokLWoCM4Z8px00jcB6x5gWIgEDGVakHf+TguEPmgf9FFQXDNwzi5ZmQrYAHhEAkIE44HEGHBdw7gxrK075hcbLJGmblC1dDrT90Va2AGakRwvi1IQIlGT8FLRhQkZTTbCFR7rlJ96xfP56q+5gJ951Yt52lMuIhfH0Yt6vOBFS4jZ5rnPDxgMeaYJfPko3pA+U0xVBgDBGEEo8Y2iGshykF7OwoE+0unjTVK4VI3MzkWUwCnvaIOZtrcjmVOsU/IOWBcpizwZ64idCvBYRDSZlYtSdPIEwRGANgFGY+SZz9zPa3/96Vx6RYff/L3nMLeQTzEMnDfcLY/+sZcHJS02b4gm8PFPOI4fn01aIS69A5npcOmVBf/k21e4585IWz+iF/5FGY8lA5+PON8PU3TaIlCEpJo1szCh29tENEdRWtsSiFPO3Q73X1MyIFAWQpYpVZUW3hQFW+slf/0GJZecbulZWYZeJ0z7r2lrjVIRJaSKgh8A+SPfJQAe1B9WAW+R0EG0PLfJZpkgZkyRB7rdiv58QPHEUvE2Mr+glFlF27Y4q8zNRTwRT0t/FtqiQ9vpsx1nWQ09Zv7/7f15nB1Vnf+PP885VXW37tt7p7PQkH2HJIQQBAkQFpUlLsgwuKEjgv6UwRkdFcfPZ/x+HH04og/8DAwfiSwuoIiiwzYCiSyCEokCAgJBAkggS+/b7XtvVZ3z+6OWVDcB0lmgQ9frQei+fe+tOnXqXe/zPu/l9a6BuvpO6hsHUNrCVpJMpoKjfKQWIPIgDZYlsR0CZkHho2RiwkwOcAL2N1kK8gqiUs/Qe/DmwYCogvCADKEFSJhsQTDX2eA9ekH5GAkqIxkaytDdo2ltdfE9j9JACYkdLEDCB1HBoEPmah8lJU4GgrQtH6U8Mjmbqm7BM3VIKwhV5PNdZB2XnC1wpASdDQ2JYZSskM25ZByCY4/z/MDXhgkTYwOZlkIiyOK7geFu2eBKQ0kYtOPgSYH2DUZrisWAGryvqoJujsaA0VjKIKXGyYKUmoxjYbQgSLIZDsiwpIuUGnBQlhPU3Bsf8IInTGucbIW6hgqOU6WmoYLjWEGvj0jXAEa4mHHL0W8wwoTjE0CQIKlEA5gsFa1xpQi8ekHkEcuyqWsaon1GgaefLDM06PJK4Rrf3pA3G6kxsA8Q6LSgVS9IhDJhrNXBkYL2KRW+9tWptLVsx5DHlR5alFEChNTB4u1nyNkWlvSDLHpTYHjIQlkKHx9tbMpDGf5wv8/cRT4Hz8zj2AJHGCxpwnEYpAjqaMOAJvu/5/tuIEpYEuECKgAVjM8IB43ENpCzJUL28+FzK3zlK03UFgwugsZasBnEsn0EtZQrNmWvipXzQRsqHoiipub0d8AF5/GkbGCTmo+wFcbKc9zb6pna1I1vNE7OwbIMrg3SqSB0CduS2BbYSiAoYztBdz+hdUBnGg0/KscSo6/tTYIQQR4GO8NDI2+3Fb4UGApBZYVvyDsKf9hmeGiY5nqPQRqwyjYeblCHrnyUyKIBS2qwg9yNjG3AMjjY2EZTkxeAQ8V3EDmNRiKFgyUssMFIgdACSwh83Y3RkKvVXPy1OSxe3IA1bhej3YWPNAQGrQCFS9l4CD9oHiWFzZArwbHQYfvdim/wrUCuGJRokwky4qWHZQ1g6cDDguWSzXqh10wFiYhKYkuNkV0YWSFjqyDpy4ig9FOBUHZcppmRCkcKlBBIaSOwErKxMy9m3EEAwkfgYxsQQoMFTUUHq2pTrWj6laYkPLJuIEPVQh9W1mXJfIferu30l7yg6FOUMVExk3Ex+KlJ8CpIjYG9xM5yKDniYRNCIXFQQNbp5+ijC9TUD+NrCX4GW9sY35DJ9mI7z+BkurCkh6KCkdupVly0Z+F6AX88wg3CEKaG5jaPQr3AVj7KeQ4py2E9okCEiXoCEvW1byJGVIglev7K4KcJQwgSyCiJMJoFC2HZkmyw91aaaZN9LAS2A6hB+vo8hssWliOQvgFP4GQkor0Vf8Fc+kQd/aKRggOYPFOnZijUPkyu1sVSDpYSGCugkMXYODZk8xpfdyHIooQFwsdId+f9JGiBHHsEout4k5IHd8Ji5GMcjkfI2OMCBkMmlA9wlMIdlniuppAXeGSwtA+iTCZrEJaikLOwrDJ1xQEMJRxpyDoCrTTG+FjKkM9l8I3A0xqV0RgUkAnuqRUNIyDcyucV+Da2snnb2yfRNiXIXzhg7YHQwRWMP6gCUsLD1RphDJYCYTS+CUivHMenJrcVO+OishrLGcQdKIHsJ1uzDcfRYezbBLm2lonJgQJiK4W0A+pjdBaMxLYM0kReCYVQQVmowEIKB4FCCic0ZCO5iDOUEvIxPiEAYUTQaFDBtPZhlBwGrRgUNoMqhwoTDYVtY9s2U5oF1Sr85jcunu+ETLCRkAW/p8bArjEelou3CF59YZCUQWrKloVHFeHlscljLI+T3uHw7e9O46OfnEq+MMiMOdv58r830D6rylApkZwmy6BKAQmMpXFsg63g4q8czozZNTuTA0nUEo9YsN5sJOZH7OKlCWKiaDtQcAaylqGlFdqnZ4OkKSVR2U5ct4Lwa1BSoTSIik+NDe5QEPc2IkdGV6nPaJQWOHnFV765isXLctTV5nDsIGlQKYXCwWhNQ6vN8afMQJBFChXULIsqEBkE4jX+vVnYqeBjj8UoIyUY+s7kVqmCcMzwsECYPNm8QjIMDCFxyRQkyraoLZZpa+nnhJNLCLuCMS6WNDj2ME0tJYz0yBdrKZsMLpKMHTqiSOSoEbjMhTAct2omWZlF+gopdOgCHt90uK+PcPxCIFWQX+FVQRsXywEhKvhVTc6WzJvbxHf/ayUL5hWZM7eeo95Wj+9WmDd/iO9c1srCBWF+htIoCyzphB4BgxZD4RKm0cZDUAPY2E5A/CRid/pIRBIgRsgJ48SIfT0Em4QgZSBgxTzm+Ebe8a482vc4ZN4krJYpVByB0NDkFCigyOcNVZPj5ps7GS7JOOclgBwZ0k0xAqkx8AZAmoB2tWI7VByNkQ4KDyFKLF0sOPdDzRx1ZJ6cAw11Q6x5fwHbHqJckUgrJOEx4VbLgBLBgplRgpNOnkRrqxOfy4z37i6jkFRJtg3IgC5VaChkqrRP72bS1EAxSkRIqJRBIrFkwDuuXZdCVuJqQdm26bXzCDFMLh/kWyhLc/I7p9HcqmhucXGsEpYg4L/HRfqabMawdHkttuMhLNBGYsijxQGelWwIc1Miw1JjWeB5EtsqoCyBkR5Ig5YSkQFlC+pqh5naYpjU1oNUPrZS2JYkX7SZcnAfUmlqmzKUyFKVQZtdkdx1GYNAI1RAiTu51WBZL2LLCgoRuH45kOdWjLB6hAKBwq8KDD7KAW18vIogY2WoK1qcuqaR+hpJW0uGI5Y3MdgLDUXNe96XpbamhBQ+UpVRlsGWGqGi2HlADOV7CrSF7bgI4WFZIwmFR+70Dyw9kIQZ8Vu4eAuP6bMFhx+lUALsomIwn8XLCGxbMKm+RNEZxJJQ9Q39gzaYbCiPSUYCQZo7sGtMSGPAGIPv+xhj8DwP3/fxfT/+fd8uqALh5TDawqgM5UkH02tL8pkK0vZRSmALsIwmKwWO8rEUSEsx2GdQ0gp2/NoJdsdCIXwH4SksBZYaCslyDlAYjTAeCJ/6JgOyhOV4CKChQbNkeZlC0SCVxndBeznQTrCzt8BH42oflZf0+ZrivNlkFx9KrlEh8yJkIDWosNnOYUurHHxwQGokpWbylG4WLSqTERJd9jB6OOx5bvB9MOMh52Jv8Iqha0KmZypuGeXYuMrGFXX4mUacAljKpba2hG1DLpNFopDCwrbAMi5NzT4Z26W+WTBIlrJUZJ2AXQ92VtYGblmBMIpJkx3etcZl8lQrTKRTiYTHAxUi5BQx4Rpj45YDIyEoFlJUywJHSRxLo6w+LOFjobEtwWB/BsexA6+KrgYubxmEAubNH6b9kIDqWpg8IPErCiUlK97Wg1K9MfseBM5vM8K0PtAXOz+I78eXIxC4OFkX40vKvs2QI3GFQFqGRQsrzJvtYQuQSjJYcqlWghLhnZXUUdIvpAbBKzEO6qLeePi+j9aBtWjCsj6tNZZl4fv+Pq/F1zoDyuD5NpljzqL/uXW05xvY0ePgqOA8Qkta2ypY9stks2DJErXFDA2Njfzt2SqaMpBBShejBoOSYeMFMUHUG88dsM9gwASJUtPas9TUSWqKLsrSLF/exoLlbVh2FWOqWDIfkNWIChiwFGhRxTUaKwcDQM2CuSz92Gzm/rKL2pYCnjFYUmGQCOHz3jOnUxkWVIWPY2ve+37JZy5qJ4ug2j+E0BV6u3NIUQiyto3DeO/u9roQAkzQZtEQVKoIWcb3qtjOVKrZZnr9mZRy7bRmJBl8agpVSgOFoJpAu0gkSmiUKLH6uNlsffZ5mpuzPIKDxMa2JAJN3GjWRNn2CglMnWZz2fdOwc6VEVqDCrwCZtxHrl8DJnLRG5SlwNTQtU2gfRl4uYRFxRdh/kDQttsIgxSGTEbSO+gzPScCsjyvhJSFoGzVeFzynaU4WYkiYIRUBvAqONY21ry/nSefdLGsiLfjVQYX7awPQEQ7+lA7BvlYukouY7AQeL6gakxIZe5z6vsOxi0p3KqHQTJcGsZEfBYi6RlInuHAnJv9hQPdNN8jKBUsnr7vs3nzZjZu3Ei5XB7x3j6FJdGWwB2WfOP723l4cAp2o0VWVsnZoVGibeYtlMycXYs0oHxFTcZncpMhZ/czc8528vlB8jU9tM/1YmZfwh4IBy4kAWujJJuzyOUsHEeAdnFsQz4X7HeUkAglOOFdTRw0fSu+X0YY8PUw2nOpLRgqnoexbbyChZ0BJU0Yxw4TrQAn41GoDd+TkM35FPIlJD4L5uVZcFgnB7fbAYOZcd8i6kIAUc9lgbI10upnxhyHQq3NQLaFjcN5NvVBNgtFqamv9emp+FhZGYRnlCCf1SxZ3scRR7gsWdrJlEmKkrHwsXFsBUnPAEBYWiuki61c8jmDo0RYullBv9n0F3sDszNVAzSOo9DG4sYbh/E9geMESX9lF6QNIBG6JmjAJQROBnoGqmQKATfEUW+3OfYEhTE+Di41uSpZ2yCxUAaUMBzUbnP4yj6amzwc6ZGxwxwADCLyYJnkAN+UmdlHEBA2NEMGXCxKZJg0SZHJ9aFdjXYFUhiE0VhOP9kaF6GDpOJqpRIysQowCWMgiGG9WRc1rnEgryJ7jCgM8OSTT3LllVeyceNGrrzyyjhcsK/j7lpWUBmwhOKx7nY2V6ejHQcHn0zGYJSLsqsccXQjX/36O7GUIGO7OPYADXUeU9pcLr9yLnPmVFl0qOQfv7AUE7ao1Xq8JwK9DgxgbNAWSkqUlGQzgctTSB8l3DBfwkYon/M/O4N//tdWfK8cZBFTRbgetVkNrosRUBVBVYYlgjh2wM4X7G6lBGl5SAy2khjPAzTICqec0cKPf3EMRx5TG+Qo6BzCHMhxbTMqXhrAtkHZwxx7fIb6BsWgKPCy1UbJyiOBrKxSX6fpGxxCZT2k0ChpaG3Nc/mVx7Dy6Bq+e9nbmdSqqBgLHwvLimKxYsR6FBALDQMa4Ul0JYMU0d8OfERZEpYNGcew5QWJ0XZIagUVTyPtYGE2xiFq2uXYUK5CNpcF6fHZi6dz7v+vBmkPo3QOqR3QEqEFQbilyiGzMlxx9QkcNKUe6Sssa+coXoEDWCUEUMQ5JcIF4aG14dhjG5l6UFfw3FYslACMQggXqYaQEmxhMTxYRRuBMQZjRucMpNgVJmSYIMKGDRs4+eSTefvb387FF19Mf38/DQ0N9PT08Oijj7Jjxw56e3vZsWPHmI8dNQPSxtA/VEGJfnwqDOrJuJ5NVYCvKgxVK3R0ewhZQRrwjcNQpxO07lQ+uZyLoEwmWyWX6SNjG4ar/ezoquJohTAuSBnGC/cc0Xir1SqDg4Ns374dpVQcTtl/qCIw+LpIX4+DdPupDDezvacfYSqghsEUUNrCp0pZgi0FwrcZ6gOvqvE8jfRdvP4Btm/vpmsgi6kaBju78YY9pJZBTFVWQQxjTJah/gZqZRnhDtLZkSdYtiSOtOjthUF8dEhvvLc7iVgWtGZoaCie2zci2VMLjTASaSRCDKJ1np7ePKqaIyM9SsPDVF3FFjUHz1MM9vehnQFUxqNohrHdHFnfp6c0REdXBd/qprecQfg5NJph6qiKPMNDVTo6OpFCMuBIcp7CooIft/m1EP4QCNAETI9Brsi+k6/+/n6MMZRKpXi+92f4TAsQvsASkkHXIJwOnPw0fJOlp6uMrrjoKgyXy2zvKmP7BiMsKsZiuKSR2sM20N3djVEelXItlu/R2+OyvWsb2Bb4NkoOIISL5xXxpGK45KAwlAb76OwQMYGTSZJORV1C9xKR7JZKpRGyG4VT9x+CihNjMghjkKKK79sMljPITCcD5SkYX1LqdtnR0YPARSDo7yuh5Hbwi/QMunR09mJpgWAYnyBZNqheihhRdw0hBK7rUqlUDuAQ7NgwIY2BSMArlQo1NTXxza5Wq0CQ/ey6YY94EViXUo79wRIiqJHVWmIDthPkW1c8GXQeU6DsIElNYiPxQiUJSmVQapjaosayfSwbClkLSRVLqLDfe9AH3cSlb3uHaB6inApjDJZl7ddFK5hfHTS+wcNR/ZhQaRrfQ8mA5U8gUEJhCx+hJcJX2JZGhE11nEyw4JWrUKp6aGMQJjBmgv2FQhC0Mva1Tz4H//iPs8gVfMLtRbgXidyrMqFK983cRgtUZGAptX+9DkEiX1C9LgxBIyaTRQqwhU9drY+0DE5GcMjCZv68YYCjT2ji5PdMY8p0lzPf4/Dc5iFyqkTVlPFNHku4SFEAabCzgpJxcO1a6hryQfwWFcixsMKSMIkgEwzICuQofBrCkse9N2KTv0fePSnl/je4hB9IjRHYFiw8dAqPPx48NxnHBMZ9BRwbgtJOH+0HXCTZTJAUm7EstC/CZ9pFGjeIUFlg8FDSjuVRCYUvfGpqDI7tIaUO+EyECnVaPDD2ZaXGm6EXIEjCDAneQQTtxbPZLB8691B+cq1muBwkXxs0wg8yip0ai6/+nxV8+eI/U/ECz5jRYdKGAiN0wF0QlVq+CpI6f/9viMYHJqQxEAl3e3s7jz/+OFOmTAGgpqYGYwyNjY2ceOKJvPTSS7z88ss0NzePyRiIDAgIFHJn3zDNzfUU8oNoDUJICjUC24bGpiytLXVh7MvDCMmwUeTzJQo1hkmTfJzMIK3NDsViAW+4SltzLZbtYxsr1Kdmj/euybFCYBCVy2Xa2trQWu+RETS282u8qkYqm76+Mk6mk4ZGn8bGOixdB9JFqmDxR2pc4VNbHAShaWk1OBnwfJ9ik4X7cpG+7kZ6eio4BUXzpHrsrMaSLvjhgiQNvhnGGEFLWx1RE6f9d30JWTCGnp4eJk2aFL+3X3cdAjQaaVSYy5dF6zzK8sjknmP2vDYmH5RFqzLNU3yUo2mf7rBsRQHkVpy5dQzd2oOVfYFJk2cxqbUeKWwkeTCS1sllhoXFsFXH1PZJNLbYKBQFyyVjnHDnr/ZbhHa07FYqFerq6uLneL/Kbji3QkuklvjSMHvOMK7bSy5v0dSUI+PYaG1obMjS1JjHEQZjHFwBzS09GK2pLeZpa21GWYLeoUFyNS9QV38Qrc1TMMIEXh1RAxiMn8GTFZStKDb8jUmTZtDcXHzFPOyTyxt1zFwuhxDijdMLYbgpSCH0kcJCa0PZs1l5TIGf/+QlhKmlpcWhobkGRwbdIl2hWHViPcX/qODYFs1NLTih8aklaLyQVTQylnY9b1FieSaTiV+/1T0EE9IYiIT82GOP5eqrr+aaa67hve99L9lsNhZyz/NiN7mUco8EIXqgjA76B1i2j6Uk2piw/wAoZYLxRETt4WmUBUKWqa3zsTLDCKA2r+grlQkKZtzQslVErGJ7imic8XjD+YnmYr8+BEYGTIBAS4vNscfX09TiIPAIqiTC5u3hEIwJdljgYYwhl7OwMhosm40bbT7wvmfp7K/l5Hc4+Hg7KW8FcV24lAIoAxUM2XDnun8f9Feb2+i9/YG46j8ur3bAGDI5wf/55tEcfnQ9vnYxtmJI21S1RU7lcXyJMa1IbTFlquHL/3Y0s2YVAwUd8V2gUQ6UpUMp00ChNhtTIe9c/vdeNncHybmN5Dia0/2rwMPzhJ45yzb095fJZu2gAIWgMVGQ9S/Q2o8JgpyMRuOjLBG0INaGqdNsvvYfK5kxqzYQeRUyXUaJdAKkcKmp1Xzl/1tJ+/TCiOvb19f6Wnphvxtbcf6jicsLlZRkHEltAaqVMuVK0GhMKYn2q0GOkXSRloOT8alUXIzJhMRMIw+80yn1+nO2v8NN4wUT0hiIhLiuro6LLrpol0LtOE7ght9LgReAlEGCkaVctA5anFrKRgpNNqORKmp4E7ivpAClNMquUqj1sawyjgXNLR6l/m6kCHbUO6V83+8Kop/7Pa4twso9A41Niv/4zmnYGY1UFWTCjR5cpQjdzqBNBWlBbTGHr/vI5KFvoJGu7UU8YYHcFoQOImUaBApBROVKWYLQQbS47X/saoHar14JxM5ghwCwEdKQzQreceokjFOhq8dgZTP0ljXCsSjkRch/7yOkR7FumHefOQ07p8Ls+UxcDGjZAuFkGZSFwLCVEk0QlhHCxLu7/YnRc/pGGFmJswf/iaC0wHZ8qtUyTkYgJQgJGoNSIQ+gjMo7NbZj0PgIi8BI1Yac43Lyqa3YKrB3BVEIMKg4EBKkEGQtn3ecOgmVePb317Umj/uGzm3crTDi+QgomCUWxZyhWi7h6YDq2gBKZsE4GA2WFOQyOXwXpAwI2mJ+KGF2KzqVNCj3dzhvvGBCGgO7s2tIuoXG6s59hUsp7EiWy9uARgjIZAzKCsqto41bclm3bIGT8agpGpTlYtmCFUfZOI4NJmhjaghyEKRgr6q1R4939PXu70ShoATNRglBNmcCEiXhBrXx8XXF5gA7uQl8agp5hC6hFBil8Ywd9u0xSC0RUgTuVmFAeAh0UL1grDADjP3ay+nV5jZpaO1Pz4CIyqkSMqKECBoJAZa2kbakVLUwUpOv1SDLCFEGFMJ4gZzG68BOSZUShCXxVA47vC2+AIeAfTAwtsR+ndvReOPmNrwsszNRz3Y0QvrkCoFnIHIaKBnlTpiAfiGsPkCCdETstZJobMtHGiveyYp4sY/O6aBwA2NtP/OLjJ7fpF7Yv27zoJTQhKyZBokxFkIrBIa6AihRxfU9pBWOS0vQQYtnhSabyeFWI2MiHH9IzRRtunbHIJhImJDGwBsNY3yk0DQ3NRC0hzXk8xrHMtjW6CYzwc7BsgWW7VNb1IEywXDK6ZM48eRJKKURxgoekoR4H4iI1I2IXggTL9hxC9MROilYjDQuQkIua6N0QJUrVRkjchg0Spmgv7sJGr0gfIQxBJQlKj7Xzmk7MOfvtRCasuE8hgx1BgSSoO2xjYXBscuUq3mkNUguB8LPYlQmUK66EyGrRF3uRKhcDYHXLGODsEIDQ4f2WyyVb8VZHY2d5D6WYwCXXCEHBLtQKWVA+RwZUSLwUVtBfiVWSBMifMAE7aOjhmcRvXOQqEhQQodNYGRVQDi8JVW4gWR7cJP4LQgNGmqLEvNyFPnbqSCkNEhLUFNwqJSr7DSnTMLbkGJXmJA8A280IndpW1uYCGQ0uazBUoELcWR9duD+t1TA6OZkPKSsIgVI26dQLCOlS0wiw4Eu4IKAdIhEnNACMoAdGjw7YdDU1FjU1ymkVGSzAimHsJVGhVUWCJesoxFSg6xiRDU4OHawWukwaCiHQVTe6At+AxEtxYYoL3vnnlaDDgiFslkPsGhtBTsDQbahQRuJMFa4UkGcuxHGWqUQOFbAqKdEtJsbefoDWTJfE/FjZ2Jjy7aD8F0+n4nXfRl6BcL4QPxTWUG2vGUldqjGIbgjgPDi4xLzOppAdrXkwH/uXweGhGcw3CAIg1BVpDK0thYQYXJqwGURGAZCVhHKJ19QVKshTbvZuakQI0/wxl7TOMdb0Kwcf5A6gy819c0+kiE8nceqNeQckNICAmWM8ND0IfwG8kri6AyHLmnl3791BHW1hLHHiIwjyC0wkfI4YBHtmJKvk0ogfJBluPtC8bYj67jxpuXMn1sgnxti4RFF2mcVyMhOBoTkne9w+cwFbSAlHgKFinfJse8Wgi3thLCHozlNXmuYR2EJ8sqm7FV521E5crmgagOswG6SOkwa3LlTiw4plcC2XKQv0I5AKEPGCCADQoaV7rsRoD1AEYhkBSM0PjVkFeSMR61dQEvwbAW2j6VsFD7GhEu6kDgKHGnIWJlwrQqegyBPoIw2TuLJjmLWInEbMiTv5/7K7XmtcMD+LdsMjCBpdpb4anykcVAC6ooqaGgmAKwgWmN8MEGZZsGpwR1SeNpDCQmuQNqR/gy4GYLeGDuvIRkCmYiYCJrwzYdw8YUgX+shKCMRFBsM+eLfyGQDV1awYRCBOxtDU2MJW22jsTHD0sObsRXhrsEOOQZkzDNwoIYIIFJ/YSxSEi7WIrYPBCTip8Hni7VwxIoaanKCXMaiuTVPQ4OFIqi9XrTAYdGiAiZslSuQcXxVxAeVBK7vNy6B8M2B2Hn90TwLQRzUVgJbWbiuR8GuhgtQmEghgLi74MjkimAKBZYNtgEdfiRghQzrCYR+y05t5PgPLjAIoTTUK2bN7WLWLB8hwUgJ0oT5FmFWfih7lpJkrMAzYJQGFSQhCiOCfJjw2EFbcrUzP0AAUmBQGDMyy3+/XOcbUpWxKwQ7950mkgjzJgJpri04gWj6BIt6KNfGGKQQTDtYIux+hPTQVBEyCC6GWTOkS98rkXoG9jsMRg1jhEttrUDhg/BpqNd84UuHMn9OHmFkoARQCPJIITnn3DZOXpNHWhVkVEI44bHT7S0keC5YFmSVIWeDJYKFPZMLeiAL6QYMjcJ5rYNOXITTaTs25e5KQKE7Bp0vpcCyAz59GeYKvGVX/11BBMyKaAdLCOYvKHDDr06kWG8hpY+FQgod0gYH5QWRSVVTkyFXsFAOIAYD9jGdQwhJNJVhNOZNxa5KC9+YnfNoN76INwUIKBRspHFRYmeSrJCAMCjLcM657Wzv7gHRj6AmfC8kktP2zrSE5BnNK70EEwmpMbDfITBCYIRLTcFGCg9pytQVDStWNmJrFcu8QGIoYEnBIbNsDqYRQxU56jZNBAKMETCvvFZjIi6GLpTsI6tmYltB05JMxg74CLRB7GdylAMaBowR2JagXNFBdcAYvi6EQFkC340aQk00mCCpzwThkIzjc1C7CfJUdMA+qoSLlAZjJFGHQ4GgviFLbY0d5gxUgmTNOKVDQ9QWOXm2UYvwG8GMF+maiH0w2e11/1cZJQ0CHbj2NXi+olhfIVezFcTCgAsGMDrIK/CNT1OLYLDSidDNYCy0CZI7QQcEZCLMm0lcJ7DHnDJvBaTGwBsBnQEjaW6RNDdAvjDIlMlFMCaxG9OAQGgLhEbJoIuiMZnAMTZh41li1M+dJCAGyeLDHFzhBh3e5ABCOuTyeQRBKZKRE22+xoYgyU3guSasdBnDdyVIofFNQKo1KkOLN31bu98R5l1ILzCMCBNTkRhhYWGwlYtSBq0NIoihYIBCXlJX65O1DEbngiZGQByGEQF7467m0BiD53l0dnaOoFzeF7phdFlmRLpWrVbxfZ+Ojo74XKP7PyQX1IgSOvnZsSFMeDVBCAbhBf+MBTrLcasn0TLt7Ugl6eh+EUtLhJYYCb5RGKOxpaB7h4dkGMwwRvYEJbNeNahWUuX4bEmyqoaGBhxn4nkTU2PgDYDUEqWztLfbrDjcZsnb6lgwvwHb8sCPFISJ6rLCHIAgaSvumhfFzSeo1QrEXuhohyWV5B2nzeSQOQVyWXAyZYyoBuVxhqD2OMVrQsqA68L1zJg9A1IIpAo4HFTEuJdM1nwLwwgReqx8kP0Ik0XoPAiFCRN7HctGqh6EqA8WTgM6pBiur4e6um3YogVpGoMqARmVCdtE3Q13FR4UQjA4OEihUKBQKOwznRAt5sneGUlmR8/zRtDz+r4f9yiIGhdFTYwsy4qPs2fjizwDVlgmoAl29gKMjZ0T5BoEk6dI8k4RaRSYLCbSl8anqVqP7eQiziaEKIAYDO6TcTBypGfFGMPg4CClUgnbtvdgzAc2UmPgDYASYCHI5zQLlnazfGWWbNagqSKlExBqxIltUY12KIwisrzf2sr1dWF2/ox0i9EGyzY4Tj+F/CSkKCGFS6EmrClOJCFO+Pl7FQTlbYZKRYc0zWP4rgRLaVwRhBgmWqmWDnkwJGEYwAcsl2D1CZJ7GxsFk6cUwph2+HkD0oJPXDCHWfNtZEh+ZWQVHxtFFvB2mRic3JFns9kRnoG9NQqi9u0R3XAyJBD9fTQLYXTuyCiIPqe1jpn7kn8fG0xoCIgwIWBn9r9ta6QcRokapMiGKYYWvgYjfJQEhRVUXKlKkIBpLBC5ILkTwpLPndBa4zhO3PVyonlhU2PgDYDvD2NMGWkU//iFRTg5G4GPNlW0CBb9nS2IqwihEZF7TA4TGAYTz231aohYGYQEoatI4ZLJEJTFmTK5vA7dipEpkCZf7hJh3wZLGapVFynDXd9uGk5CRHkbhJneyW++1RVpYLRrBIIcwtjholXCCIkWRQya+gZDXQNx+CC2mYTmqGOzKGsQIWpAesAghjo0CsXInenoqoHI0wChJzEmltpz4zfq5Lr52c3MnjMbKSVPP/00Bx10EL/5zW845ZRTXjGmn/3sZyxcuJCFCxfGRoTv+zz++OO8/PLL8WdbWlpYsWLFWEcUnSmRmxpwZkghEb4JmQcj74Sht6eXnr4OZsyYRhDGcRH0YSgEmy4Uu2rrNrqnxUREagy8AZCWjZQOUtnUWtEOzAqUQOjWMiIS/bDsJRbKkKJsQkK84tfgR9i8CAHGwtcOVRWQj+RkmdqCTxUbSw2iyJEaA68Gg4UmZ4FXtoKKAKlBhwx4r6MXBWArEfbKMEGQIDQwgq/6vJXnPipQE2TZGWMpxBUDrnbRTh92JvycJwLRVT5oiRJeaCNElUQ1WK/BjZ1cqLQBTyqMB7JaxUgf42g8oVBGovygic+r2gRGIDT42kfYQazdSMFwuczVV1/NN77+dXztc9VVV/Gv//qvtLe3I4Tg+eefZ/PmzTQ0NNDW1sb27dsRQtDd3c2iRYuorw9CIvl8nkwmw/e+9z0++tGPMjQ0xLp162hra2P+/Pk89dRTlEolyuUys2fP5qmnnmLOnDk0NTXx5JNPMjQ0SD5fYOHChXiez6OPPoLvexx++DK6uzvYurWDTZuep672CDZvfoZMJsvSJUtZf/c67r3vbj7ykQ+Ry9TQfsg0sjmHzc8+S1vbQTz//HMMDvSzaNGhPPf8cwwMDHDooYfS2NgYTMsE8wYkkRoDbwAC/oCAjDzgBYgebHvUIgevVJ4TL3Y1EqMVWsywH7v6pDQIy6CE5thjajl0WQ4fgzIBi2F0mBQjEcihQQLVctiUK+rySJRt/erKMUg+1EglwtuUnOSIMOqtCRFXwEPQnZFQxpz410qlAngoKQgaNxESDPkE+UBB/JuwdFgI61XFdLTbWqCx9SADz3XQ/eenyAwPg20wKuTo9yTGKocEUq+Epxzy85bRMn06xoCvDUYKtDH09vZy1513UvWDJMVKpcL3v/99vvjFL3LppZfy9re/nauuuooPfvCDeJ7Hn//8Z3p7e/nTn/7EhRdeiJSSWbNm0d7ezi233MKiRYu46qqrOPzww7npppt45zvfydVXX828efPo7u7mpptuYsWKFdx555185jOf4X/9r//F2WefzSOPPMKZZ57Jpk2bGBgYwLIsnnrqaQqFAr/+9a8577zz6O3tolwus2HDBjo6dqAsiWXZZLMFbvrlTZx11lnU1dXxgx9cx1lnncXXvvY1Pv7xj/PrO37Ntm3baGtr449//COf+cxnJmTSYBKpMZDigEW0AwsYXQ2Tpw1z8MEV6us9MlIifRkQkrx1N6d7BwNSSJQyVCthd7ZdlHG+GoLkQw/Lihq/hEl1RiSMiokLYQzKSIQvAiGVhIvzK8sG9+DgSFy6Xn6Wv9x5G/XDLtL4+EIgjEL5EmSZZPlcEiU7Q7NdR/PMGSASNfwYLKWwlELZdpyfANDd3U19fT2nn346L730ElprLMvipJNOYsaMGVx22WWx0ZLMD3j55Zf505/+hO/7DAwM0NvbS7FY5JRTTmHbtm088sgjvO997+N3v/sd/f39tLe38+53v5v6+noee+wx7r77btra2rBtm4aGBorFIscffzxHHXUUDz30EA8++CBbtmyhWq1y7LHHMm/ePObOnYtSCtd1Y/e/7/ssW7aM448/nosvvhitNd3d3SNKJicyUmMgxYELA0YLjGuRzwr+v68fiaUclJZgNMJEfPypW2DXCBYB2wkS4JSVcFHvRp9XIUEqD8sKXeQjXKzpnNfmfPpFJXAEqMAuhaBR2V47TYwE8kxfcTzTlx0L2g2jiQJ8GfyLDJBdQUiw7CDzwZjYIJBCUlNTw7HHHYdUkrvW3RVXCeRyOfr6+ti+fTvPPfccs2bNQkqJ4zhxvkDU9j3pxWhoaOCggw7iox/9KFpr6uvrWb9+ffx+LpdDKRWXKXZ2drJlyxaeffZZZsyYwSGHHMIJJ5zAvHnzqFar/PGPfySfz+N5HrfccgurV6/m+eef5/nnnyefzzM4OIgxhmKxyIsvvsjWrVsZHh6Or8G2bWbMmMHUqVM56qijJmz1wGikxkCKAxoiDLsoWWXhoTYYO8xsD2PfKV4TQoC0K0gFQvmJRIGoNe+rL+pBAqGPsuNGu6M/sT+GfEBACJg8xaWzc0eQOqENKB+NT0Stu1fHRyA8B3IBH7SvoCqC4IwCLLPTUbMryLDgBm2QMqBJ1lqjpGLx4sUoy0JIwaJFi8hkMhx66KFMnjyZ4447jmuvvZahoaF4Ua2vryebzbJgwQJgZPXAoYceSltbG3/3d3/HddddRzab5QMf+ACLFy+mWCxSrVY56KCDMMZw2GGH4TgOjuNwyy23YNs2xxxzDAsXLuTGG2/kwQcf5JhjjqGtrQ0hBLZtc+qpp7J+/Xrq6+uZN28ec+bM4Z577uEXv/gFq1ev5le/+hUtLS0sWrSIuro6Zs6ciVKKs88+mxtuuIG1a9dyxBFHMH369L27IW8BpNoyxQENIcDIEgiN1LmQAGYItA26ENR1pngVGITSzJjVjOPswM5E3oDd6+gmBVi2H9eeEzHqpADgE5+aw5a/TUGpYKGVlsGEJXJBQ6I9N5eMMJQdF2krbAxKC7Im+LsW4IsgJVG8VthHgAgaJxD1Wsjnc3z43I/EbvMPfehDCCE499xzY56BKVOm0N3dzcKFC2ltbQ2+LwQf/OAHY5Ki6G8f/vCHkVJyzDHHcMwxx8RJkGeffTZCCFpaWliwYAFKKT7+8Y/T0dFBa2srn/nMZ2IvQ21tLf/0T/8Uu/qTOPLIIznqqKNihkQhBJ/73OcAKJfLfOlLX4rHJIRg2rRpQFDd8OlPf3oEUdJER2oMpDhgYcJyNiOC6ow4i8uEpZiCUQlxKUbCgPA5ZGYR29mBPdb8KQHaVCjUNCCk2VmyFdsUe7HaHfAwtE93aG9vCOiKpQhTKiU7k1j2PIRlMLiySjVsImULEDrwkkkBPgLlW6+aumGiittk9Z4x2LYdEwgBcbzd8zyklCxevJgpU6Zw+umnU1dX9wqWwSS3QOT6H10SmaQ4jhb86DsNDQ1ceOGFWJYV5ytEn4/GEI0pOofv+zHfQvT3KJ8hOn70HaXUiLyGyFCYcBTvu0BqDKQ4oGGEQJtMQPoi+8HkwS+E9AJVgjLNVMx3BYMJPCqWQUqPbC7z+l9KQFlQKIAyVni8RL5B0skwEXWsAERfsCDpbOgxiVJe9x7SCAplB0c5eAoqQqBUEB5Ah+aGMuhXSSDc6QsIWzET5AsQL4ojb1y0UE6aNImWlpYRi3g8psSiGn0+WqRHL9hCCCzLiomOhBC4rott2xx00EEjjhnt+CNDQ0qJ53kx0VFysU/mLLiuO2Lxj64jCmPEtOZ7RIj01kM6AykOaAR7LY1AAtkwqy1KnFIE/O4pdg2FZzJYGYMtSxTsbFAOaACjXlc52BJqsoNodxvakxhjAwYjhoKlRssJaQdEcEQeJRyEUqCCNsRhM+J9cvyoc6/CoKgiCLqjhrw8QYvj8N1X/hMIPCSR213g+yagTNY+CB38ZOcuP7nwRmyEo9kPk4t7smeCUuoVu/fo79F3LcsaEWLwPI8kkueIjhGFDXaVvKiUGtF4KAol7KoTY4rUM5DiAIaI/4UERBFLo4reTcX7NSGC/1k2WDhkHIuI7zlgFPRfkytACFh8aC3VskW0sTLCIKImMxN4rxEkCTrJPyR/7H0xAQasKogMUqugs6k0QBWUB1IRNOt61QGCUHFOgSHoTVEul3l601PMnTsXx8nEO3p4ZQOiaGcd/fQ8jwceeIDe3t74bwsWLGDWrFkIIahWq1hW6EVKhAqSRkay8VGSahl2GgNRn4TIIPF9nxdeeIFsNsukSZPiMXqeh+M4IwyGZGggWTaZhglSbZkixQRG4FVxbIGlJNlcyP0uBEHv9yqvVqcOIJXhY+cdhtFgWZVg16WyCGGPICae2Cp2/0AA+Jo4W1BI0BmMckD4mKjHyatOvkSZ0JwWYWxB+pSGh/jRj67ja//n3+OF+tFHH2Xbtm0sX74cYwyVSoV8Ps+LL77I3LlzefbZZ1m0aBFKKdra2ujt7eXXv/41H/vYx9i2bRubNm1i0aJFTJkyhY0bN1KtVsnlcjQ1NfH000+zcuVKXNflhRdeoKuri0MOOYRZs2YxMDDAhg0bqKur47DDDmPz5s2Uy2WMMTQ3N/P4448zbdo0Fi9ezNq1a6mpqeG0005Da828efPo6OiIvQg7duxgaGiIRYsW8dhjjyGlZMWKFeTzeYARvRQmKiau6Z4ixYRHsIu3LYOlDJlMRB4Ewf/81/y2QFPIuxRqfKTxsGTgt9Y4AXeh2L2qhBRjh0FhTA3ak1RcTalqGHJ9BiuagbJicNjGHVJ4g6/2D7RrMDHlgU/QFVBjW1lAoKRi48aN/OIXv2BwcJD//M//ZMeOHdx+++3ccccdXHLJJWzevJm77747dtHPnz8/LidsbW3l7rvvJpPJcOWVV/Liiy9yySWX0N3dzZVXXsnNN9/Miy++yM9//nM2b97MN7/5TbTWXHHFFWzbto3LLruMarXK3XffzUMPPcRPf/pT1q9fTz6fp7u7m1wux09/+lOefvpp8vk8jY2NFItFrrvuOoaGhnj44Ye55557uO+++/jxj39MNpvlxz/+MV1dXWzatIlbb701mMswXDHRkXoGUqSYqDASoyW2Asf2qavLEFPsGguE/TpLuUCJkLZY5gNvgAEjFEZ4BMZGSuayv+AZwf/c/Dj/ecmf8SrT0SJH0PhYYhDh/3cNKbv4h0/YfOSjxyBU4BUwRiNEQKMsBUileOKJJ1i9ejVHHnkkv//97ykWi7zwwgvU1taycOFC7rrrLhYtWjQiCS+K2//lL3/hiSeeiBsg9fb2Mm3aNN71rnfx3HPPsXz5clpbW7nuuus49NBDWbZsGSeeeCJPPvkkjz/+OA899BCu61Iul+nu7sZxHE4//XTmzp0bGwHPP/88zz77LFOnTmXmzJlMmTIFIDZOlFLYts1xxx3HggUL+Na3vsX8+fPRWjNr1qy43XKK1BhIkWICQyCNRcap4NQ+x6TWBWEpmsFoAcrmNUmH4kbxbqKMMGg7KzAI4bGz5DPFPoXQeFaF5atmcMm8Kfieg0EGPANhJo2tX4OAkOm0tQh8TUgnbaFN0HfS9106u3qwHUlLSwtPPPEELS0tuK5La2srrusyMDDACSecwH/8x3/w7ne/+xXleVpr2tramDdvHueddx79/f0Ui8URpX5SSizLwvM8jDFs3ryZrVu3snXrVk488UTa29s588wzqampIZfL8dhjj2HbNp7ncf/993PBBRdwww03xAt6Z2dnHBZ48cUXefzxx2lsbCSTycRkRnPmzOG0005j6tSpI/IGkomGExWpMZAixURFUFlI2xSLf//WMTQ122H8uAJkw5r41/x6+P8KGCf8bhVhMkEVhyERdkixT2HAFpLJzXlaG/NhG2kd5npEFQMer5bzIcgEuYYqaJpkjEIKiaUqTGpr5sq1/xXvxDs7O7ntttv4yEc+guM4rFq1CqUU8+bN4/jjj2fq1Klx0p4xhmw2y5IlS1iyZAkvv/wyP/zhD5k6dSonnXQSy5YtA4gJi2pra1m6dClSSgqFAjfccANHHnkk8+fP5xOf+AR33XUXSilOP/10FixYQF1dHZZlceqpp3LnnXfS3t7OwQcfTHNzM9dddx3FYpEzzzyTO+64g2KxyJw5c7Asi1wuRzab5YILLuCmm27C8zxOOeWU2JOQAoRJayt2CWMM27Zt4yc/+Qmf/exnx2Q1JqfUGMOmTZuYPXv2LutwxwOS4y2Xy7z00kvMnDkzfm881+BqrXn66aeZO3fuCKKU8YLRspAc65suCwaMZ/BkiapQKOGQQSMYwuhajNQ888xfmX7IjJi7PTnW4NJcoBdMMwgfQTlgfoxKPYIv7Z/hj1Jdf/vb32hsbKSmpubNn9vXgTGGHTt2xCx88NpyO/pau7o7yecNuWwt2ncQRiCkAOGHbFxyJwnUriAMvq4gpcIYhSDYGUf5A8YERkJ0E6Myv4iUKKkTouS7ZJlh9LdkKV9UKTB6R26M4Q9/+AO/+93vuOiii15RQZDcuSerG5LNhZIeCQhCFeVymUwmM+K8SarkiKsg+jzA8PAwpVKJpqYmtNZ87Wtf45//+Z8pFArjVpb2FVLPQIoUExkWKByyRApVBsRNUoTcDa+nABWI4s7fye5smJNinyJZHx+ULmYAFbSQ1pEHZmc7ZfGa90BgWZnwuDuNppCGiKQhkGT6i3oCJEMCyeS7ZL3/6PdGJ+lFr4UQHHbYYcyZM2eEkZE06JJcBBGSnASjjb9ozFH5YfLv0e/JlsWREZH83ERDagykSDFRES4YI5P8gjI1QbDzf60FJdCbEsgk1v5UpewvJD1fxoDRFmCHO+c9Mb6ixfQVZ3rFeZMLbWQU7GqnvKe751wuRy6X2+WxXu2Yyb+P/mzkARhhQL2OpyhJkZwkTZooSJ/cFClSpBjnGL042bZNuVwesbve127s5PE8z4td/pGXYDyHDyM64oiyeHfmxhjD8PDwhG1nnBoDu0CSRvOtjt25xvHMznUg36PxPK+vhgNxzAcKxrJoFQoFurq66OnpeUW8fF8hOZZKpUK1WqVYLI7IARiv8H2foaEhGhsb493+a403GYYoFAoHtF7ZU6TGwKsg6WLaW3aqqClGEuNVoY5WLOOdkCOa2yhJaDwnjcHO8e4qhjkekeRzH+/JpMkGNNHr8Sy7UTJccse9O4ia9rS2tu6SrndfYPRx+/v76e3tpampKX5/PNfnR4mA9fX1I5IWXwvJZzHZt2A8Gz37EuP3br6J2FWP67FYiqMbdXieFxsVUZvQ8WR5jq4PjmhIR2fCj0dEmcbJBXY8La6jm6JEPOnjVRaSSPLHR3LhOM64Ge/o52z03EYu4vGMpFzsiaG1v2R9dFgiaWRFz9yBgKQeez1ja2SlzM7N4IFyrXuL1BjYBaKF2/d9HnvsMa655poxfz+5AGzbto0HH3wwjkWN7sb1ZiPZ6atSqdDf309LS0vcYnQ8W8bGGLZu3Up7e3usUKNFYTwgKQtaa7Zt28aGDRviXdV4k4XR2LZtG5MmTYozs8fTeKO5jQzBjo4OamtryefzSClxXffNHuJror+/HyEE9fX1wE4jZrwgqRdKpRLlcpmmpiaUUjFR0HiF7/t0dHQwbdq0EUbM7iIyHJ555plxJfP7E6kx8CrQWtPY2Mg//dM/7bHFHi0AV1xxBe9617vI5XKvmYn7ZiG5C+jq6uLmm2/mjDPOGOF6H6+oVqusXbuWd7/73XGb1PFmvETz6/s+l19+OWeccQa2bY/7+TXG8P3vf58TTzyRmpqaV3SRGw9IGt2//OUvaW9vZ/bs2bE3brzOLcD999+PZVkcccQRsRt7PM1vcjzPPvssjzzyCEcdddS41GGj0dvby0033cSaNWtiWuKx6AWtNbZtc8QRR8TNjN7qSI2BV4FlWSilWLhwITA2d1zSxeR5Hq2trcyZM4dMJhPH+8bTg5Qc77Zt22hra2POnDnATo7v8TTeJMrlMlOmTGH+/PnxWMeTW29XsjB37txYBsabLCShtWbq1KnMmTOH+vr6mPZ1vIw3uXBGY50xYwazZs0CGHeykIQxhpdeegnLsliwYAGe52Hb9riZWxgpu8YYOjo6mD17NjD+9UJXVxdTp05l/vz5IzgHxoLxZJi9EUiNgV1gX8Sek/GmaCc4HjE6N6BYLHLyySfH743Xhz2CZVmccsopseIfjw9wUhbWrFkzrpPakhBCcOKJJ5LP52PDZbwhyU539NFH09zcfEDILcC8efPiHfZ4S8YbrRcmT57M0Ucf/SaOaGwoFAqccMIJe2UMHggytC+R0hHvByQtaiB2B0dhg/G2u4pEYFfjjazq8TLe0UjGWcdjNcGBJAujkUx4TO4Cx8t4R89t8nWUqT+ePQNJqt4km954wGvphdG0wuMR410vjEeML3N0nCJZYlKtVpFSxlSWvu/jui62bcdxqWq1+orXWmsymUz80EfHdF0XrTWO4yCEeMVrGHuIIoLrurFrN9p5RH+LQhae5+F5HplMJlb+yWtMjtcYE78XdQ+LkrSklGQymfjcezLmaC6jtqNAPL5oLKNfm5AoRCkVX2dyDiqVCkA8tmq1ijFmn431tWRh9GtjDJVK5VVlIRpbdLxKpRIvFtH92tPxaq3jexXJViSb0byNfh1Bax3PeTKO/FqytDdjjeQMAnKd6D5XKpVYNkbLYjIUkzSykhU9SbmJ7kM0H0nynrGOF4if8aRcVqtVhBDx/Rwte9VqNU56jcYwWr5G65HkvdlbvRAdOzm2pB7yfZ9sNosxBs/z4nsyWnajsUX3xnXdOOlOKTWC9nd3x5wcb3Tvknphd2Qvqf+SVTCj782u7tVY5/etgNQY2A1Ei+S6devYsGEDlUqF008/ncWLF3Pttdfy0ksv0dzczKc+9Sk2bNjAddddx0knncSZZ57Jk08+yc9+9jN832fp0qW8+93vjhe8LVu28N3vfhelFCeccALHH3881157Lffccw9f//rXOfjgg1/RGnR3oLWmUqlw5ZVX0tfXB8D5559PtVrlv/7rv9Bas2rVKk444QR+8IMfsG7dOr773e/S3NzMLbfcwuOPP06lUuFDH/oQc+fOxfM8pJSsW7eOX//61+RyOT71qU/x0ksvsW7dOnp7eymVSnz7298eoWB2d6yRQrz++uv561//iuu6nH/++RSLRS6//HL6+vpYunQpZ511FjfddBM///nP+eIXv8jSpUt56qmnuPHGGymXyxx33HGsXr06nrOHHnqIm2++mUqlwkc+8hHa29v5z//8T7Zs2cIll1wSZ52PVan6vs+tt97Ko48+SqVS4f3vfz+zZs1i7dq1dHR0MHnyZM4//3zuvfderrvuOs4880xOP/10Xn75Za655hqGh4dZsGABZ599diwLzz33HJdffjlKKU477TTmzJnDT37yEwYHB3n44Yf5zne+w7Rp08YUboqUuJSSP/zhD/z617+mWq1y/PHHs2rVKn7yk5/w5JNPUlNTw0UXXcRTTz3F1VdfzaJFi7jggguAwCD59re/TU1NDRdeeOGI+PHll19OuVzmqKOO4tRTT+WnP/0pd955J1/+8pfjnvFj9Sr5vs/TTz/Nz372MzzPY/HixaxZs4a77rqL3/72t9i2zac+9SnK5TJr167Fsiz+7d/+LS4v/dGPfsRTTz3F1772tfi5GR4e5rvf/S79/f3MnDmTc845h5///Oe8+OKL/OUvf+Gcc87hHe94x5i9NNHcdnR0sHbtWiqVCm1tbZx//vn84Q9/4Fe/+hXGGC644AJqamr4f//v//HSSy9x+eWXY1kWX/7yl3Ech8mTJ/PhD3+Y2traeA7Wrl3Lli1bqK+v51Of+hRPPPEEa9eu5bjjjuODH/zgHuVvaK0ZGBjgiiuuoFKpkMvl+MQnPsHWrVu55pprEEJwxhlncOihh3LllVeyceNGvv/975PL5bj00kvZvn07ra2tnHPOObS1tWFMwDtx44038tBDD8Uy8vDDD/Pggw+yfft26urq+N//+3/HSXxjGWu0Abnqqqt4+eWX8X2ff/zHf8TzPK644gqGhoZYvXo1J554Ij/60Y+4/fbbufTSS5k2bRrbt2/nmmuuwfM8Vq1axdFHHx0bwOvXr+fee+/F8zw++clPks1mufTSS9Fa8+///u9xIvJEw/j0oY0zRNbvUUcdxec+9zne8573cO+997Jt2za2bNnCxRdfTKlU4oknnmD58uWcccYZlEoltNbMmDGDz33uc3z84x/nt7/9bey+FEJw6623snr1aj796U9z1113AfD3f//3HHLIIXtF9xkpig9+8IN8/vOfJ5fL8dxzz7F+/XpWrlzJRRddxB133EGlUuHMM89k1qxZ8S73pJNO4l/+5V9Yvnw5GzZsiBV6qVTi1ltv5Qtf+AIrVqxg/fr1LFu2jM9//vMcfvjhrFixYo8eoGR52GmnncaXvvQlFi1axJ/+9Cc2bNhAY2Mj//qv/8qDDz7I1q1bOeWUUzjmmGPiXcrs2bO56KKLOPnkk9mwYUO8u9Jac/PNN/OBD3yAs88+m1tuuSVWftls9hWNSXYXEcHRcccdx+c//3lOOeUU7rvvPl544QV6e3v58pe/zI4dO3jmmWfiRbJUKgEwadIkLrzwQs4++2z++Mc/jgjB/OpXv+L000/nvPPO47bbbqOxsZHPfOYznHXWWcycOZNJkybtUU5EtEguXLiQz3/+85x77rn89re/Zfv27TzyyCN8/vOfp1gs8vvf/5758+dzzjnnxLtmz/NYt24d9fX1uK47wg1/zz33sGDBAv75n/+Ze+65h2q1ynve8x4WLlxIuVyOF4qxIDK0DjnkED772c9y4YUX8rvf/Y7h4WHuuusuLrroIpYsWcK6deuYOnUq55577oid9IYNG+JzJ13ZjzzyCI7j8IUvfIFnnnmGvr4+PvCBD3DhhRfS0tLCkiVL9mpu6+vr+fSnP83nPvc5/vrXv9LV1cV///d/87GPfYw1a9Zwww03UFdXxz/8wz/EmelCCMrlMtOmTWPlypXkcrlYFp977jleeuklLr744tg4Wrx4MWeeeSZDQ0Px98cKz/PI5XKcd955fO5zn6Ovr48dO3Zw++23c8YZZ/AP//AP/Pd//3esO1pbW+PKgVKpREtLCytWrKC5uTleMDs7O3nggQf4yle+wkEHHcSDDz7IqlWr+Jd/+RfmzJnDUUcdtUdjTX7n/e9/P1/84heZNGkSTz/9NOvXr2fRokV88Ytf5Pbbb6e/v5/3vve9LFy4MPZK3HTTTUgpmTt3LjNnzozvb7lc5o477uCTn/wkq1ev5n/+539obGzkggsuGHdVSG80UmNgNxDFTWtrazHGcM8997By5Uo6OjqYMmUKuVyOadOm0dHRgW3bccJV5N7NZDI88MADLFu2jN7eXq677jrWr19PT08P06ZNo6WlBd/3GRwcpKamZoSre09SOiJFWF9fz7Zt2+js7KS9vZ1t27Yxc+ZM6uvrUUpRLpfJ5/NYlhUn4BUKBcrlMg8//DArVqzg0Ucf5cc//jGPP/44juNQV1fHjBkzePnll4FAwTz00EMcd9xxI2KfY0H04NfV1dHf38+jjz7KokWL6OrqYubMmdTU1MTv5fP52JUXJV7dd999/OxnP2Pu3Lk8//zzXH/99WzcuJHh4WGam5s5+OCD6e/vx/d9amtr4/PtyVgjhVEsFvE8j/vvv58jjzySHTt20N7eTi6XY8qUKXR1deE4TpwhHi2Of/zjH/ne977HwQcfTHd3N9dffz333nsvfX19TJkyhalTpzI8PBzzzt9///28/e1vj4+zJ4pVShnf5zvvvJPDDz+cSqVCU1MTxWKRqVOn0tHRQSaTIZfLxd6STZs28cwzz7B06VKq1Sr9/f3ceOON/M///A/bt29nypQpNDY2ks/n6e/vj2V3T8cZ7QYzmQz5fJ57772XhQsXUqlUcByHxsZGDj74YDo6OpBSxq2KATo6Orj77rtZtWoVvu9TLpe59dZb+cUvfsHf/vY32tvbqauro6Ghga6uLpRSccvj5ubmeJ7GgkjelVIUi0U2btxIS0sLuVwO13U56KCDaGtrY2BgIH62knNz7rnnsmTJEn7605/yxBNPsG7dOm644QY2b95MY2MjhUKB9vZ2duzYQTabJZvNxmPcE9mNZLC+vp5nn30WgMbGRrq6upg+fTrNzc2xaz2fz8fhCa01733ve1m9ejX33HMPDzzwABs2bOD6669n06ZNFItFamtrmT59Olu2bEFKyfDwMJs2bWLFihV7NbcADQ0NdHR08MILLzB9+nQ6OzuZPXs2xWKRTCbD8PAwhUJhRPngk08+GYdtrrnmGh577DGuv/56HnvsMbTWNDQ0MGPGDLq6uhBCUFNTM+I5nYiYmFc9RkQP/PDwMFdffTWzZs3i6KOPprGxkc7OTlzXpbu7m/r6+rjfdxT79zyPX/7yl/T09PC+972P2tpaDj/8cGbOnBnziw8ODiKEiJVItAPb09zOaGHfsmULa9eu5WMf+xgtLS00NjaydetWyuXyiLh1NE7f9+nr6+Oyyy7jxBNPZNasWbS3t3P44YfT1tZGtVqlUqnQ2dlJfX09xhj++te/ks1mmTx58h4rqOjcHR0dXHbZZbznPe9h+vTpFItFtm3bhud5DA8PU1NTg5Qy9gpEIYbVq1fzpS99id///vfk83mWLl3KQQcdhGVZlEoluru7KRQKcVw8+u6ezG+UVT84OMj3vvc9li1bxvLly2lsbGT79u24rktPTw/FYjFewKNz+b7PUUcdxVe+8hWee+65OHQ0ffp0crkcPT09DAwMoJQik8kwODjIo48+yhFHHLHHbssollqpVPjBD35AfX09J510ErlcjoGBASqVCr29vdTX18eyEI23u7ub7du385Of/ITf//73bNmyhUMPPZT58+dTX19Pd3c3pVKJSqVCbW1tPLcRg+Wezq3Wmttvv50tW7bwwQ9+kJqaGqrVKoODg3R1dVFXVxffy0h2u7u76e/v55prruGhhx7i0UcfZd68eSxevJimpiZ27NiB67oMDAxQLBapVCrccccdHHfccSPizGOB7/vxv/vuu4/f//73nHfeeWQyGWzbpquri4GBAXK5XGzgR583xrBkyRIOP/xw5s+fz/bt25k1axZLly6ltbWVvr4+PM+js7OTxsbGWB/sih10dxEZeU899RQ33HADn/zkJykWixSLRbZv387Q0FCcexMtjNH55s2bx5IlSzj22GN5+umnmTZtWjzWwcFBXNeNx+r7Pg8//DDt7e00NjbusdEd6YUXX3yR733ve3z4wx+mra2Nuro6Xn755dgLkOQBiGRwypQprFixgre97W309PRQX1/PsmXLmDJlSuwh6OjooK6ubsQzujd690BHmjOwG4gSVG666SbWr1/PCSecwL333suKFSuwLItLLrmESqXCwoULefTRR7n55pvp7u5mxowZZDIZ1q5dy8knn8zNN9/MWWedFdfqrl69mquvvpq77rqLI488Etu2ufHGG9m4cSNaa84//3ymTZs25oUgSpD6t3/7N2pra1m/fj1SSlatWsWVV17Jfffdx7Jly8hms9x88808/PDDXHvttXz4wx/mxhtv5C9/+Qv19fVks1mWL19OfX09WmtWrlzJt771LcrlMh//+McBeOCBBzjhhBPihMI9qTyIku2+/e1vs337djZu3BgTfnznO9/hG9/4RrzLuueee/jtb3/L5s2byeVy9PX18cc//pHBwUGWLFlCY2Mjra2tuK7L6tWrueKKK9Bas2bNGrTWXHXVVTz++ONce+21nHvuudTV1Y1pfqMkxuuuu44NGzaQyWQoFAoceuihVCoVvvWtb+H7PnPmzOGhhx7itttuo1Kp0N7eTj6fZ/369fGusampiZaWFowxnHzyyVx33XU4jsOxxx6LUoq//OUvtLe3M2nSpD1WUNHcrl+/nl/84heccsoprFu3jhNOOIG2tjYuueQShoeHWbNmDX/961+58cYb2bx5MzNnzuSUU05h5cqVPPXUU6xbt465c+fiOA6e53Hsscdy6aWX8vDDDzN//nyy2Sy33XYbv/vd72ImwBkzZowpxyFagB5//HH+7//9v/Ezc9ppp7Fs2TK++93vMjQ0xAUXXMC2bdv48Y9/zMMPP8wvf/lLzjjjDL761a/S09PDFVdcwfLly+NwUFNTE/fccw/f/OY3qauro7m5maGhIbZu3cqSJUuAnd60schuZDxt3bqVr371qxx//PHceuutrFmzhlWrVnHZZZdhjOHv/u7v6Ovr44c//CFPPvkkP/jBDzjttNO4+eabKZVKbNu2jRNPPJH29nYgSOa75ZZb+MY3vkGlUmHWrFk888wz3HjjjfT09DBjxgxWrVpFNpsdkyxIKRkYGOArX/kKc+bM4bbbbuPUU09l9erVXH/99TiOw6pVq9Ba87Of/YzHHnuMq666ig984AP85je/YceOHbz00kt89KMfZdKkSUyaNAnP85gzZw5f//rXKZVKXHjhhRhj+N3vfsc73vGO+HkZq16IPAO+7/PVr34Vx3HinJFVq1Zx+eWXs2HDBpYuXUqhUOC2227jT3/6E9VqlY9+9KO8853v5Ic//CEPPPAAK1asYOrUqbEeX7lyJd/5znfiXKJSqcTatWt57LHH+NGPfsTf//3fx16ciYS0tHA3EGViDwwMUCqVYndzQ0MDQ0ND9Pf3UywWyefzDA8P093djTGG2tpabNump6cndlk1NjbGCtL3ffr7+6lWqzQ1NWFZFtu2bYvj983NzTiOM6ZEocia11qzY8eO2LJvbm4ml8vR3d2N67o0NjZiWRY9PT0MDQ0hhKCpqYmhoaHYc1BbW0ttbe2IjOOenh4cx6GhoQHgFa57GFs71WSW+Pbt2+OxNzY2ks1mGR4eZnBwkPr6ejKZDAMDA/T19aGUinezvb29CCFoaGiId3mu6+K6Ln19fUgp40V3x44dcW16U1PTiIqJschCX18f5XIZ3/cpFos0NDQwMDDAwMAAdXV15HI5SqUSnZ2dANTX15PL5ejs7EQpRUNDw4gM58gr43leLAvDw8OxrO3p3EZZ3r29vQwNDWGMIZvN0tDQgO/79PT0kMvl4t13R0cHSiny+TwNDQ1xAl6lUolZCKPs8aGhISqVSkxdHSWSRvcim82OaRGIxhvJWZQtHoXRurq6yGazFIvF2JMUxcGjBjpCCLq6umhsbIxfa60plUoMDg5SV1dHPp+Pn73oGiOMRRaiyoVKpRK7m4UQsfHW29sLED8r0fPoOA5NTU309/dTKpVieYm8B67rUi6XY1kqFArxOQBqamqora0ds16Ixrt9+/b4GWhpacG27VhOGxoakFLS2dkZV+m0trZSKpUYGhqKzx3NLQTEX729veRyudhj2NfXF48xwlhkN3rOhBDxeCM9Zts2Q0NDlEqlWJ/29vbS398f69hsNhvPd/SdSC9Uq1X6+vqwbZumpqZ4TiDgLWlsbBxRzTVRkBoDu4EkP3+0G4iUxuimKMnEvyQhSvSQRwtrlOiWdM1Gn03uUqISoD0xBkYrtujYyfNH493V56PyoKTbMPkzQpRkmDzPWB76KEkpSq5MHiN6L0q2S9a9R9cbnT+az4iKNpnwFn0+GQeNjjkWz0CSk313ZCE5ZsuyYkNv9FxH44yuJclHkCzTG6sxEN3D5D2Kvp90iSavIZqn5PxGcxjNbfTZZP5F8hjRvIzFGIhkYVdzm7zPSblIPjfJce0qITD59+QcJOVjrNUEo8+RPE40xuTraJ6Sc5eU1eR9j8aXLCWM5CrKl9lbvbCr5yQaR/J5H52rMJqSeFcVT3ujF6KxJuctKf+vpRcij0Lyu8lrTMp9Uh9Hchu9nmi5A2mYYDcQxTIjgUsu2Mns9eSiFCGplKKHLSmsSUUbfT76++iHaazjjR780UobdiZFJhfR6MFKlqRFCj25wO5KQYy1/DE51miM0QOYVIRJJZBcKKOFKXlN0dijYyaNn+hYoxXLWDF6/l5PFkbLRPLcuzJeXs0w29NdSiS3u1qUo3FE54RXdgJMykXSSN1VSWby/T3tETFasSefj+h8kWxG1zB6QU7KenQvRi/4SVnelYG7O4iOn5TT5EKTlIno86MXptHjH/0ZGGkoRs/jnjTjGj0Hu3oWdjX30etoLEndsCujYF/ohaRhuSu9sKtnPGnIREjK/+gNV3K8ybmYqPvj1DOwGxjrFI0WqNd7vTvfHwvGcuzXw548HPtyvHuD5Nhf6zrGMt43+3HZ13M72mBMJlJZlhXHe6PPRGWrycVsX433QJtb2HvdsDd4M/XCnuCtrBfeCkiNgRQpJiiMMWzatIl169aRyWQ45JBDWLlyZdykSwhBtVrlxRdfxHVd5s2bBzDCkzCeqapTpEix+0jDBClSTGBEdddHHnkk69at45FHHmHNmjXcd999tLa2smrVKm677TYeffRRzjnnHA4//HDuvvtuXNfluOOOY/LkyW/2JaRIkWIfYGJlSKRIkWIEhBC0trbG7IMRxfKCBQt48sknWb9+Pe3t7SxevJjFixfzwx/+MM5K/+lPf/qmu/ZTpEixb5B6BlKkSAHsTIbcuHEjmzZtorOzE8dxmDNnDk1NTdTW1vLII4/Q29uLbdtMnjw5NQZSpHiLIDUGUqSYwIjq4V944QXuvPPOmDhrzZo13H///RhjqKmp4c9//jOVSoU5c+Ywf/58Zs+eHdNYp0iR4sBHmkCYIsUEhTEB29/tt99OPp/n4IMP5thjj+X555/nvvvui/tmLFq0iF/96ldMmjSJZcuWceeddzI4OMjb3vY2DjvssDSBMEWKtwBSYyBFigmKXZHcRJzwUfngaJ6L0eRIe8rXkCJFivGF1BhIkWICY28f/9QQSJHirYE0ZyBFigmMdDFPkSIFpKWFKVKkSJEixYRHagykSJEiRYoUExypMZAiRYoUKVJMcKTGQIoUKVKkSDHBkRoDKVKkSJEixQRHagykSJEiRYoUExypMZAiRYoUKVJMcKTGQIoUKVKkSDHBkRoDKVKkSJEixQRHagykSJEiRYoUExypMZAiRYoUKVJMcKTGQIoUKVKkSDHBkRoDKVKkSJEixQRHagykSJEiRYoUExz/f3ISgFDHFFT6AAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -450,102 +499,112 @@
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCritics\u001b[0m (to Commander):\n",
       "\n",
-      "The provided figure shows a time series plot of high and low temperatures in Seattle. Here are a few suggestions for improving the plot:\n",
+      "The plot depicting high and low temperatures in Seattle can be improved in several ways:\n",
+      "\n",
+      "1. Date Axis: The dates on the x-axis are not visible or readable, which makes it hard to associate the temperature data with specific time periods. Consider formatting the date axis so that the dates are displayed at a readable angle or with sufficient spacing.\n",
       "\n",
-      "1. **Resolution and Clarity**: The image appears to have low resolution, which makes the text and lines a bit blurry. Increasing the resolution would enhance clarity.\n",
+      "2. X-axis Labeling: There appears to be an issue with the x-axis labels or ticks as it shows a dense black area, indicating overlapping or condensed labels. The frequency and format of the labels should be adjusted to prevent overlap and allow for clear reading.\n",
       "\n",
-      "2. **Color Contrast**: The colors chosen for high and low temperatures (red and blue) are good for contrast, but ensuring that they are distinguishable by all audiences, including those with color vision deficiencies, can be useful. Consider using colorblind-friendly palettes.\n",
+      "3. Y-axis Range: Consider starting the y-axis at a lower value if the temperature data permits, to give more space to the temperature lines and improve readability.\n",
       "\n",
-      "3. **Line Thickness**: The lines look slightly thick, which can cause them to blur together when they cross or when viewed at smaller sizes. Thinner lines might provide better clarity.\n",
+      "4. Gridlines: Adding horizontal gridlines can aid in reading specific temperatures at a glance.\n",
       "\n",
-      "4. **Smoothing**: The data appears to be quite noisy. If the aim is to show trends, consider applying a smoothing filter or showing a moving average to make the general trends more apparent.\n",
+      "5. Legend: The legend clearly indicates which color corresponds to high and low temperatures, which is good. Ensure the legend does not obscure any important data.\n",
       "\n",
-      "5. **Legend**: The legend is clear, but it is positioned in such a way that it could potentially cover up data points. Consider placing the legend outside the main plot area or making it semi-transparent so that no data is obscured.\n",
+      "6. Resolution: The actual resolution of the image may be fine, but the x-axis issue might give the illusion of a low-resolution image. Once the x-axis is corrected, the overall clarity of the chart may naturally improve.\n",
       "\n",
-      "6. **Axes Labels**: The font size for the axes labels and the title could be increased to improve legibility, especially if the image is scaled down.\n",
+      "7. Color Contrast: The blue and orange color choices are generally good for contrast, but ensure they are differentiable to all users, including those who may be colorblind. Consider using colorblind-friendly palettes.\n",
       "\n",
-      "7. **Grid Lines**: The grid lines are helpful, but if they are too prominent, they can distract from the data. Lightening the grid\n",
+      "8. Title: The title \"High\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCommander\u001b[0m (to Coder):\n",
       "\n",
       "Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\n",
-      "The provided figure shows a time series plot of high and low temperatures in Seattle. Here are a few suggestions for improving the plot:\n",
+      "The plot depicting high and low temperatures in Seattle can be improved in several ways:\n",
+      "\n",
+      "1. Date Axis: The dates on the x-axis are not visible or readable, which makes it hard to associate the temperature data with specific time periods. Consider formatting the date axis so that the dates are displayed at a readable angle or with sufficient spacing.\n",
       "\n",
-      "1. **Resolution and Clarity**: The image appears to have low resolution, which makes the text and lines a bit blurry. Increasing the resolution would enhance clarity.\n",
+      "2. X-axis Labeling: There appears to be an issue with the x-axis labels or ticks as it shows a dense black area, indicating overlapping or condensed labels. The frequency and format of the labels should be adjusted to prevent overlap and allow for clear reading.\n",
       "\n",
-      "2. **Color Contrast**: The colors chosen for high and low temperatures (red and blue) are good for contrast, but ensuring that they are distinguishable by all audiences, including those with color vision deficiencies, can be useful. Consider using colorblind-friendly palettes.\n",
+      "3. Y-axis Range: Consider starting the y-axis at a lower value if the temperature data permits, to give more space to the temperature lines and improve readability.\n",
       "\n",
-      "3. **Line Thickness**: The lines look slightly thick, which can cause them to blur together when they cross or when viewed at smaller sizes. Thinner lines might provide better clarity.\n",
+      "4. Gridlines: Adding horizontal gridlines can aid in reading specific temperatures at a glance.\n",
       "\n",
-      "4. **Smoothing**: The data appears to be quite noisy. If the aim is to show trends, consider applying a smoothing filter or showing a moving average to make the general trends more apparent.\n",
+      "5. Legend: The legend clearly indicates which color corresponds to high and low temperatures, which is good. Ensure the legend does not obscure any important data.\n",
       "\n",
-      "5. **Legend**: The legend is clear, but it is positioned in such a way that it could potentially cover up data points. Consider placing the legend outside the main plot area or making it semi-transparent so that no data is obscured.\n",
+      "6. Resolution: The actual resolution of the image may be fine, but the x-axis issue might give the illusion of a low-resolution image. Once the x-axis is corrected, the overall clarity of the chart may naturally improve.\n",
       "\n",
-      "6. **Axes Labels**: The font size for the axes labels and the title could be increased to improve legibility, especially if the image is scaled down.\n",
+      "7. Color Contrast: The blue and orange color choices are generally good for contrast, but ensure they are differentiable to all users, including those who may be colorblind. Consider using colorblind-friendly palettes.\n",
       "\n",
-      "7. **Grid Lines**: The grid lines are helpful, but if they are too prominent, they can distract from the data. Lightening the grid\n",
+      "8. Title: The title \"High\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCoder\u001b[0m (to Commander):\n",
       "\n",
-      "To address the feedback and improve the figure, I'll make several adjustments to the script. The improvements will include:\n",
+      "To address the feedback received on the figure, we'll make the following improvements to the plot:\n",
       "\n",
-      "1. Increase the resolution by specifying a higher DPI (dots per inch) when saving the figure.\n",
-      "2. Use a colorblind-friendly palette for the high and low temperature lines.\n",
-      "3. Decrease the line thickness for better clarity.\n",
-      "4. Apply a rolling window to smooth the high and low temperature data.\n",
-      "5. Move the legend to an optimal location to avoid covering data points and make it semi-transparent.\n",
-      "6. Increase the font size for axes labels and title to improve legibility.\n",
-      "7. Adjust grid line style to be less prominent.\n",
+      "1. Format the date axis for better readability.\n",
+      "2. Adjust the frequency and format of the x-axis labels to prevent overlap.\n",
+      "3. Extend the y-axis range if possible.\n",
+      "4. Add horizontal gridlines for ease of reading temperatures.\n",
+      "5. Ensure the legend is placed properly to avoid obscuring data.\n",
+      "6. Check for clarity once x-axis labels are resolved.\n",
+      "7. Use a colorblind-friendly palette for the plot.\n",
+      "8. Ensure the title is descriptive and relevant.\n",
       "\n",
-      "Please execute the updated Python script:\n",
+      "Here's an updated Python script to make these improvements:\n",
       "\n",
       "```python\n",
-      "# filename: plot_temperatures_improved.py\n",
+      "# filename: improved_plot_temperatures.py\n",
       "import pandas as pd\n",
       "import matplotlib.pyplot as plt\n",
-      "import seaborn as sns  # For colorblind-friendly palette\n",
+      "import matplotlib.dates as mdates\n",
       "\n",
-      "# Ensure you have seaborn installed: pip install seaborn\n",
-      "\n",
-      "# URL of the CSV file\n",
+      "# URL to download the CSV data\n",
       "url = \"https://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\"\n",
       "\n",
-      "# Read the data from the URL\n",
+      "# Download the data\n",
       "data = pd.read_csv(url)\n",
+      "data['date'] = pd.to_datetime(data['date'])\n",
+      "\n",
+      "# Now, let us plot the high and low temperatures\n",
+      "plt.figure(figsize=(10, 5))\n",
+      "plt.plot(data['date'], data['temp_max'], label='High Temp', color='tab:blue')\n",
+      "plt.plot(data['date'], data['temp_min'], label='Low Temp', color='tab:orange')\n",
       "\n",
-      "# Extract the Date, Temperature High, and Temperature Low columns\n",
-      "dates = pd.to_datetime(data['date'])\n",
-      "temp_high = data['temp_max'].rolling(window=7).mean()  # 7-day rolling average\n",
-      "temp_low = data['temp_min'].rolling(window=7).mean()    # 7-day rolling average\n",
+      "# Set the x-axis major locator and formatter for better date display\n",
+      "plt.gca().xaxis.set_major_locator(mdates.AutoDateLocator())\n",
+      "plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))\n",
       "\n",
-      "# Plot the high and low temperatures using seaborn's colorblind-friendly palette\n",
-      "plt.figure(figsize=(10, 5), dpi=150)\n",
-      "plt.plot(dates, temp_high, label='High Temperature', color=sns.color_palette('colorblind')[2], linewidth=1)\n",
-      "plt.plot(dates, temp_low, label='Low Temperature', color=sns.color_palette('colorblind')[0], linewidth=1)\n",
-      "plt.xlabel('Date', fontsize=12)\n",
-      "plt.ylabel('Temperature (°C)', fontsize=12)\n",
-      "plt.title('High and Low Temperatures in Seattle (7-day Rolling Average)', fontsize=14)\n",
+      "# Rotate the dates on x-axis for better readability\n",
+      "plt.gcf().autofmt_xdate()\n",
       "\n",
-      "# Adjusting the legend\n",
-      "leg = plt.legend(loc='upper left', frameon=True)\n",
-      "leg.get_frame().set_alpha(0.5)\n",
+      "# Improving Y-axis Range\n",
+      "temp_min = data['temp_min'].min() - 5  # Give some padding below the min temp\n",
+      "temp_max = data['temp_max'].max() + 5  # Give some padding above the max temp\n",
+      "plt.ylim(temp_min, temp_max)\n",
+      "\n",
+      "# Adding gridlines and formatting the grid\n",
+      "plt.grid(True, which='both', linestyle='--', linewidth=0.5)\n",
+      "\n",
+      "# Providing labels and title\n",
+      "plt.xlabel('Date')\n",
+      "plt.ylabel('Temperature (°C)')\n",
+      "plt.title('High and Low Temperatures in Seattle Over Time')\n",
       "\n",
-      "# Adjusting the grid\n",
-      "plt.grid(True, linestyle='--', linewidth=0.5, color='grey', alpha=0.7)\n",
+      "# Adjusting legend position\n",
+      "plt.legend(loc='upper right')\n",
       "\n",
-      "# Increase tick label size\n",
-      "plt.tick_params(labelsize=10)\n",
+      "# Check done, no need to adjust color contrast as colorblind-friendly colors are used\n",
       "\n",
-      "# Save the figure with high resolution\n",
-      "plt.savefig('result.jpg', format='jpg', dpi=150)\n",
+      "# Save the figure in high resolution\n",
+      "plt.savefig('result.jpg', dpi=300)\n",
       "\n",
-      "# Show the plot\n",
-      "plt.show()\n",
+      "print(\"The improved plot has been saved as 'result.jpg'.\")\n",
       "```\n",
       "\n",
-      "This script should create a higher-quality figure with the suggested improvements. Once the script has run, please check the `result.jpg` file for the enhanced plot.\n",
+      "Please execute this script. Make sure that the `matplotlib.dates` module is available in your environment before running the script. If you encounter any issues, let me know.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[31m\n",
@@ -554,17 +613,17 @@
       "\n",
       "exitcode: 0 (execution succeeded)\n",
       "Code output: \n",
-      "Figure(1500x750)\n",
+      "The improved plot has been saved as 'result.jpg'.\n",
       "\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCoder\u001b[0m (to Commander):\n",
       "\n",
-      "The script executed successfully and created an improved figure with high resolution, colorblind-friendly colors, thinner lines, smoothed data, a better-positioned and semi-transparent legend, enhanced legibility for labels and title, and less prominent grid lines.\n",
+      "The improved plot has been saved successfully as 'result.jpg'. The improvements should address the feedback points provided earlier, resulting in a clearer and more readable figure.\n",
       "\n",
-      "The updated figure is saved as `result.jpg` in the current directory. You can view the figure by opening the `result.jpg` file on your computer.\n",
+      "The dates on the x-axis are now formatted for better visibility, the frequency of x-axis labels has been adjusted, the y-axis range has been extended, gridlines have been added, and the legend has been positioned properly. The colors used should be distinguishable by individuals with color vision deficiencies.\n",
       "\n",
-      "If there are any more changes you would like to make or if you need further assistance, feel free to let me know.\n",
+      "If no further improvements are needed, the task is now complete.\n",
       "\n",
       "TERMINATE\n",
       "\n",
@@ -573,7 +632,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -593,21 +652,27 @@
       "\u001b[33mCommander\u001b[0m (to Coder):\n",
       "\n",
       "Here is the feedback to your figure. Please improve! Save the result to `result.jpg`\n",
-      "Improve <img result.jpg>\n",
+      "Improve <img tmp/result.jpg>\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[33mCoder\u001b[0m (to Commander):\n",
       "\n",
-      "I'm sorry, but as an AI developed by OpenAI, I don't have the capability to view images directly or interpret visual feedback. My previous response provided an updated script intended to address the initial feedback points you provided.\n",
+      "I apologize for any confusion, but as an AI, I don't have the capability to directly view the images generated. However, I can certainly help you further refine the plot based on standard practices if there is anything specific that you feel needs further adjustment.\n",
+      "\n",
+      "You mentioned feedback for a figure referenced as \"<img tmp/result.jpg>\". Since I can't view images, I'm assuming this feedback is a placeholder. In case there are specific changes or additional improvements you want to implement in the figure, please provide me with those details explicitly.\n",
       "\n",
-      "If there are specific aspects of the figure from the `result.jpg` file that you would like to further adjust, please provide clear textual feedback about what specific improvements you would like to see. For example, you can ask for:\n",
+      "For instance:\n",
+      "- Are there any specific elements in the legend that still need repositioning?\n",
+      "- Should the date formatting be adjusted further for better clarity?\n",
+      "- Are there any other specific elements (e.g. colors, lines, labels, title) that require modification?\n",
       "\n",
-      "- Adjustments to the graphical elements such as line style or markers.\n",
-      "- Changes to the axes or grid to improve visibility.\n",
-      "- Modifications to the smoothing function or its parameters.\n",
-      "- Any other specific visual modifications to the plot.\n",
+      "Without specific feedback, I can suggest some general improvements, such as:\n",
       "\n",
-      "Once I have your specific feedback, I can provide you with an updated code script to address those points.\n",
+      "- Ensuring the plot does not appear cluttered.\n",
+      "- Checking if the aspect ratio is appropriate for the data being presented.\n",
+      "- Verifying that the font sizes are readable.\n",
+      "\n",
+      "If specific feedback can be provided, we can proceed accordingly. Otherwise, please let me know if we should conclude the task.\n",
       "\n",
       "TERMINATE\n",
       "\n",
@@ -616,7 +681,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -630,10 +695,20 @@
      "text": [
       "\u001b[33mFigure Creator~\u001b[0m (to User):\n",
       "\n",
-      "result.jpg\n",
+      "tmp/result.jpg\n",
       "\n",
       "--------------------------------------------------------------------------------\n"
      ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ChatResult(chat_id=None, chat_history=[{'content': '\\nPlot a figure by using the data from:\\nhttps://raw.githubusercontent.com/vega/vega/main/docs/data/seattle-weather.csv\\n\\nI want to show both temperature high and low.\\n', 'role': 'assistant'}, {'content': 'tmp/result.jpg', 'role': 'user'}], summary='tmp/result.jpg', cost=({'total_cost': 0}, {'total_cost': 0}), human_input=[])"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -654,36 +729,19 @@
     ")"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f0a58827",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "if os.path.exists(\"result.jpg\"):\n",
-    "    os.remove(\"result.jpg\")  # clean up"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c6206648",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "markdown",
    "id": "a95d87c2",
    "metadata": {},
    "source": [
-    "## Group Chat Example with Multimodal Agent"
+    "## Vision Capability: Group Chat Example with Multimodal Agent\n",
+    "\n",
+    "We recommend using VisionCapability for group chat managers so that it can organize and understand images better."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "56bd5742",
    "metadata": {
     "scrolled": false
@@ -695,7 +753,7 @@
      "text": [
       "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n",
       "\n",
-      "Describe the image:\n",
+      "Write a poet for my image:\n",
       "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
@@ -703,14 +761,51 @@
       ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
       "\u001b[33mimage-explainer-1\u001b[0m (to chat_manager):\n",
       "\n",
-      "In the soft embrace of a quiet room, a curly-haired sentinel of joy rests upon the floor—a puppy, with fur the color of sunlit autumn leaves. Its innocent eyes, deep and dark as a twilight sky, gaze into the world with wonder and gentle curiosity. Adorned with a collar that boasts a bright, mosaic-like pattern, the young canine wears a name tag shaped like a heart, whispering the promise of unconditional love and companionship. In the background, a pair of black boots stands sentinel, hinting at the return of a beloved owner, while the puppy waits, the embodiment of warmth and affection in an often too brisk world.\n",
+      "In a cozy nook by the door, where whispers of adventure pour,\n",
+      "Lies a curly apricot dream, with eyes that softly gleam.\n",
+      "Westphal, a name upon his tag, a dapper bow, no room to brag.\n",
+      "\n",
+      "A poodle or a mix so sweet, with paws that barely seem to meet\n",
+      "The floor that holds his tender frame, in this quiet household game.\n",
+      "A sentinel of love and joy, more charming than any toy.\n",
+      "\n",
+      "His gaze, a tale of gentle days, and love that in his heart blazes.\n",
+      "With boots behind, like silent guards, he watches without regards\n",
+      "To the world's rush and its tides, in his serene abode, he abides.\n",
+      "\n",
+      "Oh, little guardian of the hearth, your worth is not defined by birth.\n",
+      "But by the joy you bring to life, a respite from our daily strife.\n",
+      "In you, we find a friend so true, Westphal, a treasure through and through.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[31m\n",
       ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
-      "\u001b[33mUser_proxy\u001b[0m (to chat_manager):\n",
+      "\u001b[33mimage-explainer-2\u001b[0m (to chat_manager):\n",
+      "\n",
+      "A pup so young with fur of gold,\n",
+      "In a house where stories unfold.\n",
+      "Westphal, with your bow so neat,\n",
+      "Your puppy eyes, oh so sweet.\n",
+      "\n",
+      "Curled up on the floor so light,\n",
+      "Your coat a soft, delightful sight.\n",
+      "A mix, perhaps, of breeds so kind,\n",
+      "In your face, pure joy we find.\n",
       "\n",
+      "With a tag that jingles slight,\n",
+      "And a bow tie oh so bright,\n",
+      "You lie there with such grace,\n",
+      "A calm presence in this place.\n",
       "\n",
+      "Behind you, boots await the rain,\n",
+      "But inside, you contently remain.\n",
+      "A symbol of love, a furry delight,\n",
+      "A beacon of warmth, shining so bright.\n",
+      "\n",
+      "In a world that's fast and vast,\n",
+      "Your gentle spirit holds us fast.\n",
+      "Westphal, with your charm so grand,\n",
+      "You bring peace to this human land.\n",
       "\n",
       "--------------------------------------------------------------------------------\n",
       "\u001b[31m\n",
@@ -746,7 +841,7 @@
     "\n",
     "user_proxy = autogen.UserProxyAgent(\n",
     "    name=\"User_proxy\",\n",
-    "    system_message=\"Ask both image explainer 1 and 2 for their description.\",\n",
+    "    system_message=\"Desribe image for me.\",\n",
     "    human_input_mode=\"TERMINATE\",  # Try between ALWAYS, NEVER, and TERMINATE\n",
     "    max_consecutive_auto_reply=10,\n",
     "    code_execution_config={\n",
@@ -756,65 +851,299 @@
     "\n",
     "# We set max_round to 5\n",
     "groupchat = autogen.GroupChat(agents=[agent1, agent2, user_proxy], messages=[], max_round=5)\n",
+    "\n",
+    "vision_capability = VisionCapability(lmm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300})\n",
     "group_chat_manager = autogen.GroupChatManager(groupchat=groupchat, llm_config=gpt4_llm_config)\n",
+    "vision_capability.add_to_agent(group_chat_manager)\n",
     "\n",
-    "user_proxy.initiate_chat(\n",
+    "rst = user_proxy.initiate_chat(\n",
     "    group_chat_manager,\n",
-    "    message=\"\"\"Describe the image:\n",
+    "    message=\"\"\"Write a poet for my image:\n",
     "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\"\"\",\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d69b6c81",
+   "metadata": {},
+   "source": [
+    "## Behavior with and without VisionCapability for Agents\n",
+    "\n",
+    "\n",
+    "Here, we show the behavior of an agent with and without VisionCapability. We use the same image and question as in the previous example."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "aae1167f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "agent_no_vision = AssistantAgent(name=\"Regular LLM Agent\", llm_config=gpt4_llm_config)\n",
+    "\n",
+    "agent_with_vision = AssistantAgent(name=\"Regular LLM Agent with Vision Capability\", llm_config=gpt4_llm_config)\n",
+    "vision_capability = VisionCapability(lmm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300})\n",
+    "vision_capability.add_to_agent(agent_with_vision)\n",
+    "\n",
+    "\n",
+    "user = UserProxyAgent(\n",
+    "    name=\"User\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    max_consecutive_auto_reply=0,\n",
+    "    code_execution_config={\"use_docker\": False},\n",
+    ")\n",
+    "\n",
+    "message = \"\"\"Write a poet for my image:\n",
+    "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\"\"\""
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "00bd473d",
+   "id": "c9e81b7e",
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "defaultdict(list,\n",
-       "            {<autogen.agentchat.groupchat.GroupChatManager at 0x7f690545b820>: [{'content': [{'type': 'text',\n",
-       "                 'text': 'Describe the image:\\n                        '},\n",
-       "                {'type': 'image_url',\n",
-       "                 'image_url': {'url': <PIL.Image.Image image mode=RGB size=1920x1080>}},\n",
-       "                {'type': 'text', 'text': '.'}],\n",
-       "               'name': 'User_proxy',\n",
-       "               'role': 'user'},\n",
-       "              {'content': [{'type': 'text',\n",
-       "                 'text': 'In the soft embrace of a quiet room, a curly-haired sentinel of joy rests upon the floor—a puppy, with fur the color of sunlit autumn leaves. Its innocent eyes, deep and dark as a twilight sky, gaze into the world with wonder and gentle curiosity. Adorned with a collar that boasts a bright, mosaic-like pattern, the young canine wears a name tag shaped like a heart, whispering the promise of unconditional love and companionship. In the background, a pair of black boots stands sentinel, hinting at the return of a beloved owner, while the puppy waits, the embodiment of warmth and affection in an often too brisk world.'}],\n",
-       "               'role': 'assistant'},\n",
-       "              {'content': [{'type': 'text', 'text': ''}],\n",
-       "               'name': 'User_proxy',\n",
-       "               'role': 'user'},\n",
-       "              {'content': [{'type': 'text', 'text': ''}],\n",
-       "               'name': 'User_proxy',\n",
-       "               'role': 'user'},\n",
-       "              {'content': [{'type': 'text', 'text': ''}],\n",
-       "               'name': 'User_proxy',\n",
-       "               'role': 'user'}]})"
-      ]
-     },
-     "execution_count": 10,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser\u001b[0m (to Regular LLM Agent):\n",
+      "\n",
+      "Write a poet for my image:\n",
+      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mRegular LLM Agent\u001b[0m (to User):\n",
+      "\n",
+      "As an AI, I can't directly view images or web content. However, I can help you generate a poem by gathering information about the image. Please describe the image for me, including details such as the setting, prominent colors, the mood it evokes, and any specific elements like animals, nature, cityscapes, or people that you want to be highlighted in the poem. Once you provide a description of the image, I can compose a poem based on that description.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "user.send(message=message, recipient=agent_no_vision, request_reply=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "8720820f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser\u001b[0m (to Regular LLM Agent with Vision Capability):\n",
+      "\n",
+      "Write a poet for my image:\n",
+      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mRegular LLM Agent with Vision Capability\u001b[0m (to User):\n",
+      "\n",
+      "In apricot hues, a curl-coated pup reclines,\n",
+      "With Westphal's name upon his tag that shines.\n",
+      "A bow tie blooms in a brilliant blue sky,\n",
+      "Amid a light-drenched floor where soft paws lie.\n",
+      "\n",
+      "Possessor of gazes, tender and deep,\n",
+      "In the quiet foyer, his watch he keeps.\n",
+      "Beneath black rubber guards of rainy days,\n",
+      "He stirs a comfort, a homely embrace.\n",
+      "\n",
+      "His lineage drawn from the poodles' grace,\n",
+      "Or maybe a mix, with a doodle's face,\n",
+      "Gold or Lab, his curls are just as sweet,\n",
+      "In each bouncing step, in each heartbeat.\n",
+      "\n",
+      "A picture of love, in a tiny frame,\n",
+      "A heartbeat wrapped in an apricot mane.\n",
+      "The pup in his calm, an invite to cheer,\n",
+      "A whisper of joy in a pet-lover's ear.\n",
+      "\n",
+      "Around him, life's simple clutter does unfold,\n",
+      "Yet untouched by worry, untouched by cold.\n",
+      "With every breath, he claims this slice of earth,\n",
+      "A master of mirth, from the moment of his birth.\n",
+      "\n",
+      "Paws outstretched on the soft, forgiving ground,\n",
+      "His soulful eyes speak, without a sound.\n",
+      "For in the sweet stillness of his gentle rest,\n",
+      "Lies the simple truth that we are blessed.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
     }
    ],
    "source": [
-    "agent1._oai_messages"
+    "user.send(message=message, recipient=agent_with_vision, request_reply=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba4d3753",
+   "metadata": {},
+   "source": [
+    "## Custom Caption Function for Vision Capability\n",
+    "\n",
+    "In many use cases, we can use a custom function within the Vision Capability to transcribe an image into a caption.\n",
+    "\n",
+    "For instance, we can use rule-based algorithm or other models to detect the color, box, and other components inside the image.\n",
+    "\n",
+    "The custom model should take a path to the image and return a string caption.\n",
+    "\n",
+    "In the example below, the Vision Capability will call LMM to get caption and also call the custom function to get more information."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "39d293fb",
+   "execution_count": 12,
+   "id": "bff55c81",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "def my_description(image_url: str, image_data: Image = None, lmm_client: object = None) -> str:\n",
+    "    \"\"\"\n",
+    "    This function takes an image URL and returns the description.\n",
+    "\n",
+    "    Parameters:\n",
+    "        - image_url (str): The URL of the image.\n",
+    "        - image_data (PIL.Image): The image data.\n",
+    "        - lmm_client (object): The LLM client object.\n",
+    "\n",
+    "    Returns:\n",
+    "        - str: A description of the color of the image.\n",
+    "    \"\"\"\n",
+    "    # Print the arguments for illustration purpose\n",
+    "    print(\"image_url\", image_url)\n",
+    "    print(\"image_data\", image_data)\n",
+    "    print(\"lmm_client\", lmm_client)\n",
+    "\n",
+    "    img_uri = pil_to_data_uri(image_data)  # cast data into URI (str) format for API call\n",
+    "    lmm_out = lmm_client.create(\n",
+    "        context=None,\n",
+    "        messages=[\n",
+    "            {\n",
+    "                \"role\": \"user\",\n",
+    "                \"content\": [\n",
+    "                    {\"type\": \"text\", \"text\": \"Describe this image in 10 words.\"},\n",
+    "                    {\n",
+    "                        \"type\": \"image_url\",\n",
+    "                        \"image_url\": {\n",
+    "                            \"url\": img_uri,\n",
+    "                        },\n",
+    "                    },\n",
+    "                ],\n",
+    "            }\n",
+    "        ],\n",
+    "    )\n",
+    "    description = lmm_out.choices[0].message.content\n",
+    "    description = content_str(description)\n",
+    "\n",
+    "    # Convert the image into an array of pixels.\n",
+    "    pixels = np.array(image_data)\n",
+    "\n",
+    "    # Calculate the average color.\n",
+    "    avg_color_per_row = np.mean(pixels, axis=0)\n",
+    "    avg_color = np.mean(avg_color_per_row, axis=0)\n",
+    "    avg_color = avg_color.astype(int)  # Convert to integer for color values\n",
+    "\n",
+    "    # Format the average color as a string description.\n",
+    "    caption = f\"\"\"The image is from {image_url}\n",
+    "    It is about: {description}\n",
+    "    The average color of the image is RGB:\n",
+    "        ({avg_color[0]}, {avg_color[1]}, {avg_color[2]})\"\"\"\n",
+    "\n",
+    "    print(caption)  # For illustration purpose\n",
+    "\n",
+    "    return caption"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "cbafea62",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser\u001b[0m (to Regular LLM Agent with Custom Func and LMM):\n",
+      "\n",
+      "Write a poet for my image:\n",
+      "                        <img https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0>.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "image_url https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0\n",
+      "image_data <PIL.Image.Image image mode=RGB size=1920x1080 at 0x7F599DA4CCA0>\n",
+      "lmm_client <autogen.oai.client.OpenAIWrapper object at 0x7f599da3ab20>\n",
+      "The image is from https://th.bing.com/th/id/R.422068ce8af4e15b0634fe2540adea7a?rik=y4OcXBE%2fqutDOw&pid=ImgRaw&r=0\n",
+      "    It is about: Cute brown curly-haired puppy with blue collar indoors.\n",
+      "    The average color of the image is RGB: \n",
+      "        (170, 155, 137)\n",
+      "\u001b[33mRegular LLM Agent with Custom Func and LMM\u001b[0m (to User):\n",
+      "\n",
+      "Beneath a sky of homely hue,\n",
+      "Where RGB blends a gentle stew,\n",
+      "Lies a pup of curls and capers,\n",
+      "Bound by blue, his neck in drapers.\n",
+      "\n",
+      "Soft in gaze, his eyes implore,\n",
+      "Warming hearts down to the core,\n",
+      "Coat of brown, with tangles sweet,\n",
+      "Whispers of play in each petite feet.\n",
+      "\n",
+      "In a world quite vast and wide,\n",
+      "Indoors he sits, with pride inside.\n",
+      "A silent wish, a breath, a start,\n",
+      "Curly companion, a work of art.\n",
+      "\n",
+      "Within the frame, he's captured still,\n",
+      "Yet, through the lens, his charm does spill.\n",
+      "A tiny heartbeat in the quiet room,\n",
+      "His presence banishes all gloom.\n",
+      "\n",
+      "A puppy's joy, in sepia tone,\n",
+      "With collar blue, he reigns alone.\n",
+      "A picture's worth, this moment's glee,\n",
+      "Cute curly friend, for all to see.\n",
+      "\n",
+      "This poem encapsulates the essence of the cute brown curly-haired puppy wearing a blue collar, blending an emotional portrayal with the aesthetic elements you provided.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "agent_with_vision_and_func = AssistantAgent(\n",
+    "    name=\"Regular LLM Agent with Custom Func and LMM\", llm_config=gpt4_llm_config\n",
+    ")\n",
+    "\n",
+    "vision_capability_with_func = VisionCapability(\n",
+    "    lmm_config={\"config_list\": config_list_4v, \"temperature\": 0.5, \"max_tokens\": 300},\n",
+    "    custom_caption_func=my_description,\n",
+    ")\n",
+    "vision_capability_with_func.add_to_agent(agent_with_vision_and_func)\n",
+    "\n",
+    "user.send(message=message, recipient=agent_with_vision_and_func, request_reply=True)"
+   ]
   }
  ],
  "metadata": {
+  "front_matter": {
+   "description": "In AutoGen, leveraging multimodal models can be done through two different methodologies: MultimodalConversableAgent and VisionCapability.",
+   "tags": [
+    "multimodal",
+    "gpt-4v"
+   ]
+  },
   "kernelspec": {
    "display_name": "Python 3 (ipykernel)",
    "language": "python",
diff --git a/test/agentchat/contrib/capabilities/test_vision_capability.py b/test/agentchat/contrib/capabilities/test_vision_capability.py
new file mode 100644
index 00000000000..a62d5245057
--- /dev/null
+++ b/test/agentchat/contrib/capabilities/test_vision_capability.py
@@ -0,0 +1,116 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from autogen.agentchat.conversable_agent import ConversableAgent
+
+try:
+    from autogen.agentchat.contrib.capabilities.vision_capability import VisionCapability
+except ImportError:
+    skip_test = True
+else:
+    skip_test = False
+
+
+@pytest.fixture
+def lmm_config():
+    return {
+        "config_list": [{"model": "gpt-4-vision-preview", "api_key": "sk-my_key"}],
+        "temperature": 0.5,
+        "max_tokens": 300,
+    }
+
+
+@pytest.fixture
+def vision_capability(lmm_config):
+    return VisionCapability(lmm_config, custom_caption_func=None)
+
+
+@pytest.fixture
+def conversable_agent():
+    return ConversableAgent(name="conversable agent", llm_config=False)
+
+
+@pytest.mark.skipif(
+    skip_test,
+    reason="do not run if dependency is not installed",
+)
+def test_add_to_conversable_agent(vision_capability, conversable_agent):
+    vision_capability.add_to_agent(conversable_agent)
+    assert hasattr(conversable_agent, "process_last_received_message")
+
+
+@pytest.mark.skipif(
+    skip_test,
+    reason="do not run if dependency is not installed",
+)
+@patch("autogen.oai.client.OpenAIWrapper")
+@pytest.mark.skipif(
+    skip_test,
+    reason="do not run if dependency is not installed",
+)
+def test_process_last_received_message_text(mock_lmm_client, vision_capability):
+    mock_lmm_client.create.return_value = MagicMock(choices=[MagicMock(message=MagicMock(content="A description"))])
+    content = "Test message without image"
+    processed_content = vision_capability.process_last_received_message(content)
+    assert processed_content == content
+
+
+@patch("autogen.agentchat.contrib.img_utils.get_image_data", return_value="base64_image_data")
+@patch(
+    "autogen.agentchat.contrib.img_utils.convert_base64_to_data_uri",
+    return_value="data:image/png;base64,base64_image_data",
+)
+@patch(
+    "autogen.agentchat.contrib.capabilities.vision_capability.VisionCapability._get_image_caption",
+    return_value="A sample image caption.",
+)
+@pytest.mark.skipif(
+    skip_test,
+    reason="do not run if dependency is not installed",
+)
+def test_process_last_received_message_with_image(
+    mock_get_caption, mock_convert_base64, mock_get_image_data, vision_capability
+):
+    content = [{"type": "image_url", "image_url": {"url": "notebook/viz_gc.png"}}]
+    expected_caption = (
+        "<img notebook/viz_gc.png> in case you can not see, the caption of this image is: A sample image caption.\n"
+    )
+    processed_content = vision_capability.process_last_received_message(content)
+    assert processed_content == expected_caption
+
+
+####### Test the Custom Caption Func
+
+
+@pytest.fixture
+def custom_caption_func():
+    """Fixture to provide a sample custom caption function."""
+
+    def caption_func(image_url: str, image_data=None, lmm_client=None) -> str:
+        # This is a simplistic example. Replace with the actual logic.
+        return f"An image description. The image is from {image_url}."
+
+    return caption_func
+
+
+@pytest.mark.skipif(
+    skip_test,
+    reason="do not run if dependency is not installed",
+)
+class TestCustomCaptionFunc:
+    def test_custom_caption_func_with_valid_url(self, custom_caption_func):
+        """Test custom caption function with a valid image URL."""
+        image_url = "notebook/viz_gc.png"
+        expected_caption = f"An image description. The image is from {image_url}."
+        assert custom_caption_func(image_url) == expected_caption, "Caption does not match expected output."
+
+    def test_process_last_received_message_with_custom_func(self, lmm_config, custom_caption_func):
+        """Test processing a message containing an image URL with a custom caption function."""
+        vision_capability = VisionCapability(lmm_config, custom_caption_func=custom_caption_func)
+
+        image_url = "notebook/viz_gc.png"
+        content = [{"type": "image_url", "image_url": {"url": image_url}}]
+        expected_output = f" An image description. The image is from {image_url}."
+        processed_content = vision_capability.process_last_received_message(content)
+        assert expected_output in processed_content, "Processed content does not contain the expected caption."

From 28c37f3c3541bee687fd196b69676322d3ba310d Mon Sep 17 00:00:00 2001
From: Eric Zhu <ekzhu@users.noreply.github.com>
Date: Mon, 25 Mar 2024 07:35:29 -0700
Subject: [PATCH 04/27] Native tool call support for Mistral AI API and topic
 notebook. (#2135)

* Support for Mistral AI API and topic notebook.

* formatting

* formatting
---
 autogen/agentchat/conversable_agent.py        |   38 +-
 website/.gitignore                            |    2 +
 .../about-using-nonopenai-models.md           |    6 +-
 .../non-openai-models/cloud-mistralai.ipynb   | 1009 +++++++++++++++++
 4 files changed, 1042 insertions(+), 13 deletions(-)
 create mode 100644 website/docs/topics/non-openai-models/cloud-mistralai.ipynb

diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index ef350f7d9e2..54206b55052 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -696,8 +696,8 @@ def _print_received_message(self, message: Union[Dict, str], sender: Agent):
                 id_key = "name"
             else:
                 id_key = "tool_call_id"
-
-            func_print = f"***** Response from calling {message['role']} \"{message[id_key]}\" *****"
+            id = message.get(id_key, "No id found")
+            func_print = f"***** Response from calling {message['role']} ({id}) *****"
             print(colored(func_print, "green"), flush=True)
             print(message["content"], flush=True)
             print(colored("*" * len(func_print), "green"), flush=True)
@@ -714,7 +714,7 @@ def _print_received_message(self, message: Union[Dict, str], sender: Agent):
             if "function_call" in message and message["function_call"]:
                 function_call = dict(message["function_call"])
                 func_print = (
-                    f"***** Suggested function Call: {function_call.get('name', '(No function name found)')} *****"
+                    f"***** Suggested function call: {function_call.get('name', '(No function name found)')} *****"
                 )
                 print(colored(func_print, "green"), flush=True)
                 print(
@@ -726,9 +726,9 @@ def _print_received_message(self, message: Union[Dict, str], sender: Agent):
                 print(colored("*" * len(func_print), "green"), flush=True)
             if "tool_calls" in message and message["tool_calls"]:
                 for tool_call in message["tool_calls"]:
-                    id = tool_call.get("id", "(No id found)")
+                    id = tool_call.get("id", "No tool call id found")
                     function_call = dict(tool_call.get("function", {}))
-                    func_print = f"***** Suggested tool Call ({id}): {function_call.get('name', '(No function name found)')} *****"
+                    func_print = f"***** Suggested tool call ({id}): {function_call.get('name', '(No function name found)')} *****"
                     print(colored(func_print, "green"), flush=True)
                     print(
                         "Arguments: \n",
@@ -1309,6 +1309,12 @@ def _generate_oai_reply_from_client(self, llm_client, messages, cache) -> Union[
                 )
             for tool_call in extracted_response.get("tool_calls") or []:
                 tool_call["function"]["name"] = self._normalize_name(tool_call["function"]["name"])
+                # Remove id and type if they are not present.
+                # This is to make the tool call object compatible with Mistral API.
+                if tool_call.get("id") is None:
+                    tool_call.pop("id")
+                if tool_call.get("type") is None:
+                    tool_call.pop("type")
         return extracted_response
 
     async def a_generate_oai_reply(
@@ -1525,7 +1531,6 @@ def generate_tool_calls_reply(
         message = messages[-1]
         tool_returns = []
         for tool_call in message.get("tool_calls", []):
-            id = tool_call["id"]
             function_call = tool_call.get("function", {})
             func = self._function_map.get(function_call.get("name", None), None)
             if inspect.iscoroutinefunction(func):
@@ -1543,13 +1548,24 @@ def generate_tool_calls_reply(
                     loop.close()
             else:
                 _, func_return = self.execute_function(function_call)
-            tool_returns.append(
-                {
-                    "tool_call_id": id,
+            content = func_return.get("content", "")
+            if content is None:
+                content = ""
+            tool_call_id = tool_call.get("id", None)
+            if tool_call_id is not None:
+                tool_call_response = {
+                    "tool_call_id": tool_call_id,
                     "role": "tool",
-                    "content": func_return.get("content", ""),
+                    "content": content,
                 }
-            )
+            else:
+                # Do not include tool_call_id if it is not present.
+                # This is to make the tool call object compatible with Mistral API.
+                tool_call_response = {
+                    "role": "tool",
+                    "content": content,
+                }
+            tool_returns.append(tool_call_response)
         if tool_returns:
             return True, {
                 "role": "tool",
diff --git a/website/.gitignore b/website/.gitignore
index 3988a173387..957507b6b44 100644
--- a/website/.gitignore
+++ b/website/.gitignore
@@ -19,6 +19,8 @@ docs/topics/code-execution/*.mdx
 docs/topics/task_decomposition.mdx
 docs/topics/prompting-and-reasoning/*.mdx
 docs/topics/non-openai-models/*.mdx
+docs/topics/non-openai-models/**/*.py
+docs/topics/non-openai-models/**/*.svg
 
 # Misc
 .DS_Store
diff --git a/website/docs/topics/non-openai-models/about-using-nonopenai-models.md b/website/docs/topics/non-openai-models/about-using-nonopenai-models.md
index e202679f29e..0e15d5ae8b1 100644
--- a/website/docs/topics/non-openai-models/about-using-nonopenai-models.md
+++ b/website/docs/topics/non-openai-models/about-using-nonopenai-models.md
@@ -21,7 +21,8 @@ These proxy servers can be cloud-based or running locally within your environmen
 By using cloud-based proxy servers, you are able to use models without requiring the hardware
 and software to run them.
 
-These providers can host open source/weight models, like [Hugging Face](https://huggingface.co/),
+These providers can host open source/weight models, like [Hugging Face](https://huggingface.co/)
+and [Mistral AI](https://mistral.ai/),
 or their own closed models.
 
 When cloud-based proxy servers provide an OpenAI-compatible API, using them in AutoGen
@@ -32,7 +33,8 @@ authentication which is usually handled through an API key.
 Examples of using cloud-based proxy servers providers that have an OpenAI-compatible API
 are provided below:
 
-- [together.ai example](/docs/topics/non-openai-models/cloud-togetherai)
+- [Together AI example](/docs/topics/non-openai-models/cloud-togetherai)
+- [Mistral AI example](/docs/topics/non-openai-models/cloud-mistralai)
 
 
 ### Locally run proxy servers
diff --git a/website/docs/topics/non-openai-models/cloud-mistralai.ipynb b/website/docs/topics/non-openai-models/cloud-mistralai.ipynb
new file mode 100644
index 00000000000..9c18d6d0408
--- /dev/null
+++ b/website/docs/topics/non-openai-models/cloud-mistralai.ipynb
@@ -0,0 +1,1009 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Mistral AI\n",
+    "\n",
+    "[Mistral AI](https://mistral.ai/) is a cloud based platform\n",
+    "serving Mistral's own LLMs.\n",
+    "You can use AutoGen with Mistral AI's API directly."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First you need to install the `pyautogen` package to use AutoGen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install pyautogen"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now you can set up the Mistral model you want to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "config_list = [\n",
+    "    {\n",
+    "        # Choose your model name.\n",
+    "        \"model\": \"mistral-large-latest\",\n",
+    "        \"base_url\": \"https://api.mistral.ai/v1\",\n",
+    "        # You need to provide your API key here.\n",
+    "        \"api_key\": os.environ.get(\"MISTRAL_API_KEY\"),\n",
+    "    }\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Two-Agent Coding Example\n",
+    "\n",
+    "In this example, we run a two-agent chat to count how many prime numbers between 1 and 10000 using coding."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from autogen import AssistantAgent, UserProxyAgent\n",
+    "from autogen.coding import LocalCommandLineCodeExecutor\n",
+    "\n",
+    "# Setting up the code executor.\n",
+    "workdir = Path(\"coding\")\n",
+    "workdir.mkdir(exist_ok=True)\n",
+    "code_executor = LocalCommandLineCodeExecutor(work_dir=workdir)\n",
+    "\n",
+    "# Setting up the agents.\n",
+    "user_proxy_agent = UserProxyAgent(\n",
+    "    name=\"User\",\n",
+    "    code_execution_config={\"executor\": code_executor},\n",
+    "    is_termination_msg=lambda msg: \"TERMINATE\" in msg.get(\"content\"),\n",
+    ")\n",
+    "\n",
+    "assistant_agent = AssistantAgent(\n",
+    "    name=\"Mistral Assistant\",\n",
+    "    llm_config={\"config_list\": config_list},\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mUser\u001b[0m (to Mistral Assistant):\n",
+      "\n",
+      "Count how many prime numbers from 1 to 10000.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mMistral Assistant\u001b[0m (to User):\n",
+      "\n",
+      "Sure, I can help with that. Here's a Python code snippet that counts the number of prime numbers from 1 to 10000.\n",
+      "\n",
+      "```python\n",
+      "# filename: prime_counter.py\n",
+      "\n",
+      "def is_prime(n):\n",
+      "    if n <= 1:\n",
+      "        return False\n",
+      "    if n <= 3:\n",
+      "        return True\n",
+      "    if n % 2 == 0 or n % 3 == 0:\n",
+      "        return False\n",
+      "    i = 5\n",
+      "    while i * i <= n:\n",
+      "        if n % i == 0 or n % (i + 2) == 0:\n",
+      "            return False\n",
+      "        i += 6\n",
+      "    return True\n",
+      "\n",
+      "count = 0\n",
+      "for num in range(1, 10001):\n",
+      "    if is_prime(num):\n",
+      "        count += 1\n",
+      "\n",
+      "print(count)\n",
+      "```\n",
+      "\n",
+      "Please save this code in a file named `prime_counter.py` and run it. The output will be the count of prime numbers from 1 to 10000.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK (inferred language is python)...\u001b[0m\n",
+      "\u001b[33mUser\u001b[0m (to Mistral Assistant):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: 1229\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mMistral Assistant\u001b[0m (to User):\n",
+      "\n",
+      "Based on the output, the code I provided earlier has successfully executed and found that there are 1229 prime numbers between 1 and 10000. Here's how I approached this task step by step:\n",
+      "\n",
+      "1. I wrote a Python function `is_prime(n)` to check if a given number `n` is prime. This function returns `True` if `n` is prime and `False` otherwise.\n",
+      "\n",
+      "2. I used a for loop to iterate through numbers from 1 to 10000, then called the `is_prime` function to determine if the current number is prime. If it is, I incremented a counter variable `count` by 1.\n",
+      "\n",
+      "3. I printed the value of `count` after the loop to display the total number of prime numbers in the given range.\n",
+      "\n",
+      "The output `1229` confirms that there are indeed 1229 prime numbers between 1 and 10000.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_result = user_proxy_agent.initiate_chat(\n",
+    "    assistant_agent,\n",
+    "    message=\"Count how many prime numbers from 1 to 10000.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Tool Call Example\n",
+    "\n",
+    "In this example, instead of writing code, we will have two agent playing chess against each other using tool to make moves.\n",
+    "\n",
+    "First install the `chess` package by running the following command:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install chess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Write function for making a move."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import chess\n",
+    "import chess.svg\n",
+    "from IPython.display import display\n",
+    "import random\n",
+    "from typing_extensions import Annotated\n",
+    "\n",
+    "board = chess.Board()\n",
+    "\n",
+    "\n",
+    "def make_move() -> Annotated[str, \"A move in UCI format\"]:\n",
+    "    moves = list(board.legal_moves)\n",
+    "    move = random.choice(moves)\n",
+    "    board.push(move)\n",
+    "    # Display the board.\n",
+    "    display(chess.svg.board(board, size=400))\n",
+    "    return str(move)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's create the agents. We have three different agents:\n",
+    "- `player_white` is the agent that plays white.\n",
+    "- `player_black` is the agent that plays black.\n",
+    "- `board_proxy` is the agent that moves the pieces on the board."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen import ConversableAgent, register_function\n",
+    "\n",
+    "player_white = ConversableAgent(\n",
+    "    name=\"Player White\",\n",
+    "    system_message=\"You are a chess player and you play as white. \" \"Always call make_move() to make a move\",\n",
+    "    llm_config={\"config_list\": config_list, \"cache_seed\": None},\n",
+    ")\n",
+    "\n",
+    "player_black = ConversableAgent(\n",
+    "    name=\"Player Black\",\n",
+    "    system_message=\"You are a chess player and you play as black. \" \"Always call make_move() to make a move\",\n",
+    "    llm_config={\"config_list\": config_list, \"cache_seed\": None},\n",
+    ")\n",
+    "\n",
+    "board_proxy = ConversableAgent(\n",
+    "    name=\"Board Proxy\",\n",
+    "    llm_config=False,\n",
+    "    # The board proxy will only respond to the make_move function.\n",
+    "    is_termination_msg=lambda msg: \"tool_calls\" not in msg,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Register tools for the agents. See [tutorial chapter on tool use](/docs/tutorial/tool-use) \n",
+    "for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "register_function(\n",
+    "    make_move,\n",
+    "    caller=player_white,\n",
+    "    executor=board_proxy,\n",
+    "    name=\"make_move\",\n",
+    "    description=\"Make a move.\",\n",
+    ")\n",
+    "\n",
+    "register_function(\n",
+    "    make_move,\n",
+    "    caller=player_black,\n",
+    "    executor=board_proxy,\n",
+    "    name=\"make_move\",\n",
+    "    description=\"Make a move.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Register nested chats for the player agents.\n",
+    "Nested chats allows each player agent to chat with the board proxy agent\n",
+    "to make a move, before communicating with the other player agent.\n",
+    "See [nested chats tutorial chapter](/docs/tutorial/conversation-patterns#nested-chats)\n",
+    "for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "player_white.register_nested_chats(\n",
+    "    trigger=player_black,\n",
+    "    chat_queue=[\n",
+    "        {\n",
+    "            \"sender\": board_proxy,\n",
+    "            \"recipient\": player_white,\n",
+    "        }\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "player_black.register_nested_chats(\n",
+    "    trigger=player_white,\n",
+    "    chat_queue=[\n",
+    "        {\n",
+    "            \"sender\": board_proxy,\n",
+    "            \"recipient\": player_black,\n",
+    "        }\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Start the chess game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "Let's play chess! Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "Let's play chess! Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "Let's play chess! Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b q k b n r\n",
+       "p p p p p p p p\n",
+       ". . . . . . . .\n",
+       ". . . . . . . .\n",
+       ". . . . . . . .\n",
+       "P . . . . . . .\n",
+       ". P P P P P P P\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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/><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(60, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 240)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(195, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(60, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(150, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(285, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "a2a3\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "You made a move: a2a3. It's my turn now.\n",
+      "\n",
+      "e2e4\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "You made a move: a2a3. It's my turn now.\n",
+      "\n",
+      "e2e4\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "You made a move: a2a3. It's my turn now.\n",
+      "\n",
+      "e2e4\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "You made a move: a2a3. It's my turn now.\n",
+      "\n",
+      "e2e4\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . .\n",
+       ". . . . p . . .\n",
+       ". . . . . . . .\n",
+       "P . . . . . . .\n",
+       ". P P P P P P P\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "e7e5\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I made a move: e7e5. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "I made a move: e7e5. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I made a move: e7e5. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "I made a move: e7e5. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . .\n",
+       ". . . . p . . .\n",
+       ". . . . . . . P\n",
+       "P . . . . . . .\n",
+       ". P P P P P P .\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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/><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(60, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 240)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 195)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(195, 150)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(60, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(150, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(285, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "h2h4\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I made a move: h2h4. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "I made a move: h2h4. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I made a move: h2h4. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "I made a move: h2h4. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b q k b . r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . n\n",
+       ". . . . p . . .\n",
+       ". . . . . . . P\n",
+       "P . . . . . . .\n",
+       ". P P P P P P .\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "g8h6\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "You moved g8h6. I made a move: g1g3. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "You moved g8h6. I made a move: g1g3. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "You moved g8h6. I made a move: g1g3. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "You moved g8h6. I made a move: g1g3. It's your turn now.\n",
+      "\n",
+      "Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b q k b . r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . n\n",
+       ". . . . p . . .\n",
+       ". . . . . . . P\n",
+       "P . . . . . . N\n",
+       ". P P P P P P .\n",
+       "R N B Q K B . 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xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "g1h3\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "You moved g8h6. I made a move: g1h3. You moved g1h3. It's my turn now.\n",
+      "\n",
+      "I made a move: d2d4. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "You moved g8h6. I made a move: g1h3. You moved g1h3. It's my turn now.\n",
+      "\n",
+      "I made a move: d2d4. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "You moved g8h6. I made a move: g1h3. You moved g1h3. It's my turn now.\n",
+      "\n",
+      "I made a move: d2d4. Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "You moved g8h6. I made a move: g1h3. You moved g1h3. It's my turn now.\n",
+      "\n",
+      "I made a move: d2d4. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b . k b . r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . n\n",
+       ". . . . p . . .\n",
+       ". . . . . . . q\n",
+       "P . . . . . . N\n",
+       ". P P P P P P .\n",
+       "R N B Q K B . 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+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "d8h4\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "You moved d8h4. I made a move: d4d5. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "You moved d8h4. I made a move: d4d5. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "You moved d8h4. I made a move: d4d5. Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "You moved d8h4. I made a move: d4d5. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool call (No tool call id found): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m******************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"400\" height=\"400\"><desc><pre>r n b . k b . r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . n\n",
+       ". . . . p . . .\n",
+       ". . . . P . . q\n",
+       "P . . . . . . N\n",
+       ". P P P . P P .\n",
+       "R N B Q K B . 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href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 240)\" /><use href=\"#white-knight\" xlink:href=\"#white-knight\" transform=\"translate(330, 240)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 195)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(330, 195)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(195, 150)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(330, 105)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(60, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool (No id found) *****\u001b[0m\n",
+      "e2e4\n",
+      "\u001b[32m****************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "You made a move: e2e4. I made a move: d5e4. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> NO HUMAN INPUT RECEIVED.\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "You made a move: e2e4. I made a move: d5e4. Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Clear the board.\n",
+    "board = chess.Board()\n",
+    "\n",
+    "chat_result = player_white.initiate_chat(\n",
+    "    player_black,\n",
+    "    message=\"Let's play chess! Your move.\",\n",
+    "    max_turns=4,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "autogen",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 9ca9b119b035f0f2f6d1f0231eca0bd7de214105 Mon Sep 17 00:00:00 2001
From: Eric Zhu <ekzhu@users.noreply.github.com>
Date: Mon, 25 Mar 2024 09:24:08 -0700
Subject: [PATCH 05/27] New conversational chess notebook using nested chats
 and tool use (#2137)

* add chess notebook

* update

* update

* Update notebook with figure

* Add example link

* redirect

* Clean up example format

* address gagan's comments

* update references

* fix links
---
 notebook/agentchat_chess.ipynb                |  985 --------------
 notebook/agentchat_nested_chats_chess.ipynb   | 1162 +++++++++++++++++
 notebook/nested-chats-chess.png               |  Bin 0 -> 84397 bytes
 website/docs/Examples.md                      |  131 +-
 .../docs/tutorial/conversation-patterns.ipynb |    9 +-
 website/docs/tutorial/tool-use.ipynb          |   20 +
 website/docusaurus.config.js                  |    4 +
 7 files changed, 1261 insertions(+), 1050 deletions(-)
 delete mode 100644 notebook/agentchat_chess.ipynb
 create mode 100644 notebook/agentchat_nested_chats_chess.ipynb
 create mode 100644 notebook/nested-chats-chess.png

diff --git a/notebook/agentchat_chess.ipynb b/notebook/agentchat_chess.ipynb
deleted file mode 100644
index 8ff713587c0..00000000000
--- a/notebook/agentchat_chess.ipynb
+++ /dev/null
@@ -1,985 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Chess Game Playing While Chitchatting by GPT-4 Agents\n",
-    "\n",
-    "AutoGen offers conversable agents powered by LLM, tool, or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation through multi-agent conversation.\n",
-    "Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
-    "\n",
-    "This notebook is modified based on https://github.com/ekzhu/FLAML/blob/evaluation/evaluation/chess/play_chess.ipynb\n",
-    "\n",
-    "## Requirements\n",
-    "\n",
-    "````{=mdx}\n",
-    ":::info Requirements\n",
-    "Some extra dependencies are needed for this notebook, which can be installed via pip:\n",
-    "\n",
-    "```bash\n",
-    "pip install pyautogen chess\n",
-    "```\n",
-    "\n",
-    "For more information, please refer to the [installation guide](/docs/installation/).\n",
-    ":::\n",
-    "````"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%%capture --no-stderr\n",
-    "from collections import defaultdict\n",
-    "from typing import Any, Dict, List, Optional, Union\n",
-    "\n",
-    "import chess\n",
-    "import chess.svg\n",
-    "\n",
-    "import autogen"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Set your API Endpoint\n",
-    "\n",
-    "The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "config_list_gpt4 = autogen.config_list_from_json(\n",
-    "    \"OAI_CONFIG_LIST\",\n",
-    "    filter_dict={\n",
-    "        \"model\": [\"gpt-4\", \"gpt4\", \"gpt-4-32k\", \"gpt-4-32k-0314\", \"gpt-4-32k-v0314\"],\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "````{=mdx}\n",
-    ":::tip\n",
-    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n",
-    ":::\n",
-    "````"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Define Agents\n",
-    "\n",
-    "We'll define a BoardAgent and a ChessPlayerAgent class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys_msg = \"\"\"You are an AI-powered chess board agent.\n",
-    "You translate the user's natural language input into legal UCI moves.\n",
-    "You should only reply with a UCI move string extracted from the user's input.\"\"\"\n",
-    "\n",
-    "\n",
-    "class BoardAgent(autogen.AssistantAgent):\n",
-    "    board: chess.Board\n",
-    "    correct_move_messages: Dict[autogen.Agent, List[Dict]]\n",
-    "\n",
-    "    def __init__(self, board: chess.Board):\n",
-    "        super().__init__(\n",
-    "            name=\"BoardAgent\",\n",
-    "            system_message=sys_msg,\n",
-    "            llm_config={\"temperature\": 0.0, \"config_list\": config_list_gpt4},\n",
-    "            max_consecutive_auto_reply=10,\n",
-    "        )\n",
-    "        self.register_reply(autogen.ConversableAgent, BoardAgent._generate_board_reply)\n",
-    "        self.board = board\n",
-    "        self.correct_move_messages = defaultdict(list)\n",
-    "\n",
-    "    def _generate_board_reply(\n",
-    "        self,\n",
-    "        messages: Optional[List[Dict]] = None,\n",
-    "        sender: Optional[autogen.Agent] = None,\n",
-    "        config: Optional[Any] = None,\n",
-    "    ) -> Union[str, Dict, None]:\n",
-    "        message = messages[-1]\n",
-    "        # extract a UCI move from player's message\n",
-    "        reply = self.generate_reply(\n",
-    "            self.correct_move_messages[sender] + [message], sender, exclude=[BoardAgent._generate_board_reply]\n",
-    "        )\n",
-    "        uci_move = reply if isinstance(reply, str) else str(reply[\"content\"])\n",
-    "        try:\n",
-    "            self.board.push_uci(uci_move)\n",
-    "        except ValueError as e:\n",
-    "            # invalid move\n",
-    "            return True, f\"Error: {e}\"\n",
-    "        else:\n",
-    "            # valid move\n",
-    "            m = chess.Move.from_uci(uci_move)\n",
-    "            display(  # noqa: F821\n",
-    "                chess.svg.board(\n",
-    "                    self.board, arrows=[(m.from_square, m.to_square)], fill={m.from_square: \"gray\"}, size=200\n",
-    "                )\n",
-    "            )\n",
-    "            self.correct_move_messages[sender].extend([message, self._message_to_dict(uci_move)])\n",
-    "            self.correct_move_messages[sender][-1][\"role\"] = \"assistant\"\n",
-    "            return True, uci_move"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "sys_msg_tmpl = \"\"\"Your name is {name} and you are a chess player.\n",
-    "You are playing against {opponent_name}.\n",
-    "You are playing as {color}.\n",
-    "You communicate your move using universal chess interface language.\n",
-    "You also chit-chat with your opponent when you communicate a move to light up the mood.\n",
-    "You should ensure both you and the opponent are making legal moves.\n",
-    "Do not apologize for making illegal moves.\"\"\"\n",
-    "\n",
-    "\n",
-    "class ChessPlayerAgent(autogen.AssistantAgent):\n",
-    "    def __init__(\n",
-    "        self,\n",
-    "        color: str,\n",
-    "        board_agent: BoardAgent,\n",
-    "        max_turns: int,\n",
-    "        **kwargs,\n",
-    "    ):\n",
-    "        if color not in [\"white\", \"black\"]:\n",
-    "            raise ValueError(f\"color must be either white or black, but got {color}\")\n",
-    "        opponent_color = \"black\" if color == \"white\" else \"white\"\n",
-    "        name = f\"Player {color}\"\n",
-    "        opponent_name = f\"Player {opponent_color}\"\n",
-    "        sys_msg = sys_msg_tmpl.format(\n",
-    "            name=name,\n",
-    "            opponent_name=opponent_name,\n",
-    "            color=color,\n",
-    "        )\n",
-    "        super().__init__(\n",
-    "            name=name,\n",
-    "            system_message=sys_msg,\n",
-    "            max_consecutive_auto_reply=max_turns,\n",
-    "            **kwargs,\n",
-    "        )\n",
-    "        self.register_reply(BoardAgent, ChessPlayerAgent._generate_reply_for_board, config=board_agent.board)\n",
-    "        self.register_reply(ChessPlayerAgent, ChessPlayerAgent._generate_reply_for_player, config=board_agent)\n",
-    "        self.update_max_consecutive_auto_reply(board_agent.max_consecutive_auto_reply(), board_agent)\n",
-    "\n",
-    "    def _generate_reply_for_board(\n",
-    "        self,\n",
-    "        messages: Optional[List[Dict]] = None,\n",
-    "        sender: Optional[autogen.Agent] = None,\n",
-    "        config: Optional[chess.Board] = None,\n",
-    "    ) -> Union[str, Dict, None]:\n",
-    "        board = config\n",
-    "        # add a system message about the current state of the board.\n",
-    "        board_state_msg = [{\"role\": \"system\", \"content\": f\"Current board:\\n{board}\"}]\n",
-    "        last_message = messages[-1]\n",
-    "        if last_message[\"content\"].startswith(\"Error\"):\n",
-    "            # try again\n",
-    "            last_message[\"role\"] = \"system\"\n",
-    "            return True, self.generate_reply(\n",
-    "                messages + board_state_msg, sender, exclude=[ChessPlayerAgent._generate_reply_for_board]\n",
-    "            )\n",
-    "        else:\n",
-    "            return True, None\n",
-    "\n",
-    "    def _generate_reply_for_player(\n",
-    "        self,\n",
-    "        messages: Optional[List[Dict]] = None,\n",
-    "        sender: Optional[autogen.Agent] = None,\n",
-    "        config: Optional[BoardAgent] = None,\n",
-    "    ) -> Union[str, Dict, None]:\n",
-    "        board_agent = config\n",
-    "        # add a system message about the current state of the board.\n",
-    "        board_state_msg = [{\"role\": \"system\", \"content\": f\"Current board:\\n{board_agent.board}\"}]\n",
-    "        # propose a reply which will be sent to the board agent for verification.\n",
-    "        message = self.generate_reply(\n",
-    "            messages + board_state_msg, sender, exclude=[ChessPlayerAgent._generate_reply_for_player]\n",
-    "        )\n",
-    "        if message is None:\n",
-    "            return True, None\n",
-    "        # converse with the board until a legal move is made or max allowed retries.\n",
-    "        # change silent to False to see that conversation.\n",
-    "        self.initiate_chat(board_agent, clear_history=False, message=message, silent=self.human_input_mode == \"NEVER\")\n",
-    "        # last message sent by the board agent\n",
-    "        last_message = self._oai_messages[board_agent][-1]\n",
-    "        if last_message[\"role\"] == \"assistant\":\n",
-    "            # didn't make a legal move after a limit times of retries.\n",
-    "            print(f\"{self.name}: I yield.\")\n",
-    "            return True, None\n",
-    "        return True, self._oai_messages[board_agent][-2]"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Construct Agents"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "max_turn = 10\n",
-    "\n",
-    "board = chess.Board()\n",
-    "board_agent = BoardAgent(board=board)\n",
-    "player_black = ChessPlayerAgent(\n",
-    "    color=\"black\",\n",
-    "    board_agent=board_agent,\n",
-    "    max_turns=max_turn,\n",
-    "    llm_config={\"temperature\": 0.5, \"cache_seed\": 1, \"config_list\": config_list_gpt4},\n",
-    ")\n",
-    "player_white = ChessPlayerAgent(\n",
-    "    color=\"white\",\n",
-    "    board_agent=board_agent,\n",
-    "    max_turns=max_turn,\n",
-    "    llm_config={\"temperature\": 0.5, \"cache_seed\": 2, \"config_list\": config_list_gpt4},\n",
-    ")"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Start Game"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Your turn.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
-       "p p p p p p p p\n",
-       ". . . . . . . .\n",
-       ". . . . . . . .\n",
-       ". . . . P . . .\n",
-       ". . . . . . . .\n",
-       "P P P P . P P P\n",
-       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "Alright, let's kick things off. I'll move my pawn from e2 to e4. The center of the board is the heart of the battle, isn't it? Your move. \n",
-      "\n",
-      "e2e4\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
-       "p p p p . p p p\n",
-       ". . . . . . . .\n",
-       ". . . . p . . .\n",
-       ". . . . P . . .\n",
-       ". . . . . . . .\n",
-       "P P P P . P P P\n",
-       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Ah, the King's Pawn Opening, a classic. Let's see how this plays out. I'll move my pawn from e7 to e5. \n",
-      "\n",
-      "e7e5\n",
-      "\n",
-      "You know, I've always admired how chess reflects life. It's all about making strategic decisions, isn't it? Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
-       "p p p p . p p p\n",
-       ". . . . . . . .\n",
-       ". . . . p . . .\n",
-       ". . . . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P P . P P P\n",
-       "R N B Q K B . 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-     },
-     "metadata": {},
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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "Indeed, chess is a mirror of life in many ways. Every move matters, just like every decision in life. I'll move my knight from g1 to f3, aiming to control the center.\n",
-      "\n",
-      "g1f3\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b . r\n",
-       "p p p p . p p p\n",
-       ". . . . . n . .\n",
-       ". . . . p . . .\n",
-       ". . . . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P P . P P P\n",
-       "R N B Q K B . 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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Well said! Chess is indeed a game of deep strategy and foresight. I will move my knight from g8 to f6, matching your control of the center.\n",
-      "\n",
-      "g8f6\n",
-      "\n",
-      "Did you know, the knight's move is actually based on the 'L' shape, which stands for 'leap'? Quite interesting, isn't it? Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
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-       "p p p p . p p p\n",
-       ". . . . . n . .\n",
-       ". . . . p . . .\n",
-       ". . B . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P P . P P P\n",
-       "R N B Q K . . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "That's a fun fact! The knight's L-shaped leap makes it a unique piece on the board. Now, I'll move my bishop from f1 to c4, putting a little pressure on your side.\n",
-      "\n",
-      "f1c4\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k . . r\n",
-       "p p p p . p p p\n",
-       ". . . . . n . .\n",
-       ". . b . p . . .\n",
-       ". . B . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P P . P P P\n",
-       "R N B Q K . . 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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "A strong move, indeed! I can see you're not one to hold back. I'll move my bishop from f8 to c5, maintaining the balance.\n",
-      "\n",
-      "f8c5\n",
-      "\n",
-      "You know, the bishop was originally an elephant in ancient chess. It's fascinating how the game has evolved over the centuries, isn't it? Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
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-       "p p p p . p p p\n",
-       ". . . . . n . .\n",
-       ". . b . p . . .\n",
-       ". . B . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P P . P P P\n",
-       "R N B Q . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "Absolutely, the evolution of chess is quite fascinating. It's a testament to the game's timeless appeal. Now, I'll castle kingside to ensure my king's safety. \n",
-      "\n",
-      "e1g1\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q . r k .\n",
-       "p p p p . p p p\n",
-       ". . . . . n . .\n",
-       ". . b . p . . .\n",
-       ". . B . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P P . P P P\n",
-       "R N B Q . 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-     "name": "stdout",
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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "A wise decision, safety first! I'll follow your lead and castle kingside as well. \n",
-      "\n",
-      "e8g8\n",
-      "\n",
-      "They say the king is a fighting piece, but in the early game, it's all about keeping him safe, isn't it? Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q . r k .\n",
-       "p p p p . p p p\n",
-       ". . . . . n . .\n",
-       ". . b . p . . .\n",
-       ". . B P P . . .\n",
-       ". . . . . N . .\n",
-       "P P P . . P P P\n",
-       "R N B Q . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "I see, it looks like we had a bit of a mix-up. Let's get back to the game. I'll move my pawn from d2 to d4, opening up lines for my queen and bishop.\n",
-      "\n",
-      "d2d4\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q . r k .\n",
-       "p p p p . p p p\n",
-       ". b . . . n . .\n",
-       ". . . . p . . .\n",
-       ". . B P P . . .\n",
-       ". . . . . N . .\n",
-       "P P P . . P P P\n",
-       "R N B Q . 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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Indeed, opening lines for your major pieces is a crucial part of the game. I'll move my bishop from c5 to b6. \n",
-      "\n",
-      "c5b6\n",
-      "\n",
-      "Chess is a lot like a dance, don't you think? Each piece moving in harmony with the others. Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q . r k .\n",
-       "p p p p . p p p\n",
-       ". b . . . n . .\n",
-       ". . . . p . . .\n",
-       ". . B P P . . .\n",
-       ". . . . . N . .\n",
-       "P P P . Q P P P\n",
-       "R N B . . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "I see, it looks like we had a bit of a mix-up. Let's get back to the game. I'll move my queen from d1 to e2, aiming to control the center.\n",
-      "\n",
-      "d1e2\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b . . r k .\n",
-       "p p p p q p p p\n",
-       ". b . . . n . .\n",
-       ". . . . p . . .\n",
-       ". . B P P . . .\n",
-       ". . . . . N . .\n",
-       "P P P . Q P P P\n",
-       "R N B . . 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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Indeed, control of the center is key. I'll move my queen from d8 to e7, matching your control of the center.\n",
-      "\n",
-      "d8e7\n",
-      "\n",
-      "Did you know the queen wasn't always the most powerful piece on the board? In the original game of chess, the piece could only move one square diagonally! Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
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-       "p p p p q p p p\n",
-       ". b . . . n . .\n",
-       ". . . . P . . .\n",
-       ". . B . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P . Q P P P\n",
-       "R N B . . 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-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "Indeed, the queen has come a long way from its humble beginnings. Now, I'll move my pawn from d4 to e5, adding some tension to the center.\n",
-      "\n",
-      "d4e5\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b . . r k .\n",
-       "p p p p q p p p\n",
-       ". b . . . . . .\n",
-       ". . . n P . . .\n",
-       ". . B . P . . .\n",
-       ". . . . . N . .\n",
-       "P P P . Q P P P\n",
-       "R N B . . 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-     "name": "stdout",
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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Interesting move! This is shaping up to be quite the game. I'll move my knight from f6 to d5.\n",
-      "\n",
-      "f6d5\n",
-      "\n",
-      "You know, the knight is the only piece that can jump over others. It's like the horse in a game of polo, leaping over obstacles. Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b . . r k .\n",
-       "p p p p q p p p\n",
-       ". b . . . . . .\n",
-       ". . . n P . . .\n",
-       ". . B . P . . .\n",
-       ". . N . . N . .\n",
-       "P P P . Q P P P\n",
-       "R . B . . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "Indeed, the knight's ability to jump over other pieces is quite unique. Now, I'll move my knight from b1 to c3, challenging your knight.\n",
-      "\n",
-      "b1c3\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b . . r k .\n",
-       "p p p p q p p p\n",
-       ". b n . . . . .\n",
-       ". . . n P . . .\n",
-       ". . B . P . . .\n",
-       ". . N . . N . .\n",
-       "P P P . Q P P P\n",
-       "R . B . . 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-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Apologies for the confusion. It seems there was a misunderstanding. I'll correct that and move my knight from b8 to c6.\n",
-      "\n",
-      "b8c6\n",
-      "\n",
-      "Did you know that in the Middle Ages, the knight piece was usually carved to resemble a horse's head? Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b . . r k .\n",
-       "p p p p q p p p\n",
-       ". b n . . . . .\n",
-       ". . . n P . B .\n",
-       ". . B . P . . .\n",
-       ". . N . . N . .\n",
-       "P P P . Q P P P\n",
-       "R . . . . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "That's a fascinating bit of history! The knight piece's design certainly adds to the charm of the game. Now, I'll move my bishop from c1 to g5, putting a little pressure on your queen.\n",
-      "\n",
-      "c1g5\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b . . r k .\n",
-       "p p p p . p p p\n",
-       ". b n . q . . .\n",
-       ". . . n P . B .\n",
-       ". . B . P . . .\n",
-       ". . N . . N . .\n",
-       "P P P . Q P P P\n",
-       "R . . . . 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-    {
-     "name": "stdout",
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-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "Well played! I see you're not one to shy away from a challenge. I'll move my queen from e7 to e6, keeping her safe.\n",
-      "\n",
-      "e7e6\n",
-      "\n",
-      "Did you know that the queen's ability to move any number of squares along a rank, file, or diagonal is a relatively recent development in the history of chess? Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b . . r k .\n",
-       "p p p p . p p p\n",
-       ". b n . q . . .\n",
-       ". . . n P . B .\n",
-       ". . B . P . . .\n",
-       ". . N . . N . .\n",
-       "P P P . Q P P P\n",
-       ". . . R . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer white\u001b[0m (to Player black):\n",
-      "\n",
-      "Indeed, the queen's powers were significantly enhanced during the Middle Ages, transforming the game of chess. Now, I'll move my rook from a1 to d1, aligning it with your queen.\n",
-      "\n",
-      "a1d1\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    },
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b . . r k .\n",
-       "p p p . . p p p\n",
-       ". b n p q . . .\n",
-       ". . . n P . B .\n",
-       ". . B . P . . .\n",
-       ". . N . . N . .\n",
-       "P P P . Q P P P\n",
-       ". . . R . 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-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "\u001b[33mPlayer black\u001b[0m (to Player white):\n",
-      "\n",
-      "My apologies for the confusion. Let's correct that. I'll move my pawn from d7 to d6.\n",
-      "\n",
-      "d7d6\n",
-      "\n",
-      "Did you know that pawns are the soul of chess? It's fascinating how these seemingly insignificant pieces can control the fate of the game. Your move.\n",
-      "\n",
-      "--------------------------------------------------------------------------------\n"
-     ]
-    }
-   ],
-   "source": [
-    "player_black.initiate_chat(player_white, message=\"Your turn.\")"
-   ]
-  }
- ],
- "metadata": {
-  "front_matter": {
-    "tags": ["chess"],
-    "description": "Use AutoGen to create two agents that are able to play chess"
-  },
-  "kernelspec": {
-   "display_name": "flaml",
-   "language": "python",
-   "name": "python3"
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- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/notebook/agentchat_nested_chats_chess.ipynb b/notebook/agentchat_nested_chats_chess.ipynb
new file mode 100644
index 00000000000..52a53de10a9
--- /dev/null
+++ b/notebook/agentchat_nested_chats_chess.ipynb
@@ -0,0 +1,1162 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Nested Chats for Tool Use in Conversational Chess\n",
+    "\n",
+    "This notebook demonstrates how to create agents that can play chess with each other\n",
+    "while communicating in natural language.\n",
+    "The key concept covered in this notebook is the use of nested chats\n",
+    "to enable tool use and packaging an LLM-based agent with a tool executor agent\n",
+    "into a single agent.\n",
+    "\n",
+    "Related tutorials:\n",
+    "- [Tool Use](/docs/tutorial/tool-use)\n",
+    "- [Nested Chats](/docs/tutorial/conversation-patterns#nested-chats)\n",
+    "\n",
+    "In this setting, each player is an agent backed by an LLM equipped two tools:\n",
+    "- `get_legal_moves` to get a list of current legal moves.\n",
+    "- `make_move` to make a move.\n",
+    "\n",
+    "A board proxy agent is set up to execute the tools and manage the game.\n",
+    "It is important to use a board proxy as a non-LLM \"guard rail\" to ensure the game\n",
+    "is played correctly and to prevent agents from making illegal moves.\n",
+    "\n",
+    "Each time a player agent receives a message from the other player agent, \n",
+    "it instantiates a nested chat with the board proxy agent to get the legal moves\n",
+    "and make a move using the tools given. \n",
+    "The nested chat between the player agent and the board agent\n",
+    "continues until the a legal move is made by the tool.\n",
+    "Once the nested chat concludes, the player agent sends a message to the\n",
+    "other player agent about the move made."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "First you need to install the `pyautogen` and `chess` packages to use AutoGen."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "! pip install -qqq pyautogen chess"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Setting up LLMs\n",
+    "\n",
+    "Now you can set up the models you want to use."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "player_white_config_list = [\n",
+    "    {\n",
+    "        \"model\": \"gpt-4-turbo-preview\",\n",
+    "        \"api_key\": os.environ.get(\"OPENAI_API_KEY\"),\n",
+    "    },\n",
+    "]\n",
+    "\n",
+    "player_black_config_list = [\n",
+    "    {\n",
+    "        \"model\": \"gpt-4-turbo-preview\",\n",
+    "        \"api_key\": os.environ.get(\"OPENAI_API_KEY\"),\n",
+    "    },\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating tools\n",
+    "\n",
+    "Write functions for getting legal moves and making a move."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import chess\n",
+    "import chess.svg\n",
+    "from IPython.display import display\n",
+    "from typing import List\n",
+    "from typing_extensions import Annotated\n",
+    "\n",
+    "# Initialize the board.\n",
+    "board = chess.Board()\n",
+    "\n",
+    "# Keep track of whether a move has been made.\n",
+    "made_move = False\n",
+    "\n",
+    "\n",
+    "def get_legal_moves() -> Annotated[str, \"A list of legal moves in UCI format\"]:\n",
+    "    return \"Possible moves are: \" + \",\".join([str(move) for move in board.legal_moves])\n",
+    "\n",
+    "\n",
+    "def make_move(move: Annotated[str, \"A move in UCI format.\"]) -> Annotated[str, \"Result of the move.\"]:\n",
+    "    move = chess.Move.from_uci(move)\n",
+    "    board.push_uci(str(move))\n",
+    "    global made_move\n",
+    "    made_move = True\n",
+    "    # Display the board.\n",
+    "    display(\n",
+    "        chess.svg.board(board, arrows=[(move.from_square, move.to_square)], fill={move.from_square: \"gray\"}, size=200)\n",
+    "    )\n",
+    "    # Get the piece name.\n",
+    "    piece = board.piece_at(move.to_square)\n",
+    "    piece_symbol = piece.unicode_symbol()\n",
+    "    piece_name = (\n",
+    "        chess.piece_name(piece.piece_type).capitalize()\n",
+    "        if piece_symbol.isupper()\n",
+    "        else chess.piece_name(piece.piece_type)\n",
+    "    )\n",
+    "    return f\"Moved {piece_name} ({piece_symbol}) from {chess.SQUARE_NAMES[move.from_square]} to {chess.SQUARE_NAMES[move.to_square]}.\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Creating agents\n",
+    "\n",
+    "Let's create the agents. We have three different agents:\n",
+    "- `player_white` is the agent that plays white.\n",
+    "- `player_black` is the agent that plays black.\n",
+    "- `board_proxy` is the agent that moves the pieces on the board."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen import ConversableAgent, register_function\n",
+    "\n",
+    "player_white = ConversableAgent(\n",
+    "    name=\"Player White\",\n",
+    "    system_message=\"You are a chess player and you play as white. \"\n",
+    "    \"First call get_legal_moves() first, to get list of legal moves. \"\n",
+    "    \"Then call make_move(move) to make a move.\",\n",
+    "    llm_config={\"config_list\": player_white_config_list, \"cache_seed\": None},\n",
+    ")\n",
+    "\n",
+    "player_black = ConversableAgent(\n",
+    "    name=\"Player Black\",\n",
+    "    system_message=\"You are a chess player and you play as black. \"\n",
+    "    \"First call get_legal_moves() first, to get list of legal moves. \"\n",
+    "    \"Then call make_move(move) to make a move.\",\n",
+    "    llm_config={\"config_list\": player_black_config_list, \"cache_seed\": None},\n",
+    ")\n",
+    "\n",
+    "# Check if the player has made a move, and reset the flag if move is made.\n",
+    "\n",
+    "\n",
+    "def check_made_move(msg):\n",
+    "    global made_move\n",
+    "    if made_move:\n",
+    "        made_move = False\n",
+    "        return True\n",
+    "    else:\n",
+    "        return False\n",
+    "\n",
+    "\n",
+    "board_proxy = ConversableAgent(\n",
+    "    name=\"Board Proxy\",\n",
+    "    llm_config=False,\n",
+    "    # The board proxy will only terminate the conversation if the player has made a move.\n",
+    "    is_termination_msg=check_made_move,\n",
+    "    # The auto reply message is set to keep the player agent retrying until a move is made.\n",
+    "    default_auto_reply=\"Please make a move.\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Register tools for the agents. See [tutorial chapter on tool use](/docs/tutorial/tool-use) \n",
+    "for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "register_function(\n",
+    "    make_move,\n",
+    "    caller=player_white,\n",
+    "    executor=board_proxy,\n",
+    "    name=\"make_move\",\n",
+    "    description=\"Call this tool to make a move.\",\n",
+    ")\n",
+    "\n",
+    "register_function(\n",
+    "    get_legal_moves,\n",
+    "    caller=player_white,\n",
+    "    executor=board_proxy,\n",
+    "    name=\"get_legal_moves\",\n",
+    "    description=\"Get legal moves.\",\n",
+    ")\n",
+    "\n",
+    "register_function(\n",
+    "    make_move,\n",
+    "    caller=player_black,\n",
+    "    executor=board_proxy,\n",
+    "    name=\"make_move\",\n",
+    "    description=\"Call this tool to make a move.\",\n",
+    ")\n",
+    "\n",
+    "register_function(\n",
+    "    get_legal_moves,\n",
+    "    caller=player_black,\n",
+    "    executor=board_proxy,\n",
+    "    name=\"get_legal_moves\",\n",
+    "    description=\"Get legal moves.\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now the agents have their tools ready. You can inspect the auto-generated\n",
+    "tool schema for each agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'type': 'function',\n",
+       "  'function': {'description': 'Call this tool to make a move.',\n",
+       "   'name': 'make_move',\n",
+       "   'parameters': {'type': 'object',\n",
+       "    'properties': {'move': {'type': 'string',\n",
+       "      'description': 'A move in UCI format.'}},\n",
+       "    'required': ['move']}}},\n",
+       " {'type': 'function',\n",
+       "  'function': {'description': 'Get legal moves.',\n",
+       "   'name': 'get_legal_moves',\n",
+       "   'parameters': {'type': 'object', 'properties': {}, 'required': []}}}]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "player_black.llm_config[\"tools\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Register nested chats for the player agents.\n",
+    "Nested chats allows each player agent to chat with the board proxy agent\n",
+    "to make a move, before communicating with the other player agent.\n",
+    "\n",
+    "In the code below, in each nested chat, the board proxy agent starts\n",
+    "a conversation with the player agent using the message recieved from the other\n",
+    "player agent (e.g., \"Your move\"). The two agents continue the conversation\n",
+    "until a legal move is made using the `make_move` tool.\n",
+    "The last message in the nested chat is a message from the player agent about\n",
+    "the move made,\n",
+    "and this message is then sent to the other player agent.\n",
+    "\n",
+    "The following diagram illustrates the nested chat between the player agent and the board agent.\n",
+    "\n",
+    "![Conversational Chess](nested-chats-chess.png)\n",
+    "\n",
+    "See [nested chats tutorial chapter](/docs/tutorial/conversation-patterns#nested-chats)\n",
+    "for more information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "player_white.register_nested_chats(\n",
+    "    trigger=player_black,\n",
+    "    chat_queue=[\n",
+    "        {\n",
+    "            # The initial message is the one received by the player agent from\n",
+    "            # the other player agent.\n",
+    "            \"sender\": board_proxy,\n",
+    "            \"recipient\": player_white,\n",
+    "            # The final message is sent to the player agent.\n",
+    "            \"summary_method\": \"last_msg\",\n",
+    "        }\n",
+    "    ],\n",
+    ")\n",
+    "\n",
+    "player_black.register_nested_chats(\n",
+    "    trigger=player_white,\n",
+    "    chat_queue=[\n",
+    "        {\n",
+    "            # The initial message is the one received by the player agent from\n",
+    "            # the other player agent.\n",
+    "            \"sender\": board_proxy,\n",
+    "            \"recipient\": player_black,\n",
+    "            # The final message is sent to the player agent.\n",
+    "            \"summary_method\": \"last_msg\",\n",
+    "        }\n",
+    "    ],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Playing the game\n",
+    "\n",
+    "Start the chess game."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "Let's play chess! Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "Let's play chess! Your move.\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "Let's play chess! Your move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_Jw535t9MZ9DMog6CMk3fleg2): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_Jw535t9MZ9DMog6CMk3fleg2\" *****\u001b[0m\n",
+      "Possible moves are: g1h3,g1f3,b1c3,b1a3,h2h3,g2g3,f2f3,e2e3,d2d3,c2c3,b2b3,a2a3,h2h4,g2g4,f2f4,e2e4,d2d4,c2c4,b2b4,a2a4\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_0e8L4c6D0HCBybuqxCD4cgjR): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"e2e4\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
+       "p p p p p p p p\n",
+       ". . . . . . . .\n",
+       ". . . . . . . .\n",
+       ". . . . P . . .\n",
+       ". . . . . . . .\n",
+       "P P P P . P P P\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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transform=\"translate(285, 330)\" /><use href=\"#white-rook\" xlink:href=\"#white-rook\" transform=\"translate(330, 330)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(60, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(150, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 195)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(195, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(60, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(150, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(285, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /><line x1=\"217.5\" y1=\"307.5\" x2=\"217.5\" y2=\"255.75\" stroke=\"#15781B\" opacity=\"0.5019607843137255\" stroke-width=\"9.0\" stroke-linecap=\"butt\" class=\"arrow\" /><polygon points=\"217.5,222.0 200.625,255.75 234.375,255.75\" fill=\"#15781B\" opacity=\"0.5019607843137255\" class=\"arrow\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_0e8L4c6D0HCBybuqxCD4cgjR\" *****\u001b[0m\n",
+      "Moved pawn (♙) from e2 to e4.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've moved my pawn from e2 to e4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my pawn from e2 to e4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I've moved my pawn from e2 to e4. Your move!\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my pawn from e2 to e4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_LyBU6E51NuiqROveKaA4EctT): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_LyBU6E51NuiqROveKaA4EctT\" *****\u001b[0m\n",
+      "Possible moves are: g8h6,g8f6,b8c6,b8a6,h7h6,g7g6,f7f6,e7e6,d7d6,c7c6,b7b6,a7a6,h7h5,g7g5,f7f5,e7e5,d7d5,c7c5,b7b5,a7a5\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "It's black's turn. I will move my pawn from e7 to e5.\n",
+      "\u001b[32m***** Suggested tool Call (call_MSLR6pqbwYIaAbfl8qxZbqnc): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"e7e5\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . .\n",
+       ". . . . p . . .\n",
+       ". . . . P . . .\n",
+       ". . . . . . . .\n",
+       "P P P P . P P P\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_MSLR6pqbwYIaAbfl8qxZbqnc\" *****\u001b[0m\n",
+      "Moved pawn (♟) from e7 to e5.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've moved my pawn from e7 to e5. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "I've moved my pawn from e7 to e5. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I've moved my pawn from e7 to e5. Your move!\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "I've moved my pawn from e7 to e5. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_gaqEpvOSEaDoh1wxvrDpwVCe): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"e2e4\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_gaqEpvOSEaDoh1wxvrDpwVCe\" *****\u001b[0m\n",
+      "Error: illegal uci: 'e2e4' in rnbqkbnr/pppp1ppp/8/4p3/4P3/8/PPPP1PPP/RNBQKBNR w KQkq - 0 2\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_BJWUGbFeqnYUwY8x6yEq6Aug): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_BJWUGbFeqnYUwY8x6yEq6Aug\" *****\u001b[0m\n",
+      "Possible moves are: g1h3,g1f3,g1e2,f1a6,f1b5,f1c4,f1d3,f1e2,e1e2,d1h5,d1g4,d1f3,d1e2,b1c3,b1a3,h2h3,g2g3,f2f3,d2d3,c2c3,b2b3,a2a3,h2h4,g2g4,f2f4,d2d4,c2c4,b2b4,a2a4\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I'll move my pawn from d2 to d4. Your turn!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "Please make a move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_tfSdfPTJgq3JeIOtT5NO2SJn): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"d2d4\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . .\n",
+       ". . . . p . . .\n",
+       ". . . P P . . .\n",
+       ". . . . . . . .\n",
+       "P P P . . P P P\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_tfSdfPTJgq3JeIOtT5NO2SJn\" *****\u001b[0m\n",
+      "Moved pawn (♙) from d2 to d4.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've moved my pawn from d2 to d4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my pawn from d2 to d4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I've moved my pawn from d2 to d4. Your move!\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my pawn from d2 to d4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_tLmkAFcQLMP7LHXKSAcUgPpA): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_tLmkAFcQLMP7LHXKSAcUgPpA\" *****\u001b[0m\n",
+      "Possible moves are: g8e7,g8h6,g8f6,f8e7,f8d6,f8c5,f8b4,f8a3,e8e7,d8e7,d8f6,d8g5,d8h4,b8c6,b8a6,e5d4,h7h6,g7g6,f7f6,d7d6,c7c6,b7b6,a7a6,h7h5,g7g5,f7f5,d7d5,c7c5,b7b5,a7a5\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_z6PVz3XkfDsfEbsBrMODJm7A): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"e5d4\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . .\n",
+       ". . . . . . . .\n",
+       ". . . p P . . .\n",
+       ". . . . . . . .\n",
+       "P P P . . P P P\n",
+       "R N B Q K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 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transform=\"translate(285, 330)\" /><use href=\"#white-rook\" xlink:href=\"#white-rook\" transform=\"translate(330, 330)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(15, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(60, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(105, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(240, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(285, 285)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(330, 285)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 195)\" /><use href=\"#white-pawn\" xlink:href=\"#white-pawn\" transform=\"translate(195, 195)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(15, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(60, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(105, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(150, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(240, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(285, 60)\" /><use href=\"#black-pawn\" xlink:href=\"#black-pawn\" transform=\"translate(330, 60)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(15, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(60, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(105, 15)\" /><use href=\"#black-queen\" xlink:href=\"#black-queen\" transform=\"translate(150, 15)\" /><use href=\"#black-king\" xlink:href=\"#black-king\" transform=\"translate(195, 15)\" /><use href=\"#black-bishop\" xlink:href=\"#black-bishop\" transform=\"translate(240, 15)\" /><use href=\"#black-knight\" xlink:href=\"#black-knight\" transform=\"translate(285, 15)\" /><use href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /><line x1=\"217.5\" y1=\"172.5\" x2=\"199.54683438038543\" y2=\"190.45316561961457\" stroke=\"#15781B\" opacity=\"0.5019607843137255\" stroke-width=\"9.0\" stroke-linecap=\"butt\" class=\"arrow\" /><polygon points=\"175.68198051533946,214.31801948466054 211.47926131290842,202.38559255213755 187.61440744786245,178.52073868709158\" fill=\"#15781B\" opacity=\"0.5019607843137255\" class=\"arrow\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_z6PVz3XkfDsfEbsBrMODJm7A\" *****\u001b[0m\n",
+      "Moved pawn (♟) from e5 to d4.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've captured your pawn by moving my pawn from e5 to d4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "I've captured your pawn by moving my pawn from e5 to d4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I've captured your pawn by moving my pawn from e5 to d4. Your move!\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "I've captured your pawn by moving my pawn from e5 to d4. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_CedmvIwaBWk23QxMZunlaOYt): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_CedmvIwaBWk23QxMZunlaOYt\" *****\u001b[0m\n",
+      "Possible moves are: g1h3,g1f3,g1e2,f1a6,f1b5,f1c4,f1d3,f1e2,e1e2,e1d2,d1h5,d1g4,d1d4,d1f3,d1d3,d1e2,d1d2,c1h6,c1g5,c1f4,c1e3,c1d2,b1c3,b1a3,b1d2,e4e5,h2h3,g2g3,f2f3,c2c3,b2b3,a2a3,h2h4,g2g4,f2f4,c2c4,b2b4,a2a4\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_gaqEpvOSEaDoh1wxvrDpwVCe): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"d1d4\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r n b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . . . . . . .\n",
+       ". . . . . . . .\n",
+       ". . . Q P . . .\n",
+       ". . . . . . . .\n",
+       "P P P . . P P P\n",
+       "R N B . K B N R</pre></desc><defs><g id=\"white-pawn\" class=\"white pawn\"><path d=\"M22.5 9c-2.21 0-4 1.79-4 4 0 .89.29 1.71.78 2.38C17.33 16.5 16 18.59 16 21c0 2.03.94 3.84 2.41 5.03-3 1.06-7.41 5.55-7.41 13.47h23c0-7.92-4.41-12.41-7.41-13.47 1.47-1.19 2.41-3 2.41-5.03 0-2.41-1.33-4.5-3.28-5.62.49-.67.78-1.49.78-2.38 0-2.21-1.79-4-4-4z\" fill=\"#fff\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" /></g><g id=\"white-knight\" class=\"white knight\" fill=\"none\" fill-rule=\"evenodd\" stroke=\"#000\" stroke-width=\"1.5\" stroke-linecap=\"round\" stroke-linejoin=\"round\"><path d=\"M 22,10 C 32.5,11 38.5,18 38,39 L 15,39 C 15,30 25,32.5 23,18\" style=\"fill:#ffffff; stroke:#000000;\" /><path d=\"M 24,18 C 24.38,20.91 18.45,25.37 16,27 C 13,29 13.18,31.34 11,31 C 9.958,30.06 12.41,27.96 11,28 C 10,28 11.19,29.23 10,30 C 9,30 5.997,31 6,26 C 6,24 12,14 12,14 C 12,14 13.89,12.1 14,10.5 C 13.27,9.506 13.5,8.5 13.5,7.5 C 14.5,6.5 16.5,10 16.5,10 L 18.5,10 C 18.5,10 19.28,8.008 21,7 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href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /><line x1=\"172.5\" y1=\"352.5\" x2=\"172.5\" y2=\"255.75\" stroke=\"#15781B\" opacity=\"0.5019607843137255\" stroke-width=\"9.0\" stroke-linecap=\"butt\" class=\"arrow\" /><polygon points=\"172.5,222.0 155.625,255.75 189.375,255.75\" fill=\"#15781B\" opacity=\"0.5019607843137255\" class=\"arrow\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_gaqEpvOSEaDoh1wxvrDpwVCe\" *****\u001b[0m\n",
+      "Moved queen (♕) from d1 to d4.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've moved my queen from d1 to d4, capturing your pawn. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my queen from d1 to d4, capturing your pawn. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I've moved my queen from d1 to d4, capturing your pawn. Your move!\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my queen from d1 to d4, capturing your pawn. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_JSsR85jDNRO58KCJFmeUU66J): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_JSsR85jDNRO58KCJFmeUU66J\" *****\u001b[0m\n",
+      "Possible moves are: g8e7,g8h6,g8f6,f8e7,f8d6,f8c5,f8b4,f8a3,e8e7,d8e7,d8f6,d8g5,d8h4,b8c6,b8a6,h7h6,g7g6,f7f6,d7d6,c7c6,b7b6,a7a6,h7h5,g7g5,f7f5,d7d5,c7c5,b7b5,a7a5\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "It's your turn, and you have a wide range of moves available. To keep the game interesting, I will move my knight from b8 to c6. Let's see how this plays out!\n",
+      "\u001b[32m***** Suggested tool Call (call_QH2T8CK9SUhUiwyPW5kbabaj): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"b8c6\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . n . . . . .\n",
+       ". . . . . . . .\n",
+       ". . . Q P . . .\n",
+       ". . . . . . . .\n",
+       "P P P . . P P P\n",
+       "R N B . 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+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player White):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_QH2T8CK9SUhUiwyPW5kbabaj\" *****\u001b[0m\n",
+      "Moved knight (♞) from b8 to c6.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer White\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've moved my knight from b8 to c6. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer White\u001b[0m (to Player Black):\n",
+      "\n",
+      "I've moved my knight from b8 to c6. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[34mStarting a new chat....\n",
+      "\n",
+      "Message:\n",
+      "I've moved my knight from b8 to c6. Your move!\n",
+      "\n",
+      "Carryover: \n",
+      "\u001b[0m\n",
+      "\u001b[34m\n",
+      "********************************************************************************\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "I've moved my knight from b8 to c6. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_WwJj6w6Wwi1gOklMDbUhUJuG): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"b8c6\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_WwJj6w6Wwi1gOklMDbUhUJuG\" *****\u001b[0m\n",
+      "Error: illegal uci: 'b8c6' in r1bqkbnr/pppp1ppp/2n5/8/3QP3/8/PPP2PPP/RNB1KBNR w KQkq - 1 4\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_fx5RRC2AGI1XoARH2EjBns8G): get_legal_moves *****\u001b[0m\n",
+      "Arguments: \n",
+      "{}\n",
+      "\u001b[32m********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION get_legal_moves...\u001b[0m\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_fx5RRC2AGI1XoARH2EjBns8G\" *****\u001b[0m\n",
+      "Possible moves are: d4g7,d4d7,d4a7,d4f6,d4d6,d4b6,d4e5,d4d5,d4c5,d4c4,d4b4,d4a4,d4e3,d4d3,d4c3,d4d2,d4d1,g1h3,g1f3,g1e2,f1a6,f1b5,f1c4,f1d3,f1e2,e1e2,e1d2,e1d1,c1h6,c1g5,c1f4,c1e3,c1d2,b1c3,b1a3,b1d2,e4e5,h2h3,g2g3,f2f3,c2c3,b2b3,a2a3,h2h4,g2g4,f2f4,c2c4,b2b4,a2a4\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "It seems there was a misunderstanding with your move as \"b8c6\" is not a valid move right now. Please ensure your move is from the current board position and legal. If you'd like to suggest another move or need assistance, please let me know!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "Please make a move.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_e4oYuwkcrLmooN9AKcrgBucB): make_move *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\"move\":\"d4d5\"}\n",
+      "\u001b[32m**************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION make_move...\u001b[0m\n"
+     ]
+    },
+    {
+     "data": {
+      "image/svg+xml": [
+       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" viewBox=\"0 0 390 390\" width=\"200\" height=\"200\"><desc><pre>r . b q k b n r\n",
+       "p p p p . p p p\n",
+       ". . n . . . . .\n",
+       ". . . Q . . . .\n",
+       ". . . . P . . .\n",
+       ". . . . . . . .\n",
+       "P P P . . P P P\n",
+       "R N B . 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href=\"#black-rook\" xlink:href=\"#black-rook\" transform=\"translate(330, 15)\" /><line x1=\"172.5\" y1=\"217.5\" x2=\"172.5\" y2=\"210.75\" stroke=\"#15781B\" opacity=\"0.5019607843137255\" stroke-width=\"9.0\" stroke-linecap=\"butt\" class=\"arrow\" /><polygon points=\"172.5,177.0 155.625,210.75 189.375,210.75\" fill=\"#15781B\" opacity=\"0.5019607843137255\" class=\"arrow\" /></svg>'"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[33mBoard Proxy\u001b[0m (to Player Black):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_e4oYuwkcrLmooN9AKcrgBucB\" *****\u001b[0m\n",
+      "Moved queen (♕) from d4 to d5.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Board Proxy):\n",
+      "\n",
+      "I've moved my queen from d4 to d5. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mPlayer Black\u001b[0m (to Player White):\n",
+      "\n",
+      "I've moved my queen from d4 to d5. Your move!\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Clear the board.\n",
+    "board = chess.Board()\n",
+    "\n",
+    "chat_result = player_white.initiate_chat(\n",
+    "    player_black,\n",
+    "    message=\"Let's play chess! Your move.\",\n",
+    "    max_turns=4,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In the output above, you can see \"Start a new chat\" is displayed\n",
+    "whenever a new nested chat is started between the board proxy agent and a player agent.\n",
+    "The \"carryover\" empty as it is a new chat in the sequence."
+   ]
+  }
+ ],
+ "metadata": {
+  "front_matter": {
+   "description": "LLM-backed agents playing chess with each other using nested chats.",
+   "tags": [
+    "nested chat"
+   ]
+  },
+  "kernelspec": {
+   "display_name": "autogen",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebook/nested-chats-chess.png b/notebook/nested-chats-chess.png
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diff --git a/website/docs/Examples.md b/website/docs/Examples.md
index f061051eef9..155921719e8 100644
--- a/website/docs/Examples.md
+++ b/website/docs/Examples.md
@@ -7,97 +7,100 @@ Please find documentation about this feature [here](/docs/Use-Cases/agent_chat).
 
 Links to notebook examples:
 
+### Code Generation, Execution, and Debugging
 
-1. **Code Generation, Execution, and Debugging**
+- Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](/docs/notebooks/agentchat_auto_feedback_from_code_execution)
+- Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat)
+- Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_qdrant_RetrieveChat)
 
-   - Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](/docs/notebooks/agentchat_auto_feedback_from_code_execution)
-   - Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_RetrieveChat)
-   - Automated Code Generation and Question Answering with [Qdrant](https://qdrant.tech/) based Retrieval Augmented Agents - [View Notebook](/docs/notebooks/agentchat_qdrant_RetrieveChat)
+### Multi-Agent Collaboration (>3 Agents)
 
-1. **Multi-Agent Collaboration (>3 Agents)**
+- Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat)
+- Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_vis)
+- Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_research)
+- Automated Task Solving with Coding & Planning Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
+- Automated Task Solving with transition paths specified in a graph - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_graph_modelling_language_using_select_speaker.ipynb)
+- Running a group chat as an inner-monolgue via the SocietyOfMindAgent - [View Notebook](/docs/notebooks/agentchat_society_of_mind)
+- Running a group chat with custom speaker selection function - [View Notebook](/docs/notebooks/agentchat_groupchat_customized)
 
-   - Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat)
-   - Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_vis)
-   - Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_research)
-   - Automated Task Solving with Coding & Planning Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
-   - Automated Task Solving with transition paths specified in a graph - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_graph_modelling_language_using_select_speaker.ipynb)
-   - Running a group chat as an inner-monolgue via the SocietyOfMindAgent - [View Notebook](/docs/notebooks/agentchat_society_of_mind)
-   - Running a group chat with custom speaker selection function - [View Notebook](/docs/notebooks/agentchat_groupchat_customized)
+### Sequential Multi-Agent Chats
 
-1. **Sequential Multi-Agent Chats**
-   - Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/docs/notebooks/agentchat_multi_task_chats)
-   - Async-solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/docs/notebooks/agentchat_multi_task_async_chats)
-   - Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents - [View Notebook](/docs/notebooks/agentchats_sequential_chats)
+- Solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/docs/notebooks/agentchat_multi_task_chats)
+- Async-solving Multiple Tasks in a Sequence of Chats Initiated by a Single Agent - [View Notebook](/docs/notebooks/agentchat_multi_task_async_chats)
+- Solving Multiple Tasks in a Sequence of Chats Initiated by Different Agents - [View Notebook](/docs/notebooks/agentchats_sequential_chats)
 
-1. **Nested Chats**
-   - Solving Complex Tasks with Nested Chats - [View Notebook](/docs/notebooks/agentchat_nestedchat)
-   - Solving Complex Tasks with A Sequence of Nested Chats - [View Notebook](/docs/notebooks/agentchat_nested_sequential_chats)
-   - OptiGuide for Solving a Supply Chain Optimization Problem with Nested Chats with a Coding Agent and a Safeguard Agent - [View Notebook](/docs/notebooks/agentchat_nestedchat_optiguide)
+### Nested Chats
 
-1. **Applications**
+- Solving Complex Tasks with Nested Chats - [View Notebook](/docs/notebooks/agentchat_nestedchat)
+- Solving Complex Tasks with A Sequence of Nested Chats - [View Notebook](/docs/notebooks/agentchat_nested_sequential_chats)
+- OptiGuide for Solving a Supply Chain Optimization Problem with Nested Chats with a Coding Agent and a Safeguard Agent - [View Notebook](/docs/notebooks/agentchat_nestedchat_optiguide)
+- Conversational Chess with Nested Chats and Tool Use - [View Notebook](/docs/notebooks/agentchat_nested_chats_chess)
 
-   - Automated Chess Game Playing & Chitchatting by GPT-4 Agents - [View Notebook](/docs/notebooks/agentchat_chess)
-   - Automated Continual Learning from New Data - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
-   - [OptiGuide](https://github.com/microsoft/optiguide) - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization
-   - [AutoAnny](https://github.com/microsoft/autogen/tree/main/samples/apps/auto-anny) - A Discord bot built using AutoGen
+### Applications
 
-1. **Tool Use**
+- Automated Continual Learning from New Data - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
+- [OptiGuide](https://github.com/microsoft/optiguide) - Coding, Tool Using, Safeguarding & Question Answering for Supply Chain Optimization
+- [AutoAnny](https://github.com/microsoft/autogen/tree/main/samples/apps/auto-anny) - A Discord bot built using AutoGen
 
-   - **Web Search**: Solve Tasks Requiring Web Info - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb)
-   - Use Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb)
-   - Use Tools via Sync and Async Function Calling - [View Notebook](/docs/notebooks/agentchat_function_call_async)
-   - Task Solving with Langchain Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_langchain.ipynb)
-   - **RAG**: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_RAG)
-   - Function Inception: Enable AutoGen agents to update/remove functions during conversations. - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_inception_function.ipynb)
-   - Agent Chat with Whisper - [View Notebook](/docs/notebooks/agentchat_video_transcript_translate_with_whisper)
-   - Constrained Responses via Guidance - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_guidance.ipynb)
-   - Browse the Web with Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_surfer.ipynb)
-   - **SQL**: Natural Language Text to SQL Query using the [Spider](https://yale-lily.github.io/spider) Text-to-SQL Benchmark - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_sql_spider.ipynb)
+### Tool Use
 
-1. **Human Involvement**
+- **Web Search**: Solve Tasks Requiring Web Info - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb)
+- Use Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb)
+- Use Tools via Sync and Async Function Calling - [View Notebook](/docs/notebooks/agentchat_function_call_async)
+- Task Solving with Langchain Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_langchain.ipynb)
+- **RAG**: Group Chat with Retrieval Augmented Generation (with 5 group member agents and 1 manager agent) - [View Notebook](/docs/notebooks/agentchat_groupchat_RAG)
+- Function Inception: Enable AutoGen agents to update/remove functions during conversations. - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_inception_function.ipynb)
+- Agent Chat with Whisper - [View Notebook](/docs/notebooks/agentchat_video_transcript_translate_with_whisper)
+- Constrained Responses via Guidance - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_guidance.ipynb)
+- Browse the Web with Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_surfer.ipynb)
+- **SQL**: Natural Language Text to SQL Query using the [Spider](https://yale-lily.github.io/spider) Text-to-SQL Benchmark - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_sql_spider.ipynb)
 
-   - Simple example in ChatGPT style [View example](https://github.com/microsoft/autogen/blob/main/samples/simple_chat.py)
-   - Auto Code Generation, Execution, Debugging and **Human Feedback** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
-   - Automated Task Solving with GPT-4 + **Multiple Human Users** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb)
-   - Agent Chat with **Async Human Inputs** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/Async_human_input.ipynb)
+### Human Involvement
 
-1. **Agent Teaching and Learning**
+- Simple example in ChatGPT style [View example](https://github.com/microsoft/autogen/blob/main/samples/simple_chat.py)
+- Auto Code Generation, Execution, Debugging and **Human Feedback** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
+- Automated Task Solving with GPT-4 + **Multiple Human Users** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb)
+- Agent Chat with **Async Human Inputs** - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/Async_human_input.ipynb)
 
-   - Teach Agents New Skills & Reuse via Automated Chat - [View Notebook](/docs/notebooks/agentchat_teaching)
-   - Teach Agents New Facts, User Preferences and Skills Beyond Coding - [View Notebook](/docs/notebooks/agentchat_teachability)
-   - Teach OpenAI Assistants Through GPTAssistantAgent - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teachable_oai_assistants.ipynb)
-   - Agent Optimizer: Train Agents in an Agentic Way - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb)
+### Agent Teaching and Learning
 
-1. **Multi-Agent Chat with OpenAI Assistants in the loop**
+- Teach Agents New Skills & Reuse via Automated Chat - [View Notebook](/docs/notebooks/agentchat_teaching)
+- Teach Agents New Facts, User Preferences and Skills Beyond Coding - [View Notebook](/docs/notebooks/agentchat_teachability)
+- Teach OpenAI Assistants Through GPTAssistantAgent - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teachable_oai_assistants.ipynb)
+- Agent Optimizer: Train Agents in an Agentic Way - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb)
 
-   - Hello-World Chat with OpenAi Assistant in AutoGen - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_twoagents_basic.ipynb)
-   - Chat with OpenAI Assistant using Function Call - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_function_call.ipynb)
-   - Chat with OpenAI Assistant with Code Interpreter - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_code_interpreter.ipynb)
-   - Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_retrieval.ipynb)
-   - OpenAI Assistant in a Group Chat - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_groupchat.ipynb)
+### Multi-Agent Chat with OpenAI Assistants in the loop
 
-1. **Multimodal Agent**
+- Hello-World Chat with OpenAi Assistant in AutoGen - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_twoagents_basic.ipynb)
+- Chat with OpenAI Assistant using Function Call - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_function_call.ipynb)
+- Chat with OpenAI Assistant with Code Interpreter - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_code_interpreter.ipynb)
+- Chat with OpenAI Assistant with Retrieval Augmentation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_retrieval.ipynb)
+- OpenAI Assistant in a Group Chat - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_oai_assistant_groupchat.ipynb)
 
-   - Multimodal Agent Chat with DALLE and GPT-4V   - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_dalle_and_gpt4v.ipynb)
-   - Multimodal Agent Chat with Llava  - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb)
-   - Multimodal Agent Chat with GPT-4V - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_gpt-4v.ipynb)
+### Multimodal Agent
 
-1. **Long Context Handling**
+- Multimodal Agent Chat with DALLE and GPT-4V   - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_dalle_and_gpt4v.ipynb)
+- Multimodal Agent Chat with Llava  - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb)
+- Multimodal Agent Chat with GPT-4V - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_gpt-4v.ipynb)
 
-   <!-- - Conversations with Chat History Compression Enabled - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb) -->
-   - Long Context Handling as A Capability - [View Notebook](/docs/notebooks/agentchat_capability_long_context_handling)
+### Long Context Handling
 
-1. **Evaluation and Assessment**
+<!-- - Conversations with Chat History Compression Enabled - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_compression.ipynb) -->
+- Long Context Handling as A Capability - [View Notebook](/docs/notebooks/agentchat_capability_long_context_handling)
 
-   - AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb)
+### Evaluation and Assessment
 
-1. **Automatic Agent Building**
+- AgentEval: A Multi-Agent System for Assess Utility of LLM-powered Applications - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agenteval_cq_math.ipynb)
 
-   - Automatically Build Multi-agent System with AgentBuilder - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb)
-   - Automatically Build Multi-agent System from Agent Library - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb)
+### Automatic Agent Building
+
+- Automatically Build Multi-agent System with AgentBuilder - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_basic.ipynb)
+- Automatically Build Multi-agent System from Agent Library - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/autobuild_agent_library.ipynb)
 
 ## Enhanced Inferences
+
 ### Utilities
+
 - API Unification  - [View Documentation with Code Example](https://microsoft.github.io/autogen/docs/Use-Cases/enhanced_inference/#api-unification)
 - Utility Functions to Help Managing API configurations effectively - [View Notebook](/docs/topics/llm_configuration)
 - Cost Calculation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_cost_token_tracking.ipynb)
diff --git a/website/docs/tutorial/conversation-patterns.ipynb b/website/docs/tutorial/conversation-patterns.ipynb
index 25b66da9374..a7d486e9f7c 100644
--- a/website/docs/tutorial/conversation-patterns.ipynb
+++ b/website/docs/tutorial/conversation-patterns.ipynb
@@ -1526,7 +1526,14 @@
     "The implementation of the nested chats handler makes use of the\n",
     "[`register_reply`](../reference/agentchat/conversable_agent/#register_reply)\n",
     "method, which allows you to make extensive customization to\n",
-    " `ConversableAgent`. The GroupChatManager uses the same mechanism to implement the group chat."
+    " `ConversableAgent`. The GroupChatManager uses the same mechanism to implement the group chat.\n",
+    "\n",
+    "Nested chat is a powerful conversation pattern that allows you to package\n",
+    "complex workflows into a single agent.\n",
+    "You can hide [tool usages](/docs/tutorial/tool-use) within a single agent by having the tool-caller agent \n",
+    "starts a nested chat with a tool-executor agent and then use the result\n",
+    "of the nested chat to generate a response.\n",
+    "See the [nested chats for tool use notebook](/docs/notebooks/agentchat_nested_chats_chess) for an example."
    ]
   },
   {
diff --git a/website/docs/tutorial/tool-use.ipynb b/website/docs/tutorial/tool-use.ipynb
index bcc161d5a4b..0e78fe742ff 100644
--- a/website/docs/tutorial/tool-use.ipynb
+++ b/website/docs/tutorial/tool-use.ipynb
@@ -719,6 +719,26 @@
     "the new tool schema and perform calculation correctly."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## How to hide tool usage and code execution within a single agent?\n",
+    "\n",
+    "Sometimes it is preferable to hide the tool usage inside a single agent, \n",
+    "i.e., the tool call and tool response messages are kept invisible from outside\n",
+    "of the agent, and the agent responds to outside messages with tool usages\n",
+    "as \"internal monologues\". \n",
+    "For example, you might want build an agent that is similar to\n",
+    "the [OpenAI's Assistant](https://platform.openai.com/docs/assistants/how-it-works)\n",
+    "which executes built-in tools internally.\n",
+    "\n",
+    "To achieve this, you can use [nested chats](/docs/tutorial/conversation-patterns#nested-chats).\n",
+    "Nested chats allow you to create \"internal monologues\" within an agent\n",
+    "to call and execute tools. This works for code execution as well.\n",
+    "See [nested chats for tool use](/docs/notebooks/agentchat_nested_chats_chess) for an example."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js
index e0f40e72e7d..d3fc424803a 100644
--- a/website/docusaurus.config.js
+++ b/website/docusaurus.config.js
@@ -216,6 +216,10 @@ module.exports = {
           {
             to: "/docs/topics/non-openai-models/local-lm-studio",
             from: ["/docs/topics/non-openai-models/lm-studio"],
+          },
+          {
+            to: "/docs/notebooks/agentchat_nested_chats_chess",
+            from: ["/docs/notebooks/agentchat_chess"],
           }
         ],
       },

From cfed8ef1f54c614b4cd76239463464096f247c44 Mon Sep 17 00:00:00 2001
From: olgavrou <olgavrou@gmail.com>
Date: Mon, 25 Mar 2024 13:43:30 -0400
Subject: [PATCH 06/27] add webarena in samples (#2114)

* add webarena in samples/tools

* Update samples/tools/webarena/README.md

Co-authored-by: gagb <gagb@users.noreply.github.com>

* Update samples/tools/webarena/README.md

Co-authored-by: gagb <gagb@users.noreply.github.com>

* Update samples/tools/webarena/README.md

Co-authored-by: gagb <gagb@users.noreply.github.com>

* update installation instructions

* black formatting

* Update README.md

---------

Co-authored-by: gagb <gagb@users.noreply.github.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
---
 samples/tools/webarena/README.md              |  32 ++
 samples/tools/webarena/webarena/run.py        | 354 ++++++++++++++++++
 .../webarena/webarena/webarena_agents.py      | 205 ++++++++++
 3 files changed, 591 insertions(+)
 create mode 100644 samples/tools/webarena/README.md
 create mode 100644 samples/tools/webarena/webarena/run.py
 create mode 100644 samples/tools/webarena/webarena/webarena_agents.py

diff --git a/samples/tools/webarena/README.md b/samples/tools/webarena/README.md
new file mode 100644
index 00000000000..9c184162923
--- /dev/null
+++ b/samples/tools/webarena/README.md
@@ -0,0 +1,32 @@
+# WebArena Benchmark
+
+This directory helps run AutoGen agents on the [WebArena](https://arxiv.org/pdf/2307.13854.pdf) benchmark.
+
+## Installing WebArena
+
+WebArena can be installed by following the instructions from [WebArena's GitHub repository](git@github.com:web-arena-x/webarena.git)
+
+If using WebArena with AutoGen there is a clash on the versions of OpenAI and some code changes are needed in WebArena to be compatible with AutoGen's OpenAI version:
+
+- webarena's openai version is `openai==0.27.0`
+- autogen's openai version is: `openai>=1.3`
+
+Prior to installation, in the WebArena codebase, any file containing `openai.error` needs to be replaced with `openai`.
+
+## Running with AutoGen agents
+
+You can use the `run.py` file in the `webarena` directory to run WebArena with AutoGen. The OpenAI (or AzureOpenAI or other model) configuration can be setup via `OAI_CONFIG_LIST`. The config list will be filtered by whatever model is passed in the `--model` argument.
+
+e.g. of running `run.py`:
+
+```
+mkdir myresultdir
+python run.py --instruction_path agent/prompts/jsons/p_cot_id_actree_2s.json --test_start_idx 27 --test_end_idx 28 --model gpt-4 --result_dir myresultdir
+```
+
+The original `run.py` file has been modified to use AutoGen agents which are defined in the `webarena_agents.py` file.
+
+## References
+**WebArena: A Realistic Web Environment for Building Autonomous Agents**<br/>
+Zhou, Shuyan and Xu, Frank F and Zhu, Hao and Zhou, Xuhui and Lo, Robert and Sridhar, Abishek and Cheng, Xianyi and Bisk, Yonatan and Fried, Daniel and Alon, Uri and others<br/>
+[https://arxiv.org/pdf/2307.13854.pdf](https://arxiv.org/pdf/2307.13854.pdf)
diff --git a/samples/tools/webarena/webarena/run.py b/samples/tools/webarena/webarena/run.py
new file mode 100644
index 00000000000..a9411ea12d1
--- /dev/null
+++ b/samples/tools/webarena/webarena/run.py
@@ -0,0 +1,354 @@
+"""Script to run end-to-end evaluation on the benchmark"""
+
+import argparse
+import glob
+import json
+import logging
+import os
+import random
+import subprocess
+import tempfile
+import time
+from pathlib import Path
+
+import openai
+import autogen
+from webarena_agents import ActionTakingCapability, EnvironmentAgent
+
+from agent import (
+    Agent,
+    PromptAgent,
+    TeacherForcingAgent,
+    construct_agent,
+)
+
+from agent.prompts import *
+from browser_env import (
+    ScriptBrowserEnv,
+)
+from browser_env.auto_login import get_site_comb_from_filepath
+from browser_env.helper_functions import (
+    RenderHelper,
+)
+from evaluation_harness import evaluator_router
+
+LOG_FOLDER = "log_files"
+Path(LOG_FOLDER).mkdir(parents=True, exist_ok=True)
+LOG_FILE_NAME = f"{LOG_FOLDER}/log_{time.strftime('%Y%m%d%H%M%S', time.localtime())}_{random.randint(0, 10000)}.log"
+
+logger = logging.getLogger("logger")
+logger.setLevel(logging.INFO)
+
+console_handler = logging.StreamHandler()
+console_handler.setLevel(logging.DEBUG)
+logger.addHandler(console_handler)
+
+file_handler = logging.FileHandler(LOG_FILE_NAME)
+file_handler.setLevel(logging.DEBUG)
+logger.addHandler(file_handler)
+
+# Set the log format
+formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
+console_handler.setFormatter(formatter)
+file_handler.setFormatter(formatter)
+
+
+def config() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description="Run end-to-end evaluation on the benchmark")
+    parser.add_argument("--render", action="store_true", help="Render the browser")
+    parser.add_argument(
+        "--slow_mo",
+        type=int,
+        default=0,
+        help="Slow down the browser by the specified amount",
+    )
+    parser.add_argument("--action_set_tag", default="id_accessibility_tree", help="Action type")
+    parser.add_argument(
+        "--observation_type",
+        choices=["accessibility_tree", "html", "image"],
+        default="accessibility_tree",
+        help="Observation type",
+    )
+    parser.add_argument(
+        "--current_viewport_only",
+        action="store_true",
+        help="Only use the current viewport for the observation",
+    )
+    parser.add_argument("--viewport_width", type=int, default=1280)
+    parser.add_argument("--viewport_height", type=int, default=720)
+    parser.add_argument("--save_trace_enabled", action="store_true")
+    parser.add_argument("--sleep_after_execution", type=float, default=0.0)
+
+    parser.add_argument("--max_steps", type=int, default=30)
+
+    # agent config
+    parser.add_argument("--agent_type", type=str, default="prompt")
+    parser.add_argument(
+        "--instruction_path",
+        type=str,
+        default="agents/prompts/state_action_agent.json",
+    )
+    parser.add_argument(
+        "--parsing_failure_th",
+        help="When concesecutive parsing failure exceeds this threshold, the agent will stop",
+        type=int,
+        default=3,
+    )
+    parser.add_argument(
+        "--repeating_action_failure_th",
+        help="When concesecutive repeating action exceeds this threshold, the agent will stop",
+        type=int,
+        default=3,
+    )
+
+    # lm config
+    parser.add_argument("--provider", type=str, default="openai")
+    parser.add_argument("--model", type=str, default="gpt-3.5-turbo-0613")
+    parser.add_argument("--mode", type=str, default="chat")
+    parser.add_argument("--temperature", type=float, default=1.0)
+    parser.add_argument("--top_p", type=float, default=0.9)
+    parser.add_argument("--context_length", type=int, default=0)
+    parser.add_argument("--max_tokens", type=int, default=384)
+    parser.add_argument("--stop_token", type=str, default=None)
+    parser.add_argument(
+        "--max_retry",
+        type=int,
+        help="max retry times to perform generations when parsing fails",
+        default=1,
+    )
+    parser.add_argument(
+        "--max_obs_length",
+        type=int,
+        help="when not zero, will truncate the observation to this length before feeding to the model",
+        default=1920,
+    )
+    parser.add_argument(
+        "--model_endpoint",
+        help="huggingface model endpoint",
+        type=str,
+        default="",
+    )
+
+    # example config
+    parser.add_argument("--test_start_idx", type=int, default=0)
+    parser.add_argument("--test_end_idx", type=int, default=1000)
+
+    # logging related
+    parser.add_argument("--result_dir", type=str, default="")
+    args = parser.parse_args()
+
+    # check the whether the action space is compatible with the observation space
+    if args.action_set_tag == "id_accessibility_tree" and args.observation_type != "accessibility_tree":
+        raise ValueError(
+            f"Action type {args.action_set_tag} is incompatible with the observation type {args.observation_type}"
+        )
+
+    return args
+
+
+def test(
+    args: argparse.Namespace,
+    agent: Agent | PromptAgent | TeacherForcingAgent,
+    config_file_list: list[str],
+) -> None:
+    scores = []
+    max_steps = args.max_steps
+
+    early_stop_thresholds = {
+        "parsing_failure": args.parsing_failure_th,
+        "repeating_action": args.repeating_action_failure_th,
+    }
+
+    env = ScriptBrowserEnv(
+        headless=not args.render,
+        slow_mo=args.slow_mo,
+        observation_type=args.observation_type,
+        current_viewport_only=args.current_viewport_only,
+        viewport_size={
+            "width": args.viewport_width,
+            "height": args.viewport_height,
+        },
+        save_trace_enabled=args.save_trace_enabled,
+        sleep_after_execution=args.sleep_after_execution,
+    )
+
+    for config_file in config_file_list:
+        try:
+            render_helper = RenderHelper(config_file, args.result_dir, args.action_set_tag)
+
+            # get intent
+            with open(config_file) as f:
+                _c = json.load(f)
+                intent = _c["intent"]
+                task_id = _c["task_id"]
+                # automatically login
+                if _c["storage_state"]:
+                    cookie_file_name = os.path.basename(_c["storage_state"])
+                    comb = get_site_comb_from_filepath(cookie_file_name)
+                    temp_dir = tempfile.mkdtemp()
+                    # subprocess to renew the cookie
+                    subprocess.run(
+                        [
+                            "python",
+                            "browser_env/auto_login.py",
+                            "--auth_folder",
+                            temp_dir,
+                            "--site_list",
+                            *comb,
+                        ]
+                    )
+                    _c["storage_state"] = f"{temp_dir}/{cookie_file_name}"
+                    assert os.path.exists(_c["storage_state"])
+                    # update the config file
+                    config_file = f"{temp_dir}/{os.path.basename(config_file)}"
+                    with open(config_file, "w") as f:
+                        json.dump(_c, f)
+
+            logger.info(f"[Config file]: {config_file}")
+            logger.info(f"[Intent]: {intent}")
+
+            agent.reset(config_file)
+
+            env_agent = EnvironmentAgent(
+                name="env_agent",
+                env=env,
+                config_file=config_file,
+                result_dir=args.result_dir,
+                action_set_tag=args.action_set_tag,
+                llm_config=False,
+                code_execution_config=False,
+            )
+
+            config_list = autogen.config_list_from_json(
+                "OAI_CONFIG_LIST",
+                filter_dict={"model": [args.model]},
+            )
+
+            action_agent = autogen.AssistantAgent(
+                "action_agent",
+                llm_config={"config_list": config_list, "cache_seed": 32},
+                system_message="",
+            )
+
+            action_taking_capability = ActionTakingCapability(
+                prompt_constructor=agent.prompt_constructor,
+                action_set_tag=args.action_set_tag,
+                max_steps=max_steps,
+                early_stop_thresholds=early_stop_thresholds,
+            )
+
+            action_taking_capability.add_to_agent(action_agent)
+
+            env_agent.initiate_chat(
+                action_agent,
+                message={
+                    "content": {"intent": intent},
+                },
+            )
+
+            evaluator = evaluator_router(config_file)
+            score = evaluator(
+                trajectory=env_agent.trajectory,
+                config_file=config_file,
+                page=env.page,
+                client=env.get_page_client(env.page),
+            )
+
+            scores.append(score)
+
+            if score == 1:
+                logger.info(f"[Result] (PASS) {config_file}")
+            else:
+                logger.info(f"[Result] (FAIL) {config_file}")
+
+            if args.save_trace_enabled:
+                env.save_trace(Path(args.result_dir) / "traces" / f"{task_id}.zip")
+
+        except openai.OpenAIError as e:
+            logger.info(f"[OpenAI Error] {repr(e)}")
+        except Exception as e:
+            logger.info(f"[Unhandled Error] {repr(e)}]")
+            import traceback
+
+            # write to error file
+            with open(Path(args.result_dir) / "error.txt", "a") as f:
+                f.write(f"[Config file]: {config_file}\n")
+                f.write(f"[Unhandled Error] {repr(e)}\n")
+                f.write(traceback.format_exc())  # write stack trace to file
+
+        render_helper.close()
+
+    env.close()
+    if len(scores) > 0:
+        logger.info(f"Average score: {sum(scores) / len(scores)}")
+
+
+def prepare(args: argparse.Namespace) -> None:
+    # convert prompt python files to json
+    from agent.prompts import to_json
+
+    to_json.run()
+
+    # prepare result dir
+    result_dir = args.result_dir
+    if not result_dir:
+        result_dir = f"cache/results_{time.strftime('%Y%m%d%H%M%S', time.localtime())}"
+    if not Path(result_dir).exists():
+        Path(result_dir).mkdir(parents=True, exist_ok=True)
+        args.result_dir = result_dir
+        logger.info(f"Create result dir: {result_dir}")
+
+    if not (Path(result_dir) / "traces").exists():
+        (Path(result_dir) / "traces").mkdir(parents=True)
+
+    # log the log file
+    with open(os.path.join(result_dir, "log_files.txt"), "a+") as f:
+        f.write(f"{LOG_FILE_NAME}\n")
+
+
+def get_unfinished(config_files: list[str], result_dir: str) -> list[str]:
+    result_files = glob.glob(f"{result_dir}/*.html")
+    task_ids = [os.path.basename(f).split(".")[0].split("_")[1] for f in result_files]
+    unfinished_configs = []
+    for config_file in config_files:
+        task_id = os.path.basename(config_file).split(".")[0]
+        if task_id not in task_ids:
+            unfinished_configs.append(config_file)
+    return unfinished_configs
+
+
+def dump_config(args: argparse.Namespace) -> None:
+    config_file = Path(args.result_dir) / "config.json"
+    if not config_file.exists():
+        with open(config_file, "w") as f:
+            json.dump(vars(args), f, indent=4)
+            logger.info(f"Dump config to {config_file}")
+
+
+# def run():
+if __name__ == "__main__":
+    args = config()
+    args.sleep_after_execution = 2.0
+    prepare(args)
+
+    test_file_list = []
+    st_idx = args.test_start_idx
+    ed_idx = args.test_end_idx
+    for i in range(st_idx, ed_idx):
+        test_file_list.append(f"config_files/{i}.json")
+    if "debug" not in args.result_dir:
+        test_file_list = get_unfinished(test_file_list, args.result_dir)
+
+    if len(test_file_list) == 0:
+        logger.info("No task left to run")
+    else:
+        print(f"Total {len(test_file_list)} tasks left")
+        args.render = False
+        args.render_screenshot = True
+        args.save_trace_enabled = True
+
+        args.current_viewport_only = True
+        dump_config(args)
+
+        agent = construct_agent(args)
+        test(args, agent, test_file_list)
diff --git a/samples/tools/webarena/webarena/webarena_agents.py b/samples/tools/webarena/webarena/webarena_agents.py
new file mode 100644
index 00000000000..10305725958
--- /dev/null
+++ b/samples/tools/webarena/webarena/webarena_agents.py
@@ -0,0 +1,205 @@
+import autogen
+
+from browser_env.actions import (
+    ActionParsingError,
+    Action,
+    create_none_action,
+    create_id_based_action,
+    create_playwright_action,
+)
+
+from browser_env import (
+    ActionTypes,
+    StateInfo,
+    Trajectory,
+    create_stop_action,
+)
+
+from browser_env.helper_functions import (
+    RenderHelper,
+    get_action_description,
+)
+
+from browser_env.actions import is_equivalent
+
+
+def early_stop(trajectory: Trajectory, max_steps: int, thresholds: dict[str, int]) -> tuple[bool, str]:
+    """Check whether need to early stop"""
+
+    # reach the max step
+    num_steps = (len(trajectory) - 1) / 2
+    if num_steps >= max_steps:
+        return True, f"Reach max steps {max_steps}"
+
+    last_k_actions: list[Action]
+    action_seq: list[Action]
+
+    # Case: parsing failure for k times
+    k = thresholds["parsing_failure"]
+    last_k_actions = trajectory[1::2][-k:]  # type: ignore[assignment]
+    if len(last_k_actions) >= k:
+        if all([action["action_type"] == ActionTypes.NONE for action in last_k_actions]):
+            return True, f"Failed to parse actions for {k} times"
+
+    # Case: same action for k times
+    k = thresholds["repeating_action"]
+    last_k_actions = trajectory[1::2][-k:]  # type: ignore[assignment]
+    action_seq = trajectory[1::2]  # type: ignore[assignment]
+
+    if len(action_seq) == 0:
+        return False, ""
+
+    last_action: Action = action_seq[-1]
+
+    if last_action["action_type"] != ActionTypes.TYPE:
+        if len(last_k_actions) >= k:
+            if all([is_equivalent(action, last_action) for action in last_k_actions]):
+                return True, f"Same action for {k} times"
+
+    else:
+        # check the action sequence
+        if sum([is_equivalent(action, last_action) for action in action_seq]) >= k:
+            return True, f"Same typing action for {k} times"
+
+    return False, ""
+
+
+class EnvironmentAgent(autogen.ConversableAgent):
+    def __init__(
+        self,
+        env,
+        config_file,
+        result_dir,
+        action_set_tag,
+        *args,
+        **kwargs,
+    ):
+        super().__init__(*args, **kwargs)
+        self.env = env
+        self.trajectory: Trajectory = []
+        obs, info = env.reset(options={"config_file": config_file})
+        self.state_info: StateInfo = {"observation": obs, "info": info}
+        self.trajectory.append(self.state_info)
+        self.meta_data = {"action_history": ["None"]}
+        self.action_set_tag = action_set_tag
+        self.render_helper = RenderHelper(config_file, result_dir, action_set_tag)
+        self.register_reply([autogen.Agent, None], EnvironmentAgent.generate_env_reply, position=1)
+        self.register_hook(
+            hookable_method="process_message_before_send",
+            hook=self.process_message_before_send,
+        )
+
+    def process_message_before_send(self, sender, message, recipient, silent):
+        if "context" in message:
+            raise ValueError("Message should not contain context freom generate_reply")
+
+        message["context"] = {
+            "trajectory": self.trajectory,
+            "meta_data": self.meta_data,
+            "state_info": self.state_info,
+        }
+
+        if "intent" in message["content"]:
+            intent = message["content"]["intent"]
+            obs = self.state_info["observation"]["text"]
+            message["content"] = f"{obs} \n\n {intent}"
+            message["context"]["intent"] = intent
+        return message
+
+    def generate_env_reply(
+        self,
+        messages=None,
+        sender=None,
+        config=None,
+    ):
+        if messages is None:
+            messages = self.chat_messages[sender]
+
+        action = messages[-1]["context"]
+        action_str = messages[-1]["content"]
+        self.trajectory.append(action)
+
+        self.render_helper.render(action, self.state_info, self.meta_data, True)
+        self.meta_data["action_history"].append(action_str)
+
+        if action["action_type"] == ActionTypes.STOP:
+            return True, {"content": "TERMINATE"}
+
+        obs, _, terminated, _, info = self.env.step(action)
+        self.state_info = {"observation": obs, "info": info}
+        self.trajectory.append(self.state_info)
+
+        if terminated:
+            self.trajectory.append(create_stop_action(""))
+            return True, "TERMINATE"
+
+        return True, {"content": obs["text"]}
+
+
+class ActionTakingCapability:
+    def __init__(self, prompt_constructor, action_set_tag, max_steps, early_stop_thresholds):
+        self.action_set_tag = action_set_tag
+        self.prompt_constructor = prompt_constructor
+        self.max_steps = max_steps
+        self.early_stop_thresholds = early_stop_thresholds
+
+    def add_to_agent(self, agent):
+        agent.register_hook(
+            hookable_method="process_all_messages_before_reply",
+            hook=self.process_all_messages_before_reply,
+        )
+        agent.register_hook(
+            hookable_method="process_message_before_send",
+            hook=self.process_message_before_send,
+        )
+
+    def process_all_messages_before_reply(self, messages):
+        if "context" in messages[-1]:
+            m = messages[-1]["context"]
+            trajectory = m["trajectory"]
+            meta_data = m["meta_data"]
+            intent = messages[0]["context"]["intent"]
+        else:
+            if "TERMINATE" in messages[-1]["content"]:
+                return messages
+
+        # take action
+        prompt = self.prompt_constructor.construct(trajectory, intent, meta_data)
+        return prompt
+
+    def process_message_before_send(self, sender, message, recipient, silent):
+        force_prefix = self.prompt_constructor.instruction["meta_data"].get("force_prefix", "")
+        response = f"{force_prefix}{message}"
+        try:
+            parsed_response = self.prompt_constructor.extract_action(response)
+            if self.action_set_tag == "id_accessibility_tree":
+                action = create_id_based_action(parsed_response)
+            elif self.action_set_tag == "playwright":
+                action = create_playwright_action(parsed_response)
+            else:
+                action = create_stop_action(f"ERROR: action tag not recognised: {self.action_set_tag}")
+        except ActionParsingError:
+            action = create_none_action()
+            action["raw_prediction"] = response
+        except Exception as e:
+            action = create_stop_action(f"ERROR: {str(e)}")
+            action["raw_prediction"] = response
+
+        messages = sender.chat_messages[recipient]
+        if "context" in messages[-1]:
+            m = messages[-1]["context"]
+            state_info = m["state_info"]
+            trajectory = m["trajectory"]
+            early_stop_flag, stop_info = early_stop(trajectory, self.max_steps, self.early_stop_thresholds)
+
+            if early_stop_flag:
+                action = create_stop_action(f"Early stop: {stop_info}")
+
+            action_str = get_action_description(
+                action,
+                state_info["info"]["observation_metadata"],
+                action_set_tag=self.action_set_tag,
+                prompt_constructor=self.prompt_constructor,
+            )
+            return {"content": action_str, "context": action}
+        return {"content": "", "context": action}

From d9e1e6d37bdf7b84eeed90795dff8e8c2bfe0853 Mon Sep 17 00:00:00 2001
From: Qingyun Wu <qingyun0327@gmail.com>
Date: Tue, 26 Mar 2024 11:31:55 -0500
Subject: [PATCH 07/27] context to kwargs (#2064)

* context to kwargs

* add tag

* add test

* text to kwargs

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
---
 autogen/agentchat/chat.py                     |  7 ++-
 autogen/agentchat/conversable_agent.py        | 47 +++++++++----------
 .../agentchat_nested_sequential_chats.ipynb   |  2 +-
 test/agentchat/test_chats.py                  |  2 +
 4 files changed, 31 insertions(+), 27 deletions(-)

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index 1296607c281..0eac4bdb7c3 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -104,7 +104,7 @@ def __find_async_chat_order(chat_ids: Set[int], prerequisites: List[Prerequisite
     return chat_order
 
 
-def __post_carryover_processing(chat_info: Dict[str, Any]):
+def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
     if "message" not in chat_info:
         warnings.warn(
             "message is not provided in a chat_queue entry. input() will be called to get the initial message.",
@@ -127,11 +127,14 @@ def __post_carryover_processing(chat_info: Dict[str, Any]):
     print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
     print(
         colored(
-            "Starting a new chat....\n\nMessage:\n" + print_message + "\n\nCarryover: \n" + print_carryover,
+            "Starting a new chat....",
             "blue",
         ),
         flush=True,
     )
+    if chat_info.get("verbose", False):
+        print(colored("Message:\n" + print_message, "blue"), flush=True)
+        print(colored("Carryover:\n" + print_carryover, "blue"), flush=True)
     print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
 
 
diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index 54206b55052..3c8f9e79d89 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -868,7 +868,7 @@ def initiate_chat(
         summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
         summary_args: Optional[dict] = {},
         message: Optional[Union[Dict, str, Callable]] = None,
-        **context,
+        **kwargs,
     ) -> ChatResult:
         """Initiate a chat with the recipient agent.
 
@@ -944,10 +944,11 @@ def my_message(sender: ConversableAgent, recipient: ConversableAgent, context: d
                 final_msg["context"] = {"prefix": "Today I feel"}
                 return final_msg
             ```
-            **context: any context information. It has the following reserved fields:
+            **kwargs: any additional information. It has the following reserved fields:
                 - "carryover": a string or a list of string to specify the carryover information to be passed to this chat.
                     If provided, we will combine this carryover (by attaching a "context: " string and the carryover content after the message content) with the "message" content when generating the initial chat
                     message in `generate_init_message`.
+                - "verbose": a boolean to specify whether to print the message and carryover in a chat. Default is False.
 
         Raises:
             RuntimeError: if any async reply functions are registered and not ignored in sync chat.
@@ -955,8 +956,7 @@ def my_message(sender: ConversableAgent, recipient: ConversableAgent, context: d
         Returns:
             ChatResult: an ChatResult object.
         """
-        _chat_info = context.copy()
-        _chat_info["recipient"] = recipient
+        _chat_info = locals().copy()
         _chat_info["sender"] = self
         consolidate_chat_info(_chat_info, uniform_sender=self)
         for agent in [self, recipient]:
@@ -968,9 +968,9 @@ def my_message(sender: ConversableAgent, recipient: ConversableAgent, context: d
             for _ in range(max_turns):
                 if _ == 0:
                     if isinstance(message, Callable):
-                        msg2send = message(_chat_info["sender"], _chat_info["recipient"], context)
+                        msg2send = message(_chat_info["sender"], _chat_info["recipient"], kwargs)
                     else:
-                        msg2send = self.generate_init_message(message, **context)
+                        msg2send = self.generate_init_message(message, **kwargs)
                 else:
                     msg2send = self.generate_reply(messages=self.chat_messages[recipient], sender=recipient)
                 if msg2send is None:
@@ -979,9 +979,9 @@ def my_message(sender: ConversableAgent, recipient: ConversableAgent, context: d
         else:
             self._prepare_chat(recipient, clear_history)
             if isinstance(message, Callable):
-                msg2send = message(_chat_info["sender"], _chat_info["recipient"], context)
+                msg2send = message(_chat_info["sender"], _chat_info["recipient"], kwargs)
             else:
-                msg2send = self.generate_init_message(message, **context)
+                msg2send = self.generate_init_message(message, **kwargs)
             self.send(msg2send, recipient, silent=silent)
         summary = self._summarize_chat(
             summary_method,
@@ -1010,7 +1010,7 @@ async def a_initiate_chat(
         summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
         summary_args: Optional[dict] = {},
         message: Optional[Union[str, Callable]] = None,
-        **context,
+        **kwargs,
     ) -> ChatResult:
         """(async) Initiate a chat with the recipient agent.
 
@@ -1023,8 +1023,7 @@ async def a_initiate_chat(
         Returns:
             ChatResult: an ChatResult object.
         """
-        _chat_info = context.copy()
-        _chat_info["recipient"] = recipient
+        _chat_info = locals().copy()
         _chat_info["sender"] = self
         consolidate_chat_info(_chat_info, uniform_sender=self)
         for agent in [self, recipient]:
@@ -1035,9 +1034,9 @@ async def a_initiate_chat(
             for _ in range(max_turns):
                 if _ == 0:
                     if isinstance(message, Callable):
-                        msg2send = message(_chat_info["sender"], _chat_info["recipient"], context)
+                        msg2send = message(_chat_info["sender"], _chat_info["recipient"], kwargs)
                     else:
-                        msg2send = await self.a_generate_init_message(message, **context)
+                        msg2send = await self.a_generate_init_message(message, **kwargs)
                 else:
                     msg2send = await self.a_generate_reply(messages=self.chat_messages[recipient], sender=recipient)
                 if msg2send is None:
@@ -1046,9 +1045,9 @@ async def a_initiate_chat(
         else:
             self._prepare_chat(recipient, clear_history)
             if isinstance(message, Callable):
-                msg2send = message(_chat_info["sender"], _chat_info["recipient"], context)
+                msg2send = message(_chat_info["sender"], _chat_info["recipient"], kwargs)
             else:
-                msg2send = await self.a_generate_init_message(message, **context)
+                msg2send = await self.a_generate_init_message(message, **kwargs)
             await self.a_send(msg2send, recipient, silent=silent)
         summary = self._summarize_chat(
             summary_method,
@@ -2217,13 +2216,13 @@ async def a_execute_function(self, func_call):
             "content": str(content),
         }
 
-    def generate_init_message(self, message: Union[Dict, str, None], **context) -> Union[str, Dict]:
+    def generate_init_message(self, message: Union[Dict, str, None], **kwargs) -> Union[str, Dict]:
         """Generate the initial message for the agent.
         If message is None, input() will be called to get the initial message.
 
         Args:
             message (str or None): the message to be processed.
-            **context: any context information. It has the following reserved fields:
+            **kwargs: any additional information. It has the following reserved fields:
                 "carryover": a string or a list of string to specify the carryover information to be passed to this chat. It can be a string or a list of string.
                     If provided, we will combine this carryover with the "message" content when generating the initial chat
                     message.
@@ -2233,17 +2232,17 @@ def generate_init_message(self, message: Union[Dict, str, None], **context) -> U
         if message is None:
             message = self.get_human_input(">")
         if isinstance(message, str):
-            return self._process_carryover(message, context)
+            return self._process_carryover(message, kwargs)
         elif isinstance(message, dict):
             message = message.copy()
             # TODO: Do we need to do the following?
             # if message.get("content") is None:
             #     message["content"] = self.get_human_input(">")
-            message["content"] = self._process_carryover(message.get("content", ""), context)
+            message["content"] = self._process_carryover(message.get("content", ""), kwargs)
             return message
 
-    def _process_carryover(self, message: str, context: dict) -> str:
-        carryover = context.get("carryover")
+    def _process_carryover(self, message: str, kwargs: dict) -> str:
+        carryover = kwargs.get("carryover")
         if carryover:
             # if carryover is string
             if isinstance(carryover, str):
@@ -2256,7 +2255,7 @@ def _process_carryover(self, message: str, context: dict) -> str:
                 )
         return message
 
-    async def a_generate_init_message(self, message: Union[Dict, str, None], **context) -> Union[str, Dict]:
+    async def a_generate_init_message(self, message: Union[Dict, str, None], **kwargs) -> Union[str, Dict]:
         """Generate the initial message for the agent.
         If message is None, input() will be called to get the initial message.
 
@@ -2269,10 +2268,10 @@ async def a_generate_init_message(self, message: Union[Dict, str, None], **conte
         if message is None:
             message = await self.a_get_human_input(">")
         if isinstance(message, str):
-            return self._process_carryover(message, context)
+            return self._process_carryover(message, kwargs)
         elif isinstance(message, dict):
             message = message.copy()
-            message["content"] = self._process_carryover(message["content"], context)
+            message["content"] = self._process_carryover(message["content"], kwargs)
             return message
 
     def register_function(self, function_map: Dict[str, Union[Callable, None]]):
diff --git a/notebook/agentchat_nested_sequential_chats.ipynb b/notebook/agentchat_nested_sequential_chats.ipynb
index 2f591c2530a..1b6ff0ea62f 100644
--- a/notebook/agentchat_nested_sequential_chats.ipynb
+++ b/notebook/agentchat_nested_sequential_chats.ipynb
@@ -811,7 +811,7 @@
   "front_matter": {
    "description": "Solve complex tasks with one or more sequence chats nested as inner monologue.",
    "tags": [
-    "nested chat"
+    "nested chat", "sequential chat"
    ]
   },
   "kernelspec": {
diff --git a/test/agentchat/test_chats.py b/test/agentchat/test_chats.py
index 153e5cd3bc9..b160520176b 100755
--- a/test/agentchat/test_chats.py
+++ b/test/agentchat/test_chats.py
@@ -232,6 +232,7 @@ def my_summary_method(recipient, sender, summary_args):
                 "message": financial_tasks[0],
                 "silent": False,
                 "summary_method": my_summary_method,
+                "verbose": True,
                 "max_turns": 1,
             },
             {
@@ -240,6 +241,7 @@ def my_summary_method(recipient, sender, summary_args):
                 "silent": False,
                 "max_turns": 1,
                 "summary_method": "reflection_with_llm",
+                "verbose": True,
             },
             {
                 "recipient": financial_assistant_1,

From 91924f558a4cbe4b197fe7b9b27b42b3ed832ecf Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Tue, 26 Mar 2024 16:59:27 +0000
Subject: [PATCH 08/27] Bump webpack-dev-middleware from 5.3.3 to 5.3.4 in
 /website (#2131)

Bumps [webpack-dev-middleware](https://github.com/webpack/webpack-dev-middleware) from 5.3.3 to 5.3.4.
- [Release notes](https://github.com/webpack/webpack-dev-middleware/releases)
- [Changelog](https://github.com/webpack/webpack-dev-middleware/blob/v5.3.4/CHANGELOG.md)
- [Commits](https://github.com/webpack/webpack-dev-middleware/compare/v5.3.3...v5.3.4)

---
updated-dependencies:
- dependency-name: webpack-dev-middleware
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
---
 website/yarn.lock | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/website/yarn.lock b/website/yarn.lock
index 55c0dbdd3d2..cc98c129ee9 100644
--- a/website/yarn.lock
+++ b/website/yarn.lock
@@ -10291,9 +10291,9 @@ webpack-bundle-analyzer@^4.9.0:
     ws "^7.3.1"
 
 webpack-dev-middleware@^5.3.1:
-  version "5.3.3"
-  resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-5.3.3.tgz#efae67c2793908e7311f1d9b06f2a08dcc97e51f"
-  integrity sha512-hj5CYrY0bZLB+eTO+x/j67Pkrquiy7kWepMHmUMoPsmcUaeEnQJqFzHJOyxgWlq746/wUuA64p9ta34Kyb01pA==
+  version "5.3.4"
+  resolved "https://registry.yarnpkg.com/webpack-dev-middleware/-/webpack-dev-middleware-5.3.4.tgz#eb7b39281cbce10e104eb2b8bf2b63fce49a3517"
+  integrity sha512-BVdTqhhs+0IfoeAf7EoH5WE+exCmqGerHfDM0IL096Px60Tq2Mn9MAbnaGUe6HiMa41KMCYF19gyzZmBcq/o4Q==
   dependencies:
     colorette "^2.0.10"
     memfs "^3.4.3"

From 4964cfe6abde8978a620dfd03ed3fced527087c2 Mon Sep 17 00:00:00 2001
From: Wael Karkoub <wael.karkoub96@gmail.com>
Date: Tue, 26 Mar 2024 19:46:44 +0100
Subject: [PATCH 09/27] Parse Any HTML-esh Style Tags (#2046)

* tried implementing my own regex

* improves tests

* finally works

* removes prints

* fixed test

* adds start and end

* delete unused imports

* refactored to use new tool

* significantly improved algo

* tag content -> tag attr

* fix tests + adds new field

* return full match

* return remove start and end

* update docstrings

* update docstrings

* update docstrings

---------

Co-authored-by: Beibin Li <BeibinLi@users.noreply.github.com>
Co-authored-by: Chi Wang <wang.chi@microsoft.com>
---
 autogen/agentchat/contrib/img_utils.py |  17 ++--
 autogen/agentchat/utils.py             | 109 ++++++++++++++++++++++++-
 test/agentchat/test_agentchat_utils.py |  76 +++++++++++++++++
 3 files changed, 191 insertions(+), 11 deletions(-)
 create mode 100644 test/agentchat/test_agentchat_utils.py

diff --git a/autogen/agentchat/contrib/img_utils.py b/autogen/agentchat/contrib/img_utils.py
index 2d259241874..a389c74b064 100644
--- a/autogen/agentchat/contrib/img_utils.py
+++ b/autogen/agentchat/contrib/img_utils.py
@@ -1,14 +1,15 @@
 import base64
 import copy
-import mimetypes
 import os
 import re
 from io import BytesIO
-from typing import Any, Dict, List, Optional, Tuple, Union
+from typing import Dict, List, Tuple, Union
 
 import requests
 from PIL import Image
 
+from autogen.agentchat import utils
+
 
 def get_pil_image(image_file: Union[str, Image.Image]) -> Image.Image:
     """
@@ -179,13 +180,9 @@ def gpt4v_formatter(prompt: str, img_format: str = "uri") -> List[Union[str, dic
     last_index = 0
     image_count = 0
 
-    # Regular expression pattern for matching <img ...> tags
-    img_tag_pattern = re.compile(r"<img ([^>]+)>")
-
     # Find all image tags
-    for match in img_tag_pattern.finditer(prompt):
-        image_location = match.group(1)
-
+    for parsed_tag in utils.parse_tags_from_content("img", prompt):
+        image_location = parsed_tag["attr"]["src"]
         try:
             if img_format == "pil":
                 img_data = get_pil_image(image_location)
@@ -202,12 +199,12 @@ def gpt4v_formatter(prompt: str, img_format: str = "uri") -> List[Union[str, dic
             continue
 
         # Add text before this image tag to output list
-        output.append({"type": "text", "text": prompt[last_index : match.start()]})
+        output.append({"type": "text", "text": prompt[last_index : parsed_tag["match"].start()]})
 
         # Add image data to output list
         output.append({"type": "image_url", "image_url": {"url": img_data}})
 
-        last_index = match.end()
+        last_index = parsed_tag["match"].end()
         image_count += 1
 
     # Add remaining text to output list
diff --git a/autogen/agentchat/utils.py b/autogen/agentchat/utils.py
index fde0b0b88b3..eef3741605d 100644
--- a/autogen/agentchat/utils.py
+++ b/autogen/agentchat/utils.py
@@ -1,4 +1,6 @@
-from typing import Any, List, Dict, Tuple, Callable
+import re
+from typing import Any, Callable, Dict, List, Tuple, Union
+
 from .agent import Agent
 
 
@@ -76,3 +78,108 @@ def aggregate_summary(usage_summary: Dict[str, Any], agent_summary: Dict[str, An
             aggregate_summary(actual_usage_summary, agent.client.actual_usage_summary)
 
     return total_usage_summary, actual_usage_summary
+
+
+def parse_tags_from_content(tag: str, content: Union[str, List[Dict[str, Any]]]) -> List[Dict[str, Dict[str, str]]]:
+    """Parses HTML style tags from message contents.
+
+    The parsing is done by looking for patterns in the text that match the format of HTML tags. The tag to be parsed is
+    specified as an argument to the function. The function looks for this tag in the text and extracts its content. The
+    content of a tag is everything that is inside the tag, between the opening and closing angle brackets. The content
+    can be a single string or a set of attribute-value pairs.
+
+    Examples:
+        <img http://example.com/image.png> -> [{"tag": "img", "attr": {"src": "http://example.com/image.png"}, "match": re.Match}]
+        <audio text="Hello I'm a robot" prompt="whisper"> ->
+                [{"tag": "audio", "attr": {"text": "Hello I'm a robot", "prompt": "whisper"}, "match": re.Match}]
+
+    Args:
+        tag (str): The HTML style tag to be parsed.
+        content (Union[str, List[Dict[str, Any]]]): The message content to parse. Can be a string or a list of content
+            items.
+
+    Returns:
+        List[Dict[str, str]]: A list of dictionaries, where each dictionary represents a parsed tag. Each dictionary
+            contains three key-value pairs: 'type' which is the tag, 'attr' which is a dictionary of the parsed attributes,
+            and 'match' which is a regular expression match object.
+
+    Raises:
+        ValueError: If the content is not a string or a list.
+    """
+    results = []
+    if isinstance(content, str):
+        results.extend(_parse_tags_from_text(tag, content))
+    # Handles case for multimodal messages.
+    elif isinstance(content, list):
+        for item in content:
+            if item.get("type") == "text":
+                results.extend(_parse_tags_from_text(tag, item["text"]))
+    else:
+        raise ValueError(f"content must be str or list, but got {type(content)}")
+
+    return results
+
+
+def _parse_tags_from_text(tag: str, text: str) -> List[Dict[str, str]]:
+    pattern = re.compile(f"<{tag} (.*?)>")
+
+    results = []
+    for match in re.finditer(pattern, text):
+        tag_attr = match.group(1).strip()
+        attr = _parse_attributes_from_tags(tag_attr)
+
+        results.append({"tag": tag, "attr": attr, "match": match})
+    return results
+
+
+def _parse_attributes_from_tags(tag_content: str):
+    pattern = r"([^ ]+)"
+    attrs = re.findall(pattern, tag_content)
+    reconstructed_attrs = _reconstruct_attributes(attrs)
+
+    def _append_src_value(content, value):
+        if "src" in content:
+            content["src"] += f" {value}"
+        else:
+            content["src"] = value
+
+    content = {}
+    for attr in reconstructed_attrs:
+        if "=" not in attr:
+            _append_src_value(content, attr)
+            continue
+
+        key, value = attr.split("=", 1)
+        if value.startswith("'") or value.startswith('"'):
+            content[key] = value[1:-1]  # remove quotes
+        else:
+            _append_src_value(content, attr)
+
+    return content
+
+
+def _reconstruct_attributes(attrs: List[str]) -> List[str]:
+    """Reconstructs attributes from a list of strings where some attributes may be split across multiple elements."""
+
+    def is_attr(attr: str) -> bool:
+        if "=" in attr:
+            _, value = attr.split("=", 1)
+            if value.startswith("'") or value.startswith('"'):
+                return True
+        return False
+
+    reconstructed = []
+    found_attr = False
+    for attr in attrs:
+        if is_attr(attr):
+            reconstructed.append(attr)
+            found_attr = True
+        else:
+            if found_attr:
+                reconstructed[-1] += f" {attr}"
+                found_attr = True
+            elif reconstructed:
+                reconstructed[-1] += f" {attr}"
+            else:
+                reconstructed.append(attr)
+    return reconstructed
diff --git a/test/agentchat/test_agentchat_utils.py b/test/agentchat/test_agentchat_utils.py
new file mode 100644
index 00000000000..99ccb9c1393
--- /dev/null
+++ b/test/agentchat/test_agentchat_utils.py
@@ -0,0 +1,76 @@
+from typing import Dict, List, Union
+from autogen import agentchat
+import pytest
+
+TAG_PARSING_TESTS = [
+    {
+        "message": "Hello agent, can you take a look at this image <img http://example.com/image.png>",
+        "expected": [{"tag": "img", "attr": {"src": "http://example.com/image.png"}}],
+    },
+    {
+        "message": "Can you transcribe this audio? <audio http://example.com/au=dio.mp3>",
+        "expected": [{"tag": "audio", "attr": {"src": "http://example.com/au=dio.mp3"}}],
+    },
+    {
+        "message": "Can you describe what's in this image <img url='http://example.com/=image.png'>",
+        "expected": [{"tag": "img", "attr": {"url": "http://example.com/=image.png"}}],
+    },
+    {
+        "message": "Can you describe what's in this image <img http://example.com/image.png> and transcribe this audio? <audio http://example.com/audio.mp3>",
+        "expected": [
+            {"tag": "img", "attr": {"src": "http://example.com/image.png"}},
+            {"tag": "audio", "attr": {"src": "http://example.com/audio.mp3"}},
+        ],
+    },
+    {
+        "message": "Can you generate this audio? <audio text='Hello I'm a robot' prompt='whisper'>",
+        "expected": [{"tag": "audio", "attr": {"text": "Hello I'm a robot", "prompt": "whisper"}}],
+    },
+    {
+        "message": "Can you describe what's in this image <img http://example.com/image.png width='100'> and this image <img http://hello.com/image=.png>?",
+        "expected": [
+            {"tag": "img", "attr": {"src": "http://example.com/image.png", "width": "100"}},
+            {"tag": "img", "attr": {"src": "http://hello.com/image=.png"}},
+        ],
+    },
+    {
+        "message": "Text with no tags",
+        "expected": [],
+    },
+]
+
+
+def _delete_unused_keys(d: Dict) -> None:
+    if "match" in d:
+        del d["match"]
+
+
+@pytest.mark.parametrize("test_case", TAG_PARSING_TESTS)
+def test_tag_parsing(test_case: Dict[str, Union[str, List[Dict[str, Union[str, Dict[str, str]]]]]]) -> None:
+    """Test the tag_parsing function."""
+    message = test_case["message"]
+    expected = test_case["expected"]
+    tags = ["img", "audio", "random"]
+
+    result = []
+    for tag in tags:
+        parsed_tags = agentchat.utils.parse_tags_from_content(tag, message)
+        for item in parsed_tags:
+            _delete_unused_keys(item)
+
+        result.extend(parsed_tags)
+    assert result == expected
+
+    result = []
+    for tag in tags:
+        content = [{"type": "text", "text": message}]
+        parsed_tags = agentchat.utils.parse_tags_from_content(tag, content)
+        for item in parsed_tags:
+            _delete_unused_keys(item)
+
+        result.extend(parsed_tags)
+    assert result == expected
+
+
+if __name__ == "__main__":
+    test_tag_parsing(TAG_PARSING_TESTS[0])

From cd7d91da172a3d438ed37586ce52d52e9ae6dd78 Mon Sep 17 00:00:00 2001
From: Shaokun Zhang <shaokunzhang529@gmail.com>
Date: Tue, 26 Mar 2024 16:31:02 -0400
Subject: [PATCH 10/27] Integrate AgentOptimizer (#1767)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* draft agent optimizer

* refactor

* remove

* change openai config interface

* notebook

* update blog

* add test

* clean up

* redir

* update

* update interface

* change model name

* move to contrib

* Update autogen/agentchat/contrib/agent_optimizer.py

Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>

---------

Co-authored-by: “skzhang1” <“shaokunzhang529@gmail.com”>
Co-authored-by: Beibin Li <BeibinLi@users.noreply.github.com>
Co-authored-by: Jieyu Zhang <jieyuz2@cs.washington.edu>
Co-authored-by: Jack Gerrits <jackgerrits@users.noreply.github.com>
---
 autogen/agentchat/contrib/agent_optimizer.py  | 440 ++++++++++++++
 notebook/agentchat_agentoptimizer.ipynb       | 546 +++---------------
 .../agentchat/contrib/test_agent_optimizer.py | 110 ++++
 .../blog/2023-12-23-AgentOptimizer/index.mdx  | 144 ++---
 4 files changed, 708 insertions(+), 532 deletions(-)
 create mode 100644 autogen/agentchat/contrib/agent_optimizer.py
 create mode 100644 test/agentchat/contrib/test_agent_optimizer.py

diff --git a/autogen/agentchat/contrib/agent_optimizer.py b/autogen/agentchat/contrib/agent_optimizer.py
new file mode 100644
index 00000000000..711874efc8f
--- /dev/null
+++ b/autogen/agentchat/contrib/agent_optimizer.py
@@ -0,0 +1,440 @@
+from autogen.code_utils import execute_code
+from typing import List, Dict, Optional
+import json
+import copy
+import autogen
+
+ADD_FUNC = {
+    "type": "function",
+    "function": {
+        "name": "add_function",
+        "description": "Add a function in the context of the conversation. Necessary Python packages must be declared. The name of the function MUST be the same with the function name in the code you generated.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string", "description": "The name of the function in the code implementation."},
+                "description": {"type": "string", "description": "A short description of the function."},
+                "arguments": {
+                    "type": "string",
+                    "description": 'JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { "url": { "type": "string", "description": "The URL", }}. Please avoid the error \'array schema missing items\' when using array type.',
+                },
+                "packages": {
+                    "type": "string",
+                    "description": "A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list.",
+                },
+                "code": {
+                    "type": "string",
+                    "description": "The implementation in Python. Do not include the function declaration.",
+                },
+            },
+            "required": ["name", "description", "arguments", "packages", "code"],
+        },
+    },
+}
+
+REVISE_FUNC = {
+    "type": "function",
+    "function": {
+        "name": "revise_function",
+        "description": "Revise a function in the context of the conversation. Necessary Python packages must be declared. The name of the function MUST be the same with the function name in the code you generated.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string", "description": "The name of the function in the code implementation."},
+                "description": {"type": "string", "description": "A short description of the function."},
+                "arguments": {
+                    "type": "string",
+                    "description": 'JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { "url": { "type": "string", "description": "The URL", }}. Please avoid the error \'array schema missing items\' when using array type.',
+                },
+                "packages": {
+                    "type": "string",
+                    "description": "A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list.",
+                },
+                "code": {
+                    "type": "string",
+                    "description": "The implementation in Python. Do not include the function declaration.",
+                },
+            },
+            "required": ["name", "description", "arguments", "packages", "code"],
+        },
+    },
+}
+
+REMOVE_FUNC = {
+    "type": "function",
+    "function": {
+        "name": "remove_function",
+        "description": "Remove one function in the context of the conversation. Once remove one function, the assistant will not use this function in future conversation.",
+        "parameters": {
+            "type": "object",
+            "properties": {
+                "name": {"type": "string", "description": "The name of the function in the code implementation."}
+            },
+            "required": ["name"],
+        },
+    },
+}
+
+OPT_PROMPT = """You are a function optimizer. Your task is to maintain a list of functions for the assistant according to the existing function list and conversation history that happens between the assistant and the user.
+You can perform one of the following four actions to manipulate the function list using the functions you have:
+1. Revise one existing function (using revise_function).
+2. Remove one existing function (using remove_function).
+3. Add one new function (using add_function).
+4. Directly return "TERMINATE" to me if no more actions are needed for the current function list.
+
+Below are the principles that you need to follow for taking these four actions.
+(1) Revise one existing function:
+1. Pay more attention to the failed tasks and corresponding error information, and optimize the function used in these tasks according to the conversation history if needed.
+2. A failed function call can occur due to incorrect input arguments (missing arguments) or an incorrect function code implementation. You should focus more on the function code implementation and make it easy to get success function call.
+3. Do not revise the function that you think works well and plays a critical role in solving the problems according to the conversation history. Only making revisions if needed.
+4. Sometimes, a NameError may occur. To fix this error, you can either revise the name of the function in the code implementation or revise the name of the function call to make these two names consistent.
+(2) Remove one existing function:
+1. Only remove the function that you think is not needed anymore in future tasks.
+(3) Add one new function:
+1. The added function should be general enough to be used in future tasks. For instance, if you encounter a problem that this function can solve, or one step of it, you can use the generated function directly instead of starting from scratch
+2. The added new function should solve a higher-level question that encompasses the original query and extend the code's functionality to make it more versatile and widely applicable.
+3. Replace specific strings or variable names with general variables to enhance the tool's applicability to various queries. All names used inside the function should be passed in as arguments.
+Below is an example of a function that potentially deserves to be adde in solving MATH problems, which can be used to solve a higher-level question:
+{{
+    \"name\": \"evaluate_expression\",
+    \"description\": \"Evaluate arithmetic or mathematical expressions provided as strings.\",
+    \"arguments\": {{
+        \"expression\": {{
+            \"type\": \"string\",
+            \"description\": \"The mathematical expression to evaluate.\"
+        }}
+    }},
+    \"packages\": \"sympy\",
+    \"code\": \"from sympy import sympify, SympifyError\\n\\ndef evaluate_expression(expression):\\n    try:\\n        result = sympify(expression)\\n        if result.is_number:\\n            result = float(result)\\n        else:\\n            result = str(result)\\n        return result\\n    except SympifyError as e:\\n        return str(e)\"
+}}
+(4) Directly return "TERMINATE":
+If you think there is no need to perform any other actions for the current function list since the current list is optimal more actions will harm the performance in future tasks. Please directly reply to me with "TERMINATE".
+
+One function signature includes the following five elements:
+1. Function name
+2. Function description
+3. JSON schema of arguments encoded as a string
+4. A list of package names imported by the function packages
+5. The code implementation
+
+Below are the signatures of the current functions:
+List A: {best_functions}.
+The following list are the function signatures that you have after taking {actions_num} actions to manipulate List A:
+List B: {incumbent_functions}.
+
+{accumulated_experience}
+
+Here are {best_conversations_num} conversation histories of solving {best_conversations_num} tasks using List A.
+History:
+{best_conversations_history}
+
+{statistic_informations}
+
+According to the information I provide, please take one of four actions to manipulate list B using the functions you know.
+Instead of returning TERMINATE directly or taking no action, you should try your best to optimize the function list. Only take no action if you really think the current list is optimal, as more actions will harm performance in future tasks.
+Even adding a general function that can substitute the assistant’s repeated suggestions of Python code with the same functionality could also be helpful.
+"""
+
+
+def execute_func(name, packages, code, **args):
+    """
+    The wrapper for generated functions.
+    """
+    pip_install = (
+        f"""print("Installing package: {packages}")\nsubprocess.run(["pip", "-qq", "install", "{packages}"])"""
+        if packages
+        else ""
+    )
+    str = f"""
+import subprocess
+{pip_install}
+print("Result of {name} function execution:")
+{code}
+args={args}
+result={name}(**args)
+if result is not None: print(result)
+"""
+    print(f"execute_code:\n{str}")
+    result = execute_code(str, use_docker="shaokun529/evoagent:v1")
+    if result[0] != 0:
+        raise Exception("Error in executing function:" + result[1])
+    print(f"Result: {result[1]}")
+    return result[1]
+
+
+class AgentOptimizer:
+    """
+    Base class for optimizing AutoGen agents. Specifically, it is used to optimize the functions used in the agent.
+    More information could be found in the following paper: https://arxiv.org/abs/2402.11359.
+    """
+
+    def __init__(
+        self,
+        max_actions_per_step: int,
+        config_file_or_env: Optional[str] = "OAI_CONFIG_LIST",
+        config_file_location: Optional[str] = "",
+        optimizer_model: Optional[str] = "gpt-4-1106-preview",
+    ):
+        """
+        (These APIs are experimental and may change in the future.)
+        Args:
+            max_actions_per_step (int): the maximum number of actions that the optimizer can take in one step.
+            config_file_or_env: path or environment of the OpenAI api configs.
+            config_file_location: the location of the OpenAI config file.
+            optimizer_model: the model used for the optimizer.
+        """
+        self.max_actions_per_step = max_actions_per_step
+        self._max_trials = 3
+        self.optimizer_model = optimizer_model
+
+        self._trial_conversations_history = []
+        self._trial_conversations_performance = []
+        self._trial_functions = []
+
+        self._best_conversations_history = []
+        self._best_conversations_performance = []
+        self._best_functions = []
+
+        self._failure_functions_performance = []
+        self._best_performance = -1
+
+        config_list = autogen.config_list_from_json(
+            config_file_or_env,
+            file_location=config_file_location,
+            filter_dict={"model": [self.optimizer_model]},
+        )
+        if len(config_list) == 0:
+            raise RuntimeError("No valid openai config found in the config file or environment variable.")
+        self._client = autogen.OpenAIWrapper(config_list=config_list)
+
+    def record_one_conversation(self, conversation_history: List[Dict], is_satisfied: bool = None):
+        """
+        record one conversation history.
+        Args:
+            conversation_history (List[Dict]): the chat messages of the conversation.
+            is_satisfied (bool): whether the user is satisfied with the solution. If it is none, the user will be asked to input the satisfaction.
+        """
+        if is_satisfied is None:
+            reply = input(
+                "Please provide whether the user is satisfied with the solution. 1 represents satisfied. 0 represents not satisfied. Press enter to submit. \n"
+            )
+            assert reply in [
+                "0",
+                "1",
+            ], "The input is invalid. Please input 1 or 0. 1 represents satisfied. 0 represents not satisfied."
+            is_satisfied = True if reply == "1" else False
+        self._trial_conversations_history.append(
+            {"Conversation {i}".format(i=len(self._trial_conversations_history)): conversation_history}
+        )
+        self._trial_conversations_performance.append(
+            {"Conversation {i}".format(i=len(self._trial_conversations_performance)): 1 if is_satisfied else 0}
+        )
+
+    def step(self):
+        """
+        One step of training. It will return register_for_llm and register_for_executor at each iteration,
+        which are subsequently utilized to update the assistant and executor agents, respectively.
+        See example: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb
+        """
+        performance = sum(sum(d.values()) for d in self._trial_conversations_performance) / len(
+            self._trial_conversations_performance
+        )
+
+        if performance < self._best_performance:
+            self._failure_functions_performance.append({"functions": self._trial_functions, "performance": performance})
+            self._failure_functions_performance = sorted(
+                self._failure_functions_performance, key=lambda x: x["performance"]
+            )
+        else:
+            self._failure_functions_performance = []
+            self._best_performance = performance
+            self._best_functions = copy.deepcopy(self._trial_functions)
+            self._best_conversations_history = copy.deepcopy(self._trial_conversations_history)
+            self._best_conversations_performance = copy.deepcopy(self._trial_conversations_performance)
+        self._trial_conversations_history = []
+        self._trial_conversations_performance = []
+
+        best_functions = copy.deepcopy(self._best_functions)
+        incumbent_functions = copy.deepcopy(self._best_functions)
+        failure_experience_prompt, statistic_prompt = self._construct_intermediate_prompt()
+
+        for action_index in range(self.max_actions_per_step):
+            prompt = OPT_PROMPT.format(
+                best_conversations_history=self._best_conversations_history,
+                best_conversations_num=len(self._best_conversations_history),
+                actions_num=action_index,
+                best_functions=best_functions,
+                incumbent_functions=incumbent_functions,
+                accumerated_experience=failure_experience_prompt,
+                statistic_informations=statistic_prompt,
+            )
+            messages = [{"role": "user", "content": prompt}]
+            for _ in range(self._max_trials):
+                response = self._client.create(
+                    messages=messages, tools=[ADD_FUNC, REVISE_FUNC, REMOVE_FUNC], tool_choice="auto"
+                )
+                actions = response.choices[0].message.tool_calls
+                if self._validate_actions(actions, incumbent_functions):
+                    break
+            if actions is not None and self._validate_actions(actions, incumbent_functions):
+                incumbent_functions = self._update_function_call(incumbent_functions, actions)
+
+        remove_functions = list(
+            set([key for dictionary in self._trial_functions for key in dictionary.keys()])
+            - set([key for dictionary in incumbent_functions for key in dictionary.keys()])
+        )
+
+        register_for_llm = []
+        register_for_exector = {}
+        for name in remove_functions:
+            register_for_llm.append({"func_sig": {"name": name}, "is_remove": True})
+            register_for_exector.update({name: None})
+        for func in incumbent_functions:
+            register_for_llm.append(
+                {
+                    "func_sig": {
+                        "name": func.get("name"),
+                        "description": func.get("description"),
+                        "parameters": {"type": "object", "properties": func.get("arguments")},
+                    },
+                    "is_remove": False,
+                }
+            )
+            register_for_exector.update(
+                {
+                    func.get("name"): lambda **args: execute_func(
+                        func.get("name"), func.get("packages"), func.get("code"), **args
+                    )
+                }
+            )
+
+        self._trial_functions = incumbent_functions
+        return register_for_llm, register_for_exector
+
+    def reset_optimizer(self):
+        """
+        reset the optimizer.
+        """
+
+        self._trial_conversations_history = []
+        self._trial_conversations_performance = []
+        self._trial_functions = []
+
+        self._best_conversations_history = []
+        self._best_conversations_performance = []
+        self._best_functions = []
+
+        self._best_performance = -1
+        self._failure_functions_performance = []
+
+    def _update_function_call(self, incumbent_functions, actions):
+        """
+        update function call.
+        """
+
+        formated_actions = []
+        for action in actions:
+            func = json.loads(action.function.arguments.strip('"'))
+            func["action_name"] = action.function.name
+
+            if func.get("action_name") == "remove_function":
+                item = {
+                    "action_name": func.get("action_name"),
+                    "name": func.get("name"),
+                }
+            else:
+                item = {
+                    "action_name": func.get("action_name"),
+                    "name": func.get("name"),
+                    "description": func.get("description"),
+                    "arguments": json.loads(func.get("arguments").strip('"')),
+                    "packages": func.get("packages"),
+                    "code": func.get("code"),
+                }
+            formated_actions.append(item)
+        actions = formated_actions
+
+        for action in actions:
+            name, description, arguments, packages, code, action_name = (
+                action.get("name"),
+                action.get("description"),
+                action.get("arguments"),
+                action.get("packages"),
+                action.get("code"),
+                action.get("action_name"),
+            )
+            if action_name == "remove_function":
+                incumbent_functions = [item for item in incumbent_functions if item["name"] != name]
+            else:
+                incumbent_functions = [item for item in incumbent_functions if item["name"] != name]
+                incumbent_functions.append(
+                    {
+                        "name": name,
+                        "description": description,
+                        "arguments": arguments,
+                        "packages": packages,
+                        "code": code,
+                    }
+                )
+
+        return incumbent_functions
+
+    def _construct_intermediate_prompt(self):
+        """
+        construct intermediate prompts.
+        """
+        if len(self._failure_functions_performance) != 0:
+            failure_experience_prompt = "We also provide more examples for different functions and their corresponding performance (0-100).\n The following function signatures are arranged in are arranged in ascending order based on their performance, where higher performance indicate better quality."
+            failure_experience_prompt += "\n"
+            for item in self._failure_functions_performance:
+                failure_experience_prompt += "Function: \n" + str(item["functions"]) + "\n"
+                failure_experience_prompt += "Performance: \n" + str(item["performance"]) + "\n"
+        else:
+            failure_experience_prompt = "\n"
+
+        if len(self._best_conversations_performance) != 0:
+            statistic_prompt = "The following table shows the statistical information for solving each task in each conversation and indicates, whether the result is satisfied by the users. 1 represents satisfied. 0 represents not satisfied."
+            statistic_prompt += "\n"
+            for item in self._best_conversations_performance:
+                statistic_prompt += str(item) + "\n"
+        else:
+            statistic_prompt = "\n"
+
+        return failure_experience_prompt, statistic_prompt
+
+    def _validate_actions(self, actions, incumbent_functions):
+        """
+        validate whether the proposed actions are feasible.
+        """
+        if actions is None:
+            return True
+        else:
+            # val json format
+            for action in actions:
+                function_args = action.function.arguments
+                try:
+                    function_args = json.loads(function_args.strip('"'))
+                    if "arguments" in function_args.keys():
+                        json.loads(function_args.get("arguments").strip('"'))
+                except Exception as e:
+                    print("JSON is invalid:", e)
+                    return False
+            # val syntax
+            for action in actions:
+                if action.function.name != "remove_function":
+                    function_args = json.loads(action.function.arguments.strip('"'))
+                    code = function_args.get("code")
+                    try:
+                        compile(code, "<string>", "exec")
+                        print("successfully compiled")
+                    except Exception as e:
+                        print("Syntax is invalid:", e)
+                        return False
+            for action in actions:
+                action_name = action.function.name
+                if action_name == "remove_function":
+                    function_args = json.loads(action.function.arguments.strip('"'))
+                    if function_args.get("name") not in [item["name"] for item in incumbent_functions]:
+                        print("The function you want to remove does not exist.")
+                        return False
+        return True
diff --git a/notebook/agentchat_agentoptimizer.ipynb b/notebook/agentchat_agentoptimizer.ipynb
index a619ce55138..4b7de9715be 100644
--- a/notebook/agentchat_agentoptimizer.ipynb
+++ b/notebook/agentchat_agentoptimizer.ipynb
@@ -11,14 +11,15 @@
     "\n",
     "In traditional ML pipeline, we train a model by updating its parameter according to the loss on the training set, while in the era of LLM agents, how should we train an agent? Here, we take an initial step towards the agent training. Inspired by the [function calling](https://platform.openai.com/docs/guides/function-calling) capabilities provided by OpenAI, we draw an analogy between model parameters and agent functions/skills, and update agent’s functions/skills based on its historical performance on the training set. As an agentic way of training an agent, our approach help enhance the agents’ abilities without requiring access to the LLMs parameters.\n",
     "\n",
-    "In this notebook, we introduce a new class, ‘AgentOptimizer’, which is able to improve the function list of one Assistant-UserProxy pair according to the historical conversation histories. This feature would support agents in improving their ability to solve problems of the same type as previous tasks.\n",
-    "Specifically, given a set of training data, AgentOptimizer would iteratively prompt the LLM to optimize the existing function list of the AssistantAgent and UserProxyAgent with code implementation if necessary.\n",
+    "In this notebook, we introduce a new class, ‘AgentOptimizer’, which is able to improve the function list of one Assistant-UserProxy pair according to the historical conversation histories.\n",
+    "This feature would support agents in improving their ability to solve problems of the same type as previous tasks.\n",
+    "Specifically, given a set of training data, AgentOptimizer would iteratively prompt the LLM to optimize the existing function list of the AssistantAgent and UserProxyAgent with code implementation if necessary. It also includes two strategies, roll-back, and early-stop, to streamline the training process.\n",
     "In the example scenario, we test the proposed AgentOptimizer in solving problems from the [MATH dataset](https://github.com/hendrycks/math). \n",
     "\n",
-    "Paper is coming soon!\n",
-    "\n",
     "![AgentEval](../website/blog/2023-12-23-AgentOptimizer/img/agentoptimizer.png)\n",
     "\n",
+    "More information could be found in the [paper](https://arxiv.org/abs/2402.11359).\n",
+    "\n",
     "Authors:\n",
     "- [Shaokun Zhang](https://github.com/skzhang1), Ph.D. student at the The Pennsylvania State University\n",
     "- [Jieyu Zhang](https://jieyuz2.github.io), Ph.D. student at the University of Washington"
@@ -26,351 +27,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [],
    "source": [
-    "import json\n",
-    "import os\n",
     "from typing import Any, Callable, Dict, List, Optional, Tuple, Union\n",
-    "\n",
-    "from openai import AzureOpenAI, BadRequestError\n",
-    "\n",
-    "import autogen\n",
-    "from autogen.agentchat import Agent\n",
+    "from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer\n",
     "from autogen.agentchat.contrib.math_user_proxy_agent import MathUserProxyAgent\n",
-    "from autogen.code_utils import execute_code, extract_code\n",
-    "from autogen.math_utils import get_answer"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# AgentOptimizer\n",
-    "\n",
-    "AgentOptimizer is a class that is designed to improve the agents through optimizing its function call. It contains two core methods:\n",
-    "\n",
-    "1. `step()`: `step()` has three inputs: previous conversation history (history), the statistical information of solving previous problems (statistic), and the signature of current functions (func_signature). The output is a series of actions to manipulate the current functions.\n",
-    "\n",
-    "2. `update_function_call()`: This method updates the functions registered in the agents according to the actions from `step()`.   "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "class AgentOptimizer:\n",
-    "    OPT_PROMPT = \"\"\"You are a function optimizer. Your task is to maintain a list of functions for the assistant according to the existing function list and conversation history that happens between the assistant and the user.\n",
-    "You can perform one of the following four actions to manipulate the function list using the functions you have:\n",
-    "1. Revise one existing function (using revise_function).\n",
-    "2. Remove one existing function (using remove_function).\n",
-    "3. Add one new function (using add_function).\n",
-    "4. Directly return \"TERMINATE\" to me if no more actions are needed for the current function list.\n",
-    "\n",
-    "Below are the principles that you need to follow for taking these four actions.\n",
-    "(1) Revise one existing function:\n",
-    "1. Pay more attention to the failed tasks and corresponding error information, and optimize the function used in these tasks according to the conversation history if needed.\n",
-    "2. A failed function call can occur due to incorrect input arguments (missing arguments) or an incorrect function code implementation. You should focus more on the function code implementation and make it easy to get success function call.\n",
-    "3. Do not revise the function that you think works well and plays a critical role in solving the problems according to the conversation history. Only making revisions if needed.\n",
-    "4. Sometimes, a NameError may occur. To fix this error, you can either revise the name of the function in the code implementation or revise the name of the function call to make these two names consistent.\n",
-    "(2) Remove one existing function:\n",
-    "1. Only remove the function that you think is not needed anymore in future tasks.\n",
-    "(3) Add one new function:\n",
-    "1. The added new function should solve a higher-level question that encompasses the original query and extend the code's functionality to make it more versatile and widely applicable.\n",
-    "2. The added new function should solve queries of the same type, based on common reasoning steps without mentioning specific object names or entity terms.\n",
-    "3. Name the function and write the description concerning both the core reasoning pattern and data organization format, without referencing specific objects. The name of the function MUST be the same with the function name in the code you generated.\n",
-    "4. Replace specific strings or variable names with general variables to enhance the tool's applicability to various queries. All names used inside the function should be passed in as arguments.\n",
-    "(4) Directly return \"TERMINATE\":\n",
-    "If you think there is no need to perform any other actions for the current function list since the current list is optimal more actions will harm the performance in future tasks. Please directly reply to me with \"TERMINATE\".\n",
-    "\n",
-    "One function signature includes the following five elements:\n",
-    "1. Function name\n",
-    "2. Function description\n",
-    "3. JSON schema of arguments encoded as a string\n",
-    "4. A list of package names imported by the function packages\n",
-    "5. The code implementation\n",
-    "\n",
-    "Below are the signatures of the current functions:\n",
-    "List A: {signiture}.\n",
-    "The success rate (performance) with this function list is {success_rate}.\n",
-    "The following list are the function signatures that you have after taking {actions_num} actions in our previous conversations:\n",
-    "List B: {after_signiture}.\n",
-    "Here are {conversation_num} conversation histories of solving {conversation_num} tasks.\n",
-    "History:\n",
-    "{history}\n",
-    "The following table shows the statistical information for solving each task in each conversation and indicates whether each task was successfully solved.\n",
-    "1 represents correct. 0 represents wrong.\n",
-    "statistic:\n",
-    "{statistic}\n",
-    "\n",
-    "According to the information I provide, please take one of four actions to manipulate list B using the functions you know.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    ADD_FUNC = {\n",
-    "        \"type\": \"function\",\n",
-    "        \"function\": {\n",
-    "            \"name\": \"add_function\",\n",
-    "            \"description\": \"Add a function in the context of the conversation. Necessary Python packages must be declared. The name of the function MUST be the same with the function name in the code you generated.\",\n",
-    "            \"parameters\": {\n",
-    "                \"type\": \"object\",\n",
-    "                \"properties\": {\n",
-    "                    \"name\": {\"type\": \"string\", \"description\": \"The name of the function in the code implementation.\"},\n",
-    "                    \"description\": {\"type\": \"string\", \"description\": \"A short description of the function.\"},\n",
-    "                    \"arguments\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": 'JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { \"url\": { \"type\": \"string\", \"description\": \"The URL\", }}. Please avoid the error \\'array schema missing items\\' when using array type.',\n",
-    "                    },\n",
-    "                    \"packages\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": \"A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list.\",\n",
-    "                    },\n",
-    "                    \"code\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": \"The implementation in Python. Do not include the function declaration.\",\n",
-    "                    },\n",
-    "                },\n",
-    "                \"required\": [\"name\", \"description\", \"arguments\", \"packages\", \"code\"],\n",
-    "            },\n",
-    "        },\n",
-    "    }\n",
-    "\n",
-    "    REVISE_FUNC = {\n",
-    "        \"type\": \"function\",\n",
-    "        \"function\": {\n",
-    "            \"name\": \"revise_function\",\n",
-    "            \"description\": \"Revise a function in the context of the conversation. Necessary Python packages must be declared. The name of the function MUST be the same with the function name in the code you generated.\",\n",
-    "            \"parameters\": {\n",
-    "                \"type\": \"object\",\n",
-    "                \"properties\": {\n",
-    "                    \"name\": {\"type\": \"string\", \"description\": \"The name of the function in the code implementation.\"},\n",
-    "                    \"description\": {\"type\": \"string\", \"description\": \"A short description of the function.\"},\n",
-    "                    \"arguments\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": 'JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { \"url\": { \"type\": \"string\", \"description\": \"The URL\", }}. Please avoid the error \\'array schema missing items\\' when using array type.',\n",
-    "                    },\n",
-    "                    \"packages\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": \"A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list.\",\n",
-    "                    },\n",
-    "                    \"code\": {\n",
-    "                        \"type\": \"string\",\n",
-    "                        \"description\": \"The implementation in Python. Do not include the function declaration.\",\n",
-    "                    },\n",
-    "                },\n",
-    "                \"required\": [\"name\", \"description\", \"arguments\", \"packages\", \"code\"],\n",
-    "            },\n",
-    "        },\n",
-    "    }\n",
-    "\n",
-    "    REMOVE_FUNC = {\n",
-    "        \"type\": \"function\",\n",
-    "        \"function\": {\n",
-    "            \"name\": \"remove_function\",\n",
-    "            \"description\": \"Remove one function in the context of the conversation. Once remove one function, the assistant will not use this function in future conversation.\",\n",
-    "            \"parameters\": {\n",
-    "                \"type\": \"object\",\n",
-    "                \"properties\": {\n",
-    "                    \"name\": {\"type\": \"string\", \"description\": \"The name of the function in the code implementation.\"}\n",
-    "                },\n",
-    "                \"required\": [\"name\"],\n",
-    "            },\n",
-    "        },\n",
-    "    }\n",
-    "\n",
-    "    def __init__(self, OAI_config, action_num=3, each_action_max_trials=10):\n",
-    "        self._action_num = action_num\n",
-    "        self._each_action_max_trials = each_action_max_trials\n",
-    "        os.environ[\"AZURE_OPENAI_API_KEY\"] = OAI_config[\"AZURE_OPENAI_API_KEY\"]  # TODO: input key into client\n",
-    "        self._client = AzureOpenAI(\n",
-    "            api_version=OAI_config[\"api_version\"],\n",
-    "            azure_endpoint=OAI_config[\"azure_endpoint\"],\n",
-    "        )\n",
-    "        self.model = \"gpt-4-1106-preview\"\n",
-    "\n",
-    "    def _val_json(self, actions):\n",
-    "        if actions is None:\n",
-    "            return True\n",
-    "        else:\n",
-    "            for action in actions:\n",
-    "                function_args = action.function.arguments\n",
-    "                try:\n",
-    "                    function_args = json.loads(function_args.strip('\"'))\n",
-    "                    if \"arguments\" in function_args.keys():\n",
-    "                        json.loads(function_args.get(\"arguments\").strip('\"'))\n",
-    "                except Exception as e:\n",
-    "                    print(\"JSON is invalid:\", e)\n",
-    "                    return False\n",
-    "        return True\n",
-    "\n",
-    "    def _val_remove(self, actions, after_signiture):\n",
-    "        if actions is None:\n",
-    "            return True\n",
-    "        else:\n",
-    "            for action in actions:\n",
-    "                action_name = action.function.name\n",
-    "                if action_name == \"remove_function\":\n",
-    "                    function_args = json.loads(action.function.arguments.strip('\"'))\n",
-    "                    if function_args.get(\"name\") not in [item[\"name\"] for item in after_signiture]:\n",
-    "                        print(\"The function you want to remove does not exist.\")\n",
-    "                        return False\n",
-    "            return True\n",
-    "\n",
-    "    def _val_syntax(self, actions):\n",
-    "        if actions is None:\n",
-    "            return True\n",
-    "        else:\n",
-    "            for action in actions:\n",
-    "                if action.function.name != \"remove_function\":\n",
-    "                    function_args = json.loads(action.function.arguments.strip('\"'))\n",
-    "                    code = function_args.get(\"code\")\n",
-    "                    try:\n",
-    "                        compile(code, \"<string>\", \"exec\")\n",
-    "                        print(\"successfully compiled\")\n",
-    "                    except SyntaxError as e:\n",
-    "                        print(\"Syntax is invalid:\", e)\n",
-    "                        return False\n",
-    "            return True\n",
-    "\n",
-    "    def _format_actions(self, actions):\n",
-    "        ans = []\n",
-    "        for action in actions:\n",
-    "            func = json.loads(action.function.arguments.strip('\"'))\n",
-    "            func[\"action_name\"] = action.function.name\n",
-    "\n",
-    "            if func.get(\"action_name\") == \"remove_function\":\n",
-    "                item = {\n",
-    "                    \"action_name\": func.get(\"action_name\"),\n",
-    "                    \"name\": func.get(\"name\"),\n",
-    "                }\n",
-    "            else:\n",
-    "                item = {\n",
-    "                    \"action_name\": func.get(\"action_name\"),\n",
-    "                    \"name\": func.get(\"name\"),\n",
-    "                    \"description\": func.get(\"description\"),\n",
-    "                    \"arguments\": json.loads(func.get(\"arguments\").strip('\"')),\n",
-    "                    \"packages\": func.get(\"packages\"),\n",
-    "                    \"code\": func.get(\"code\"),\n",
-    "                }\n",
-    "            ans.append(item)\n",
-    "        return ans\n",
-    "\n",
-    "    def _get_success_rate(self, statistic):\n",
-    "        sum = 0\n",
-    "        for key, value in statistic.items():\n",
-    "            if \"is_correct\" not in value.keys():\n",
-    "                statistic[key][\"is_correct\"] = 0\n",
-    "        for key, value in statistic.items():\n",
-    "            sum += value[\"is_correct\"]\n",
-    "        if len(statistic.keys()) != 0:\n",
-    "            success_rate = sum / len(statistic.keys())\n",
-    "        else:\n",
-    "            success_rate = None\n",
-    "        return success_rate, statistic\n",
-    "\n",
-    "    def _modify_function_signiture(self, cur_functions, action_json):\n",
-    "        for action in action_json:\n",
-    "            action_name = action.get(\"action_name\")\n",
-    "            if action_name != \"remove_function\":\n",
-    "                cur_functions = [item for item in cur_functions if item[\"name\"] != action.get(\"name\")]\n",
-    "                cur_functions.append(\n",
-    "                    {\n",
-    "                        \"name\": action.get(\"name\"),\n",
-    "                        \"description\": action.get(\"description\"),\n",
-    "                        \"arguments\": action.get(\"arguments\"),\n",
-    "                        \"packages\": action.get(\"packages\"),\n",
-    "                        \"code\": action.get(\"code\"),\n",
-    "                    }\n",
-    "                )\n",
-    "            else:\n",
-    "                cur_functions = [item for item in cur_functions if item[\"name\"] != action.get(\"name\")]\n",
-    "        return cur_functions\n",
-    "\n",
-    "    def update_function_call(self, action, mathproxyagent, assistant):\n",
-    "        def execute_func(name, packages, code, **args):\n",
-    "            pip_install = (\n",
-    "                f\"\"\"print(\"Installing package: {packages}\")\\nsubprocess.run([\"pip\", \"-qq\", \"install\", \"{packages}\"])\"\"\"\n",
-    "                if packages\n",
-    "                else \"\"\n",
-    "            )\n",
-    "            str = f\"\"\"\n",
-    "import subprocess\n",
-    "{pip_install}\n",
-    "print(\"Result of {name} function execution:\")\n",
-    "{code}\n",
-    "args={args}\n",
-    "result={name}(**args)\n",
-    "if result is not None: print(result)\n",
-    "\"\"\"\n",
-    "            print(f\"execute_code:\\n{str}\")\n",
-    "            result = execute_code(str)\n",
-    "            if result[0] != 0:\n",
-    "                raise Exception(\"Error in executing function:\" + result[1])\n",
-    "            print(f\"Result: {result[1]}\")\n",
-    "            return result[1]\n",
-    "\n",
-    "        name, description, arguments, packages, code, action_name = (\n",
-    "            action.get(\"name\"),\n",
-    "            action.get(\"description\"),\n",
-    "            action.get(\"arguments\"),\n",
-    "            action.get(\"packages\"),\n",
-    "            action.get(\"code\"),\n",
-    "            action.get(\"action_name\"),\n",
-    "        )\n",
-    "\n",
-    "        if name in mathproxyagent._function_map.keys():\n",
-    "            del mathproxyagent._function_map[name]\n",
-    "        if action_name != \"remove_function\":\n",
-    "            function_config = {\n",
-    "                \"name\": name,\n",
-    "                \"description\": description,\n",
-    "                \"parameters\": {\"type\": \"object\", \"properties\": arguments},\n",
-    "            }\n",
-    "            mathproxyagent.register_function(\n",
-    "                function_map={name: lambda **args: execute_func(name, packages, code, **args)}\n",
-    "            )\n",
-    "            assistant.update_function_signature(function_config, is_remove=False)\n",
-    "        else:\n",
-    "            assistant.update_function_signature(name, is_remove=True)\n",
-    "\n",
-    "    def step(self, history, statistic, func_signiture):\n",
-    "        action_return = []\n",
-    "        origin_signiture = func_signiture\n",
-    "        modified_signiture = origin_signiture\n",
-    "\n",
-    "        success_rate, statistic = self._get_success_rate(statistic)  # TODO: make statistic feasible outside of the loop\n",
-    "        for action_index in range(self._action_num):\n",
-    "            prompt = self.OPT_PROMPT.format(\n",
-    "                conversation_num=len(history),\n",
-    "                statistic={\"is_correct\": statistic},\n",
-    "                signiture=origin_signiture,\n",
-    "                history=history,\n",
-    "                success_rate=success_rate,\n",
-    "                actions_num=action_index,\n",
-    "                after_signiture=modified_signiture,\n",
-    "            )\n",
-    "            messages = [{\"role\": \"user\", \"content\": prompt}]\n",
-    "            for _ in range(self._each_action_max_trials):\n",
-    "                response = self._client.chat.completions.create(\n",
-    "                    model=self.model,\n",
-    "                    messages=messages,\n",
-    "                    tools=[self.ADD_FUNC, self.REVISE_FUNC, self.REMOVE_FUNC],\n",
-    "                    tool_choice=\"auto\",\n",
-    "                )\n",
-    "                actions = response.choices[0].message.tool_calls\n",
-    "                if (\n",
-    "                    self._val_json(actions)\n",
-    "                    and self._val_syntax(actions)\n",
-    "                    and self._val_remove(actions, modified_signiture)\n",
-    "                ):\n",
-    "                    break\n",
-    "            if actions is not None:\n",
-    "                action_result = self._format_actions(actions)\n",
-    "                action_return = action_return + action_result\n",
-    "                modified_signiture = self._modify_function_signiture(modified_signiture, action_result)\n",
-    "        return action_return, modified_signiture"
+    "from autogen.agentchat import Agent\n",
+    "from openai import BadRequestError\n",
+    "from autogen.code_utils import extract_code\n",
+    "from autogen.math_utils import get_answer\n",
+    "from autogen import config_list_from_json\n",
+    "import autogen\n",
+    "import json\n",
+    "import copy"
    ]
   },
   {
@@ -379,18 +50,19 @@
    "source": [
     "# MathUserProxy with function_call\n",
     "\n",
-    "This agent is a customozied MathUserProxy inherits from its [partent class](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/math_user_proxy_agent.py.) \n",
+    "This agent is a customozied MathUserProxy inherits from its [partent class](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/math_user_proxy_agent.py.).\n",
     "\n",
-    "It supports using both function_call and python to solve math problems."
+    "It supports using both function_call and python to solve math problems.\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "def is_termination_msg_mathchat(message):\n",
+    "    \"\"\"Check if a message is a termination message.\"\"\"\n",
     "    if isinstance(message, dict):\n",
     "        message = message.get(\"content\")\n",
     "        if message is None:\n",
@@ -398,12 +70,11 @@
     "    cb = extract_code(message)\n",
     "    contain_code = False\n",
     "    for c in cb:\n",
-    "        if c[0] == \"python\" or c[0] == \"wolfram\":\n",
+    "        if c[0] == \"python\":\n",
     "            contain_code = True\n",
     "            break\n",
     "    if message.rstrip().find(\"TERMINATE\") >= 0:\n",
     "        return True\n",
-    "\n",
     "    return not contain_code and get_answer(message) is not None and get_answer(message) != \"\"\n",
     "\n",
     "\n",
@@ -471,6 +142,7 @@
     "        self.max_function_call_trial = 3\n",
     "        self.query = None\n",
     "        self.answer = None\n",
+    "        self.is_correct = None\n",
     "\n",
     "    def generate_function_call_reply(\n",
     "        self,\n",
@@ -490,7 +162,6 @@
     "            else:\n",
     "                if self.max_function_call_trial == 0:\n",
     "                    error_message = func_return[\"content\"]\n",
-    "                    self.logs[\"is_correct\"] = 0\n",
     "                    self.max_function_call_trial = 3\n",
     "                    return (\n",
     "                        True,\n",
@@ -509,12 +180,11 @@
     "    def initiate_chat(\n",
     "        self,\n",
     "        recipient,\n",
-    "        query: None,\n",
     "        answer: None,\n",
     "        silent: Optional[bool] = False,\n",
     "        **context,\n",
     "    ):\n",
-    "        self.query = query\n",
+    "        self.query = context[\"problem\"]\n",
     "        if not isinstance(answer, str):\n",
     "            answer = str(answer)\n",
     "            if answer.endswith(\".0\"):\n",
@@ -522,29 +192,23 @@
     "            self._answer = answer\n",
     "        else:\n",
     "            self._answer = answer\n",
-    "        self.logs = {}\n",
-    "        self._prepare_chat(recipient, True)\n",
     "\n",
-    "        chat_history = []\n",
-    "        error_message = None\n",
+    "        self.is_correct = None\n",
     "\n",
+    "        self._prepare_chat(recipient, True)\n",
+    "        error_message = None\n",
     "        try:\n",
     "            prompt = self.PROMPTS + context[\"problem\"]\n",
     "            self.send(prompt, recipient, silent=silent)\n",
     "        except BadRequestError as e:\n",
     "            error_message = str(e)\n",
-    "            self.logs[\"is_correct\"] = 0\n",
+    "            self.is_correct = 0\n",
     "            print(\"error information: {}\".format(error_message))\n",
     "\n",
-    "        key = list(self.chat_messages.keys())[0]\n",
-    "        chat_messages = self.chat_messages[key]\n",
-    "        for item in chat_messages:\n",
-    "            chat_history.append(item)\n",
-    "        if error_message is not None:\n",
-    "            chat_history.append(error_message)\n",
     "        recipient.reset()\n",
-    "        self.reset()\n",
-    "        return self.logs, chat_history\n",
+    "        is_correct = copy.deepcopy(self.is_correct)\n",
+    "        self._reset()\n",
+    "        return is_correct\n",
     "\n",
     "    def _check_final_result(\n",
     "        self,\n",
@@ -552,8 +216,8 @@
     "        sender: Optional[autogen.Agent] = None,\n",
     "        config: Optional[Any] = None,\n",
     "    ):\n",
-    "        messages = messages[-1]\n",
     "\n",
+    "        messages = messages[-1]\n",
     "        if isinstance(messages, dict):\n",
     "            messages = messages.get(\"content\")\n",
     "            if messages is None:\n",
@@ -562,29 +226,25 @@
     "        cb = extract_code(messages)\n",
     "        contain_code = False\n",
     "        for c in cb:\n",
-    "            if c[0] == \"python\" or c[0] == \"wolfram\":\n",
+    "            if c[0] == \"python\":\n",
     "                contain_code = True\n",
     "                break\n",
     "        if not contain_code and get_answer(messages) is not None and get_answer(messages) != \"\":\n",
     "            if get_answer(messages) == self._answer:\n",
-    "                self.logs[\"is_correct\"] = 1\n",
+    "                self.is_correct = 1\n",
     "                return True, \"The result is Correct. Please reply me with TERMINATE.\"\n",
     "            else:\n",
-    "                self.logs[\"is_correct\"] = 0\n",
+    "                self.is_correct = 0\n",
     "                return False, None\n",
     "        else:\n",
     "            return False, None\n",
     "\n",
     "    def _reset(self):\n",
-    "        self._valid_q_count = 0\n",
-    "        self._total_q_count = 0\n",
-    "        self._accum_invalid_q_per_step = 0\n",
-    "        self._previous_code = \"\"\n",
-    "        self.last_reply = None\n",
-    "\n",
+    "        super()._reset()\n",
+    "        self.max_function_call_trial = 3\n",
+    "        self.is_correct = None\n",
     "        self.query = None\n",
-    "        self.answer = None\n",
-    "        self.logs = {}"
+    "        self.answer = None"
    ]
   },
   {
@@ -595,12 +255,12 @@
     "\n",
     "MATAH dataset contains 12,500 challenging competition mathematics problems. Each problem in MATH has a full step-by-step solution which can be used to teach models to generate answer derivations and explanations. \n",
     "\n",
-    "We strctly follow the train/test splits of [Craft](https://github.com/lifan-yuan/CRAFT). Please specific your own path to the dataset. Here we sample the first 10 algebra problems as examples. "
+    "We strctly follow the [train](https://github.com/lifan-yuan/CRAFT/blob/main/tab_and_math/MATH/dataset/train/algebra.jsonl)/[test](https://github.com/lifan-yuan/CRAFT/blob/main/tab_and_math/MATH/dataset/algebra.jsonl) splits of [Craft](https://github.com/lifan-yuan/CRAFT). Please specific your own path to the dataset. Here we sample the first 10 algebra problems as examples. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -625,19 +285,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
-    "config_list = autogen.config_list_from_json(\n",
-    "    \"OAI_CONFIG_LIST\",\n",
-    ")\n",
-    "mathproxyagent = MathUserProxyAgent(\n",
-    "    name=\"mathproxyagent\",\n",
-    "    human_input_mode=\"NEVER\",\n",
-    "    code_execution_config={\"work_dir\": \"_output\", \"use_docker\": False},\n",
-    "    is_termination_msg=is_termination_msg_mathchat,\n",
-    ")\n",
+    "config_list = config_list_from_json(env_or_file=\"OAI_CONFIG_LIST\")\n",
+    "\n",
     "assistant = autogen.AssistantAgent(\n",
     "    name=\"assistant\",\n",
     "    system_message=\"You are a helpful assistant.\",\n",
@@ -646,6 +299,11 @@
     "        \"seed\": 42,\n",
     "        \"config_list\": config_list,\n",
     "    },\n",
+    ")\n",
+    "user_proxy = MathUserProxyAgent(\n",
+    "    name=\"mathproxyagent\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    code_execution_config={\"work_dir\": \"_output\", \"use_docker\": False},\n",
     ")"
    ]
   },
@@ -666,32 +324,22 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "history_list, statistic_list = {}, {}\n",
-    "for index, query in enumerate(test_data):\n",
-    "    single_statistic, chat_history = mathproxyagent.initiate_chat(\n",
-    "        recipient=assistant, answer=query[\"answer\"], query=[\"question\"], problem=query[\"question\"]\n",
-    "    )\n",
-    "    history_list[\"conversation: {index}\".format(index=index + 1)] = chat_history\n",
-    "    statistic_list[\"conversation: {index}\".format(index=index + 1)] = single_statistic\n",
-    "\n",
     "sum = 0\n",
-    "for key, value in statistic_list.items():\n",
-    "    if \"is_correct\" not in value.keys():\n",
-    "        statistic_list[key][\"is_correct\"] = 0\n",
-    "for key, value in statistic_list.items():\n",
-    "    sum += value[\"is_correct\"]\n",
-    "\n",
-    "success_rate_without_agent_training = sum / len(statistic_list.keys())"
+    "for index, query in enumerate(test_data):\n",
+    "    is_correct = user_proxy.initiate_chat(recipient=assistant, answer=query[\"answer\"], problem=query[\"question\"])\n",
+    "    print(is_correct)\n",
+    "    sum += is_correct\n",
+    "success_rate_without_agent_training = sum / 10"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Agent optimizing \n",
-    "\n",
-    "Then, use the AgentOptimizer to iteratively optimize the agents by optimizing the function calls according to the historical conversations and performance.    \n",
+    "# Agent Training \n",
     "\n",
+    "Then, we use the AgentOptimizer to iteratively optimize the agents by optimizing the function calls according to the historical conversations and performance.\n",
+    "The AgentOptimizer yields register_for_llm and register_for_executor at each iteration, which are subsequently utilized to update the assistant and user_proxy agents, respectively. \n",
     "Here we optimize these two agents for ten epochs. "
    ]
   },
@@ -702,42 +350,20 @@
    "outputs": [],
    "source": [
     "EPOCH = 10\n",
-    "OAI_config = {\n",
-    "    \"AZURE_OPENAI_API_KEY\": \"gpt-4-1106-preview\",\n",
-    "    \"api_version\": \"2024-02-15-preview\",\n",
-    "    \"azure_endpoint\": \"your_azure_endpoint\",\n",
-    "    \"model\": \"your_model\",\n",
-    "}\n",
-    "agent_optimizer = AgentOptimizer(OAI_config=OAI_config)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "history_list, statistic_list, function_json, agent_list = [], [], [], []\n",
-    "for epoch in range(EPOCH):\n",
-    "    if len(history_list) != 0:\n",
-    "        actions, function_json = agent_optimizer.step(history_list, statistic_list, function_json)\n",
-    "        for action in actions:\n",
-    "            agent_optimizer.update_function_call(action, mathproxyagent=mathproxyagent, assistant=assistant)\n",
-    "    history_list, statistic_list = {}, {}\n",
+    "optimizer_model = \"gpt-4-1106-preview\"\n",
+    "optimizer = AgentOptimizer(\n",
+    "    max_actions_per_step=3, config_file_or_env=\"OAI_CONFIG_LIST\", optimizer_model=optimizer_model\n",
+    ")\n",
+    "for i in range(EPOCH):\n",
     "    for index, query in enumerate(train_data):\n",
-    "        single_statistic, chat_history = mathproxyagent.initiate_chat(\n",
-    "            recipient=assistant, answer=query[\"answer\"], query=[\"question\"], problem=query[\"question\"]\n",
-    "        )\n",
-    "        history_list[\"conversation: {index}\".format(index=index + 1)] = chat_history\n",
-    "        statistic_list[\"conversation: {index}\".format(index=index + 1)] = single_statistic\n",
-    "\n",
-    "    sum = 0\n",
-    "    for key, value in statistic_list.items():\n",
-    "        if \"is_correct\" not in value.keys():\n",
-    "            statistic_list[key][\"is_correct\"] = 0\n",
-    "    for key, value in statistic_list.items():\n",
-    "        sum += value[\"is_correct\"]\n",
-    "    print(\"Train_Epoch_{epoch_num}_Success_Rate: {average}%\".format(epoch_num=epoch, average=sum / len(statistic_list)))"
+    "        is_correct = user_proxy.initiate_chat(assistant, answer=query[\"answer\"], problem=query[\"question\"])\n",
+    "        history = assistant.chat_messages_for_summary(user_proxy)\n",
+    "        optimizer.record_one_conversation(history, is_satisfied=is_correct)\n",
+    "    register_for_llm, register_for_exector = optimizer.step()\n",
+    "    for item in register_for_llm:\n",
+    "        assistant.update_function_signature(**item)\n",
+    "    if len(register_for_exector.keys()) > 0:\n",
+    "        user_proxy.register_function(function_map=register_for_exector)"
    ]
   },
   {
@@ -758,27 +384,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "history_list, statistic_list = {}, {}\n",
-    "for index, query in enumerate(test_data):\n",
-    "    single_statistic, chat_history = mathproxyagent.initiate_chat(\n",
-    "        recipient=assistant, answer=query[\"answer\"], query=[\"question\"], problem=query[\"question\"]\n",
-    "    )\n",
-    "    history_list[\"conversation: {index}\".format(index=index + 1)] = chat_history\n",
-    "    statistic_list[\"conversation: {index}\".format(index=index + 1)] = single_statistic\n",
-    "\n",
     "sum = 0\n",
-    "for key, value in statistic_list.items():\n",
-    "    if \"is_correct\" not in value.keys():\n",
-    "        statistic_list[key][\"is_correct\"] = 0\n",
-    "for key, value in statistic_list.items():\n",
-    "    sum += value[\"is_correct\"]\n",
-    "\n",
-    "success_rate_with_agent_training = sum / len(statistic_list.keys())"
+    "for index, query in enumerate(test_data):\n",
+    "    is_correct = user_proxy.initiate_chat(recipient=assistant, answer=query[\"answer\"], problem=query[\"question\"])\n",
+    "    sum += is_correct\n",
+    "success_rate_with_agent_training = sum / 10"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -786,9 +401,12 @@
      "output_type": "stream",
      "text": [
       "------------------------------------------------Functions learned------------------------------------------------\n",
-      "{'name': 'evaluate_expression', 'description': 'Evaluate arithmetic or mathematical expressions provided as strings.', 'arguments': {'expression': {'type': 'string', 'description': 'The mathematical expression to evaluate.'}}, 'packages': 'sympy', 'code': 'from sympy import sympify, SympifyError\\n\\ndef evaluate_expression(expression):\\n    try:\\n        result = sympify(expression)\\n        if result.is_number:\\n            result = float(result)\\n        else:\\n            result = str(result)\\n        return result\\n    except SympifyError as e:\\n        return str(e)'}\n",
-      "{'name': 'calculate_compound_interest_principal', 'description': 'Calculate the principal amount needed to achieve a certain future value with quarterly compound interest.', 'arguments': {'future_value': {'type': 'number', 'description': 'The total amount of money desired in the future.'}, 'annual_interest_rate': {'type': 'number', 'description': 'The annual interest rate in decimal form.'}, 'compounding_periods': {'type': 'integer', 'description': 'The number of times interest is compounded per year.'}, 'years': {'type': 'number', 'description': 'The time in years the money is invested for.'}}, 'packages': 'sympy', 'code': \"from sympy import symbols, solve, N\\n\\nfuture_value, annual_interest_rate, compounding_periods, years = symbols('future_value annual_interest_rate compounding_periods years')\\nP = symbols('P')\\nequation = Eq(future_value, P * (1 + annual_interest_rate / compounding_periods) ** (compounding_periods * years))\\nprincipal = solve(equation, P)[0]\\nprincipal = N(principal, chop=True).evalf()\"}\n",
-      "{'name': 'solve_linear_system', 'description': 'Solve a system of linear equations represented as coefficients and variables.', 'arguments': {'equations': {'type': 'array', 'items': {'type': 'array', 'items': {'type': 'number'}}}, 'variables': {'type': 'array', 'items': {'type': 'string'}}}, 'packages': 'sympy', 'code': 'from sympy import Matrix, symbols, linsolve\\n\\ndef solve_linear_system(equations, variables):\\n    if not equations or not all(len(eq) == len(variables) + 1 for eq in equations):\\n        return \"Error: Equations list is empty or not all equations have the correct length.\"\\n    if len(variables) != len(set(variables)):\\n        return \"Error: Duplicate symbols in the \\'variables\\' list.\"\\n    try:\\n        sym_vars = symbols(\\' \\'.join(variables))\\n        matrix = Matrix(equations)\\n        system = (matrix, sym_vars)\\n        solution = linsolve(system)\\n        solution_list = list(solution)\\n        if not solution_list:\\n            return \"Error: No solution exists for the given system of equations.\"\\n        result = solution_list[0] if solution_list else []\\n        return dict(zip(variables, result))\\n    except Exception as e:\\n        return f\"Error: {str(e)}\"'}\n",
+      "evaluate_expression: Evaluate arithmetic or mathematical expressions provided as strings.\n",
+      "\n",
+      "calculate_compound_interest_principal: Calculate the principal amount needed to achieve a certain future value with quarterly compound interest.\n",
+      "\n",
+      "solve_linear_system: Solve a system of linear equations represented as coefficients and variables.\n",
+      "\n",
       "------------------------------------------------Summary------------------------------------------------\n",
       "\n",
       "success_rate_without_agent_training: 60.0%\n",
@@ -802,8 +420,8 @@
     "print(\n",
     "    \"------------------------------------------------Functions learned------------------------------------------------\"\n",
     ")\n",
-    "for function in function_json:\n",
-    "    print(function)\n",
+    "for func in assistant.llm_config[\"functions\"]:\n",
+    "    print(func[\"name\"] + \": \" + func[\"description\"] + \"\\n\")\n",
     "print(\"------------------------------------------------Summary------------------------------------------------\\n\")\n",
     "print(\"success_rate_without_agent_training: {average}%\\n\".format(average=success_rate_without_agent_training * 100))\n",
     "print(\"success_rate_with_agent_training: {average}%\\n\".format(average=success_rate_with_agent_training * 100))"
@@ -826,7 +444,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.17"
+   "version": "3.9.18"
   }
  },
  "nbformat": 4,
diff --git a/test/agentchat/contrib/test_agent_optimizer.py b/test/agentchat/contrib/test_agent_optimizer.py
new file mode 100644
index 00000000000..fe0ba2dc289
--- /dev/null
+++ b/test/agentchat/contrib/test_agent_optimizer.py
@@ -0,0 +1,110 @@
+import pytest
+import os
+from conftest import skip_openai as skip
+
+import autogen
+from test_assistant_agent import OAI_CONFIG_LIST, KEY_LOC
+from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
+
+from autogen.agentchat.contrib.agent_optimizer import AgentOptimizer
+
+here = os.path.abspath(os.path.dirname(__file__))
+
+
+@pytest.mark.skipif(
+    skip,
+    reason="requested to skip",
+)
+def test_record_conversation():
+    problem = "Simplify $\\sqrt[3]{1+8} \\cdot \\sqrt[3]{1+\\sqrt[3]{8}}"
+
+    config_list = autogen.config_list_from_json(
+        OAI_CONFIG_LIST,
+        file_location=KEY_LOC,
+    )
+    assistant = AssistantAgent(
+        "assistant",
+        system_message="You are a helpful assistant.",
+        llm_config={
+            "timeout": 60,
+            "cache_seed": 42,
+            "config_list": config_list,
+        },
+    )
+    user_proxy = UserProxyAgent(
+        name="user_proxy",
+        human_input_mode="NEVER",
+        code_execution_config={
+            "work_dir": f"{here}/test_agent_scripts",
+            "use_docker": "python:3",
+            "timeout": 60,
+        },
+        max_consecutive_auto_reply=3,
+    )
+
+    user_proxy.initiate_chat(assistant, message=problem)
+    optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env=OAI_CONFIG_LIST)
+    optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
+
+    assert len(optimizer._trial_conversations_history) == 1
+    assert len(optimizer._trial_conversations_performance) == 1
+    assert optimizer._trial_conversations_performance[0]["Conversation 0"] == 1
+
+    optimizer.reset_optimizer()
+    assert len(optimizer._trial_conversations_history) == 0
+    assert len(optimizer._trial_conversations_performance) == 0
+
+
+@pytest.mark.skipif(
+    skip,
+    reason="requested to skip",
+)
+def test_step():
+    problem = "Simplify $\\sqrt[3]{1+8} \\cdot \\sqrt[3]{1+\\sqrt[3]{8}}"
+
+    config_list = autogen.config_list_from_json(
+        OAI_CONFIG_LIST,
+        file_location=KEY_LOC,
+    )
+    assistant = AssistantAgent(
+        "assistant",
+        system_message="You are a helpful assistant.",
+        llm_config={
+            "timeout": 60,
+            "cache_seed": 42,
+            "config_list": config_list,
+        },
+    )
+    user_proxy = UserProxyAgent(
+        name="user_proxy",
+        human_input_mode="NEVER",
+        code_execution_config={
+            "work_dir": f"{here}/test_agent_scripts",
+            "use_docker": "python:3",
+            "timeout": 60,
+        },
+        max_consecutive_auto_reply=3,
+    )
+
+    optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env=OAI_CONFIG_LIST)
+    user_proxy.initiate_chat(assistant, message=problem)
+    optimizer.record_one_conversation(assistant.chat_messages_for_summary(user_proxy), is_satisfied=True)
+
+    register_for_llm, register_for_exector = optimizer.step()
+
+    print("-------------------------------------")
+    print("register_for_llm:")
+    print(register_for_llm)
+    print("register_for_exector")
+    print(register_for_exector)
+
+    for item in register_for_llm:
+        assistant.update_function_signature(**item)
+    if len(register_for_exector.keys()) > 0:
+        user_proxy.register_function(function_map=register_for_exector)
+
+    print("-------------------------------------")
+    print("Updated assistant.llm_config:")
+    print(assistant.llm_config)
+    print("Updated user_proxy._function_map:")
+    print(user_proxy._function_map)
diff --git a/website/blog/2023-12-23-AgentOptimizer/index.mdx b/website/blog/2023-12-23-AgentOptimizer/index.mdx
index b59046d55c4..23e348fa30c 100644
--- a/website/blog/2023-12-23-AgentOptimizer/index.mdx
+++ b/website/blog/2023-12-23-AgentOptimizer/index.mdx
@@ -11,57 +11,83 @@ tags: [LLM, research]
 
 **TL;DR:**
 Introducing **AgentOptimizer**, a new class for training LLM agents in the era of LLMs as a service.
-**AgentOptimizer** is able to prompt autogen agents to iteratively optimize its function/skills according to the historical conversation and performance.
+**AgentOptimizer** is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.
 Checkout one implementation for **AgentOptimizer** on [MATH](https://github.com/hendrycks/math) dataset
 [here](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb).
 
-Paper is coming soon!
+More information could be found in the [paper](https://arxiv.org/abs/2402.11359).
 
 ## Introduction
-In traditional ML pipeline, we train a model by updating its weights according to the loss on the training set, while in the era of LLM agents, how should we train an agent?
-Here, we take an initial step towards the agent training.
-Inspired by the [function calling](https://platform.openai.com/docs/guides/function-calling) capabilities provided by OpenAI,
+In the traditional ML pipeline, we train a model by updating its weights according to the loss on the training set, while in the era of LLM agents, how should we train an agent?
+Here, we take an initial step towards the agent training. Inspired by the [function calling](https://platform.openai.com/docs/guides/function-calling) capabilities provided by OpenAI,
 we draw an analogy between model weights and agent functions/skills, and update an agent’s functions/skills based on its historical performance on a training set.
-Specifically, we propose to use the function calling capabilities to formulate the actions that optimize the agents’ functions as a set of function calls,
-to support iteratively **adding, revising, and removing** existing functions.
-As an agentic way of training an agent, our approach helps enhance the agents’ abilities without requiring access to the LLMs weights.
+Specifically, we propose to use the function calling capabilities to formulate the actions that optimize the agents’ functions as a set of function calls, to support iteratively **adding, revising, and removing** existing functions.
+We also include two strategies, roll-back, and early-stop, to streamline the training process to overcome the performance-decreasing problem when training.
+As an agentic way of training an agent, our approach helps enhance the agents’ abilities without requiring access to the LLM's weights.
 
 ## AgentOptimizer
 
 **AgentOptimizer** is a class designed to optimize the agents by improving their function calls.
-It contains two core methods:
+It contains three main methods:
 
-1. `step()`: `step()` takes three inputs, including the previous conversation history (history), the statistical information of solving previous problems (statistic), and the current functions (current_functions).
+1. `record_one_conversation`:
+
+This method records the conversation history and performance of the agents in solving one problem.
+It includes two inputs: conversation_history (List[Dict]) and is_satisfied (bool).
+conversation_history is a list of dictionaries which could be got from chat_messages_for_summary in the [AgentChat](https://microsoft.github.io/autogen/docs/reference/agentchat/agentchat/) class.
+is_satisfied is a bool value that represents whether the user is satisfied with the solution. If it is none, the user will be asked to input the satisfaction.
+
+Example:
+
+```python
+optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env="OAI_CONFIG_LIST")
+# ------------ code to solve a problem ------------
+# ......
+# -------------------------------------------------
+history = assistant.chat_messages_for_summary(UserProxy)
+optimizer.record_one_conversation(history, is_satisfied=result)
+```
+
+
+2. `step()`:
+
+`step()` is the core method of AgentOptimizer.
+At each optimization iteration, it will return two fields register_for_llm and register_for_executor, which are subsequently utilized to update the assistant and UserProxy agents, respectively.
 
 ```python
-actions, updated_functions = AgentOptimizer.step(history, statistic, current_functions)
+register_for_llm, register_for_exector = optimizer.step()
+for item in register_for_llm:
+    assistant.update_function_signature(**item)
+if len(register_for_exector.keys()) > 0:
+    user_proxy.register_function(function_map=register_for_exector)
 ```
 
-It has two outputs `actions` and `updated_functions`. `actions` is a series of actions to manipulate the current functions. And `updated_functions` is the updated functions after the actions are applied (including code implementation).
+3. `reset_optimizer`:
 
-2. `update_function_call()`:
-This method takes the agents and actions as input. It updates the functions registered in these agents according to the actions from `step()`.
-For AssistantAgent, it first uses [update_function_signature](https://microsoft.github.io/autogen/docs/reference/agentchat/conversable_agent/#update_function_signature) to update the function signatures.
-Then, it updates the functions in the MathUserproxyAgent with the corresponding code implementation gained from `step()`.
+This method will reset the optimizer to the initial state, which is useful when you want to train the agent from scratch.
 
-Sometimes, the function signatures (JSON schema) returned by the `step()` may not be valid, and the generated code may also face syntax errors.
-**AgentOptimizer** includes mechanisms to check the (1) validity of the function signatures and (2) code implementation before updating the functions.
-Moreover, it also includes mechanisms to check whether each action is feasible, such as avoiding the removal of a function that is not in the current functions due to hallucination.
+**AgentOptimizer** includes mechanisms to check the (1) validity of the function and (2) code implementation before returning the register_for_llm, register_for_exector.
+Moreover, it also includes mechanisms to check whether each update is feasible, such as avoiding the removal of a function that is not in the current functions due to hallucination.
 
 ## Pseudocode for the optimization process
 
 The optimization process is as follows:
 
 ```python
-for - in range(EPOCH):
-    history, statistic, current_functions = solve_problems(train_problems)
-    actions, updated_functions = AgentOptimizer.step(history, statistic, current_functions)
-    AgentOptimizer.update_function_call(actions)
+optimizer = AgentOptimizer(max_actions_per_step=3, config_file_or_env="OAI_CONFIG_LIST")
+for i in range(EPOCH):
+    is_correct = user_proxy.initiate_chat(assistant, message = problem)
+    history = assistant.chat_messages_for_summary(user_proxy)
+    optimizer.record_one_conversation(history, is_satisfied=is_correct)
+    register_for_llm, register_for_exector = optimizer.step()
+    for item in register_for_llm:
+        assistant.update_function_signature(**item)
+    if len(register_for_exector.keys()) > 0:
+        user_proxy.register_function(function_map=register_for_exector)
 ```
 
 Given a prepared training dataset, the agents iteratively solve problems from the training set to obtain conversation history and statistical information.
-The functions are then improved using AgentOptimizer.
-Each iteration can be regarded as one training step analogous to traditional machine learning, with the optimization elements being the functions that agents have.
+The functions are then improved using AgentOptimizer. Each iteration can be regarded as one training step analogous to traditional machine learning, with the optimization elements being the functions that agents have.
 After EPOCH iterations, the agents are expected to obtain better functions that may be used in future tasks
 
 
@@ -71,7 +97,7 @@ To obtain stable and structured function signatures and code implementations fro
 we leverage the function calling capabilities provided by OpenAI to formulate the actions that manipulate the functions as a set of function calls.
 Specifically, we introduce three function calls to manipulate the current functions at each step: `add_function`, `remove_function`, and `revise_function`.
 These calls add, remove, and revise functions in the existing function list, respectively.
-This practice could fully leverages the function calling capabilities of GPT-4 and outputs structured functions with more stable signatures and code implementation.
+This practice could fully leverage the function calling capabilities of GPT-4 and output structured functions with more stable signatures and code implementation.
 Below is the JSON schema of these function calls:
 
 1. `add_function`: Add one new function that may be used in the future tasks.
@@ -84,30 +110,24 @@ ADD_FUNC = {
         "parameters": {
             "type": "object",
             "properties": {
-                "name": {
-                    "type": "string",
-                    "description": "The name of the function in the code implementation."
-                },
-                "description": {
-                    "type": "string",
-                    "description": "A short description of the function."
-                },
+                "name": {"type": "string", "description": "The name of the function in the code implementation."},
+                "description": {"type": "string", "description": "A short description of the function."},
                 "arguments": {
                     "type": "string",
-                    "description": "JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { \"url\": { \"type\": \"string\", \"description\": \"The URL\", }}. Please avoid the error 'array schema missing items' when using array type."
+                    "description": 'JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { "url": { "type": "string", "description": "The URL", }}. Please avoid the error \'array schema missing items\' when using array type.',
                 },
                 "packages": {
                     "type": "string",
-                    "description": "A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list."
+                    "description": "A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list.",
                 },
                 "code": {
                     "type": "string",
-                    "description": "The implementation in Python. Do not include the function declaration."
-                }
+                    "description": "The implementation in Python. Do not include the function declaration.",
+                },
             },
-            "required": ["name", "description", "arguments", "packages", "code"]
-        }
-    }
+            "required": ["name", "description", "arguments", "packages", "code"],
+        },
+    },
 }
 ```
 
@@ -121,30 +141,24 @@ REVISE_FUNC = {
         "parameters": {
             "type": "object",
             "properties": {
-                "name": {
-                    "type": "string",
-                    "description": "The name of the function in the code implementation."
-                },
-                "description": {
-                    "type": "string",
-                    "description": "A short description of the function."
-                },
+                "name": {"type": "string", "description": "The name of the function in the code implementation."},
+                "description": {"type": "string", "description": "A short description of the function."},
                 "arguments": {
                     "type": "string",
-                    "description": "JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { \"url\": { \"type\": \"string\", \"description\": \"The URL\", }}. Please avoid the error 'array schema missing items' when using array type."
+                    "description": 'JSON schema of arguments encoded as a string. Please note that the JSON schema only supports specific types including string, integer, object, array, boolean. (do not have float type) For example: { "url": { "type": "string", "description": "The URL", }}. Please avoid the error \'array schema missing items\' when using array type.',
                 },
                 "packages": {
                     "type": "string",
-                    "description": "A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list."
+                    "description": "A list of package names imported by the function, and that need to be installed with pip prior to invoking the function. This solves ModuleNotFoundError. It should be string, not list.",
                 },
                 "code": {
                     "type": "string",
-                    "description": "The implementation in Python. Do not include the function declaration."
-                }
+                    "description": "The implementation in Python. Do not include the function declaration.",
+                },
             },
-            "required": ["name", "description", "arguments", "packages", "code"]
-        }
-    }
+            "required": ["name", "description", "arguments", "packages", "code"],
+        },
+    },
 }
 ```
 
@@ -158,22 +172,16 @@ REMOVE_FUNC = {
         "parameters": {
             "type": "object",
             "properties": {
-                "name": {
-                    "type": "string",
-                    "description": "The name of the function in the code implementation."
-                }
+                "name": {"type": "string", "description": "The name of the function in the code implementation."}
             },
-            "required": ["name"]
-        }
-    }
+            "required": ["name"],
+        },
+    },
 }
 ```
 
 
 ## Limitation & Future work
 
-1. Unlike gradient descent in traditional machine learning training processes, each optimization step does not necessarily lead to better performance on the training set.
-When the training epoch is small, the agent’s performance may even decrease. One urgent task is to design a better mechanism to guide the optimization process.
-2. The [Current implementation](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb) of AgentOptimizer is mainly for illustration purpose and is just a proof of concept.
-It is not formally integrated into the autogen with a general interface like optimizing any kinds of agents in any tasks.
-Currently, it only supports optimizing the multi-agent system in solving problems from [MATH](https://github.com/hendrycks/math) dataset. We will integrate it into autogen with more general interface in the future.
+1. Currently, it only supports optimizing the one typical user_proxy and assistant agents pair. We will make this feature more general to support other agent types in future work.
+2. The current implementation of AgentOptimizer is effective solely on the OpenAI GPT-4 model. Extending this feature/concept to other LLMs is the next step.

From bdaa0a879cce7681622548f6f62a9ff24998e1aa Mon Sep 17 00:00:00 2001
From: Davor Runje <davor@airt.ai>
Date: Tue, 26 Mar 2024 23:39:55 +0100
Subject: [PATCH 11/27] Introducing IOStream protocol and adding support for
 websockets (#1551)

* Introducing IOStream

* bug fixing

* polishing

* refactoring

* refactoring

* refactoring

* wip: async tests

* websockets added

* wip

* merge with main

* notebook added

* FastAPI example added

* wip

* merge

* getter/setter to iostream added

* website/blog/2024-03-03-AutoGen-Update/img/dalle_gpt4v.png: convert to Git LFS

* website/blog/2024-03-03-AutoGen-Update/img/gaia.png: convert to Git LFS

* website/blog/2024-03-03-AutoGen-Update/img/teach.png: convert to Git LFS

* add SSL support

* wip

* wip

* exception handling added to on_connect()

* refactoring: default iostream is being set in a context manager

* test fix

* polishing

* polishing

* polishing

* fixed bug with new thread

* polishing

* a bit of refactoring and docs added

* notebook added to docs

* type checking added to CI

* CI fix

* CI fix

* CI fix

* polishing

* obsolete todo comment removed

* fixed precommit error

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
---
 .github/workflows/build.yml            |   2 +-
 autogen/agentchat/chat.py              |  13 +-
 autogen/agentchat/conversable_agent.py |  90 ++--
 autogen/agentchat/groupchat.py         |  30 +-
 autogen/cache/cache.py                 |   6 +-
 autogen/io/__init__.py                 |   8 +
 autogen/io/base.py                     |  73 ++++
 autogen/io/console.py                  |  37 ++
 autogen/io/websockets.py               | 207 +++++++++
 autogen/oai/client.py                  |  30 +-
 notebook/agentchat_websockets.ipynb    | 572 +++++++++++++++++++++++++
 pyproject.toml                         |   2 +
 setup.py                               |   3 +-
 test/agentchat/test_agent_logging.py   |   3 +-
 test/io/test_base.py                   |  28 ++
 test/io/test_console.py                |  38 ++
 test/io/test_websockets.py             | 176 ++++++++
 17 files changed, 1252 insertions(+), 66 deletions(-)
 create mode 100644 autogen/io/__init__.py
 create mode 100644 autogen/io/base.py
 create mode 100644 autogen/io/console.py
 create mode 100644 autogen/io/websockets.py
 create mode 100644 notebook/agentchat_websockets.ipynb
 create mode 100644 test/io/test_base.py
 create mode 100644 test/io/test_console.py
 create mode 100644 test/io/test_websockets.py

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 26bb01046e8..7d5d6e882b0 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -65,7 +65,7 @@ jobs:
       - name: Coverage
         if: matrix.python-version == '3.10'
         run: |
-          pip install -e .[test,redis]
+          pip install -e .[test,redis,websockets]
           coverage run -a -m pytest test --ignore=test/agentchat/contrib --skip-openai --durations=10 --durations-min=1.0
           coverage xml
       - name: Upload coverage to Codecov
diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index 0eac4bdb7c3..6c39160444c 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -7,6 +7,7 @@
 from .utils import consolidate_chat_info
 import datetime
 import warnings
+from ..io.base import IOStream
 from ..formatting_utils import colored
 
 
@@ -105,6 +106,8 @@ def __find_async_chat_order(chat_ids: Set[int], prerequisites: List[Prerequisite
 
 
 def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
+    iostream = IOStream.get_default()
+
     if "message" not in chat_info:
         warnings.warn(
             "message is not provided in a chat_queue entry. input() will be called to get the initial message.",
@@ -124,8 +127,8 @@ def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
         print_message = "Dict: " + str(message)
     elif message is None:
         print_message = "None"
-    print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
-    print(
+    iostream.print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
+    iostream.print(
         colored(
             "Starting a new chat....",
             "blue",
@@ -133,9 +136,9 @@ def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
         flush=True,
     )
     if chat_info.get("verbose", False):
-        print(colored("Message:\n" + print_message, "blue"), flush=True)
-        print(colored("Carryover:\n" + print_carryover, "blue"), flush=True)
-    print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
+        iostream.print(colored("Message:\n" + print_message, "blue"), flush=True)
+        iostream.print(colored("Carryover:\n" + print_carryover, "blue"), flush=True)
+    iostream.print(colored("\n" + "*" * 80, "blue"), flush=True, sep="")
 
 
 def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index 3c8f9e79d89..eb9d1c9cbcb 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -32,6 +32,7 @@
 from ..oai.client import ModelClient, OpenAIWrapper
 from ..runtime_logging import log_new_agent, logging_enabled
 from .agent import Agent, LLMAgent
+from ..io.base import IOStream
 from .chat import ChatResult, a_initiate_chats, initiate_chats
 from .utils import consolidate_chat_info, gather_usage_summary
 
@@ -681,8 +682,9 @@ async def a_send(
             )
 
     def _print_received_message(self, message: Union[Dict, str], sender: Agent):
+        iostream = IOStream.get_default()
         # print the message received
-        print(colored(sender.name, "yellow"), "(to", f"{self.name}):\n", flush=True)
+        iostream.print(colored(sender.name, "yellow"), "(to", f"{self.name}):\n", flush=True)
         message = self._message_to_dict(message)
 
         if message.get("tool_responses"):  # Handle tool multi-call responses
@@ -698,9 +700,9 @@ def _print_received_message(self, message: Union[Dict, str], sender: Agent):
                 id_key = "tool_call_id"
             id = message.get(id_key, "No id found")
             func_print = f"***** Response from calling {message['role']} ({id}) *****"
-            print(colored(func_print, "green"), flush=True)
-            print(message["content"], flush=True)
-            print(colored("*" * len(func_print), "green"), flush=True)
+            iostream.print(colored(func_print, "green"), flush=True)
+            iostream.print(message["content"], flush=True)
+            iostream.print(colored("*" * len(func_print), "green"), flush=True)
         else:
             content = message.get("content")
             if content is not None:
@@ -710,35 +712,35 @@ def _print_received_message(self, message: Union[Dict, str], sender: Agent):
                         message["context"],
                         self.llm_config and self.llm_config.get("allow_format_str_template", False),
                     )
-                print(content_str(content), flush=True)
+                iostream.print(content_str(content), flush=True)
             if "function_call" in message and message["function_call"]:
                 function_call = dict(message["function_call"])
                 func_print = (
                     f"***** Suggested function call: {function_call.get('name', '(No function name found)')} *****"
                 )
-                print(colored(func_print, "green"), flush=True)
-                print(
+                iostream.print(colored(func_print, "green"), flush=True)
+                iostream.print(
                     "Arguments: \n",
                     function_call.get("arguments", "(No arguments found)"),
                     flush=True,
                     sep="",
                 )
-                print(colored("*" * len(func_print), "green"), flush=True)
+                iostream.print(colored("*" * len(func_print), "green"), flush=True)
             if "tool_calls" in message and message["tool_calls"]:
                 for tool_call in message["tool_calls"]:
                     id = tool_call.get("id", "No tool call id found")
                     function_call = dict(tool_call.get("function", {}))
                     func_print = f"***** Suggested tool call ({id}): {function_call.get('name', '(No function name found)')} *****"
-                    print(colored(func_print, "green"), flush=True)
-                    print(
+                    iostream.print(colored(func_print, "green"), flush=True)
+                    iostream.print(
                         "Arguments: \n",
                         function_call.get("arguments", "(No arguments found)"),
                         flush=True,
                         sep="",
                     )
-                    print(colored("*" * len(func_print), "green"), flush=True)
+                    iostream.print(colored("*" * len(func_print), "green"), flush=True)
 
-        print("\n", "-" * 80, flush=True, sep="")
+        iostream.print("\n", "-" * 80, flush=True, sep="")
 
     def _process_received_message(self, message: Union[Dict, str], sender: Agent, silent: bool):
         # When the agent receives a message, the role of the message is "user". (If 'role' exists and is 'function', it will remain unchanged.)
@@ -1229,6 +1231,7 @@ def clear_history(self, recipient: Optional[Agent] = None, nr_messages_to_preser
             recipient: the agent with whom the chat history to clear. If None, clear the chat history with all agents.
             nr_messages_to_preserve: the number of newest messages to preserve in the chat history.
         """
+        iostream = IOStream.get_default()
         if recipient is None:
             if nr_messages_to_preserve:
                 for key in self._oai_messages:
@@ -1238,7 +1241,7 @@ def clear_history(self, recipient: Optional[Agent] = None, nr_messages_to_preser
                     first_msg_to_save = self._oai_messages[key][-nr_messages_to_preserve_internal]
                     if "tool_responses" in first_msg_to_save:
                         nr_messages_to_preserve_internal += 1
-                        print(
+                        iostream.print(
                             f"Preserving one more message for {self.name} to not divide history between tool call and "
                             f"tool response."
                         )
@@ -1249,7 +1252,7 @@ def clear_history(self, recipient: Optional[Agent] = None, nr_messages_to_preser
         else:
             self._oai_messages[recipient].clear()
             if nr_messages_to_preserve:
-                print(
+                iostream.print(
                     colored(
                         "WARNING: `nr_preserved_messages` is ignored when clearing chat history with a specific agent.",
                         "yellow",
@@ -1323,8 +1326,19 @@ async def a_generate_oai_reply(
         config: Optional[Any] = None,
     ) -> Tuple[bool, Union[str, Dict, None]]:
         """Generate a reply using autogen.oai asynchronously."""
+        iostream = IOStream.get_default()
+
+        def _generate_oai_reply(
+            self, iostream: IOStream, *args: Any, **kwargs: Any
+        ) -> Tuple[bool, Union[str, Dict, None]]:
+            with IOStream.set_default(iostream):
+                return self.generate_oai_reply(*args, **kwargs)
+
         return await asyncio.get_event_loop().run_in_executor(
-            None, functools.partial(self.generate_oai_reply, messages=messages, sender=sender, config=config)
+            None,
+            functools.partial(
+                _generate_oai_reply, self=self, iostream=iostream, messages=messages, sender=sender, config=config
+            ),
         )
 
     def _generate_code_execution_reply_using_executor(
@@ -1334,6 +1348,8 @@ def _generate_code_execution_reply_using_executor(
         config: Optional[Union[Dict, Literal[False]]] = None,
     ):
         """Generate a reply using code executor."""
+        iostream = IOStream.get_default()
+
         if config is not None:
             raise ValueError("config is not supported for _generate_code_execution_reply_using_executor.")
         if self._code_execution_config is False:
@@ -1371,7 +1387,7 @@ def _generate_code_execution_reply_using_executor(
 
             num_code_blocks = len(code_blocks)
             if num_code_blocks == 1:
-                print(
+                iostream.print(
                     colored(
                         f"\n>>>>>>>> EXECUTING CODE BLOCK (inferred language is {code_blocks[0].language})...",
                         "red",
@@ -1379,7 +1395,7 @@ def _generate_code_execution_reply_using_executor(
                     flush=True,
                 )
             else:
-                print(
+                iostream.print(
                     colored(
                         f"\n>>>>>>>> EXECUTING {num_code_blocks} CODE BLOCKS (inferred languages are [{', '.join([x.language for x in code_blocks])}])...",
                         "red",
@@ -1631,6 +1647,8 @@ def check_termination_and_human_reply(
             - Tuple[bool, Union[str, Dict, None]]: A tuple containing a boolean indicating if the conversation
             should be terminated, and a human reply which can be a string, a dictionary, or None.
         """
+        iostream = IOStream.get_default()
+
         if config is None:
             config = self
         if messages is None:
@@ -1675,7 +1693,7 @@ def check_termination_and_human_reply(
 
         # print the no_human_input_msg
         if no_human_input_msg:
-            print(colored(f"\n>>>>>>>> {no_human_input_msg}", "red"), flush=True)
+            iostream.print(colored(f"\n>>>>>>>> {no_human_input_msg}", "red"), flush=True)
 
         # stop the conversation
         if reply == "exit":
@@ -1715,7 +1733,7 @@ def check_termination_and_human_reply(
         # increment the consecutive_auto_reply_counter
         self._consecutive_auto_reply_counter[sender] += 1
         if self.human_input_mode != "NEVER":
-            print(colored("\n>>>>>>>> USING AUTO REPLY...", "red"), flush=True)
+            iostream.print(colored("\n>>>>>>>> USING AUTO REPLY...", "red"), flush=True)
 
         return False, None
 
@@ -1742,6 +1760,8 @@ async def a_check_termination_and_human_reply(
             - Tuple[bool, Union[str, Dict, None]]: A tuple containing a boolean indicating if the conversation
             should be terminated, and a human reply which can be a string, a dictionary, or None.
         """
+        iostream = IOStream.get_default()
+
         if config is None:
             config = self
         if messages is None:
@@ -1786,7 +1806,7 @@ async def a_check_termination_and_human_reply(
 
         # print the no_human_input_msg
         if no_human_input_msg:
-            print(colored(f"\n>>>>>>>> {no_human_input_msg}", "red"), flush=True)
+            iostream.print(colored(f"\n>>>>>>>> {no_human_input_msg}", "red"), flush=True)
 
         # stop the conversation
         if reply == "exit":
@@ -1826,7 +1846,7 @@ async def a_check_termination_and_human_reply(
         # increment the consecutive_auto_reply_counter
         self._consecutive_auto_reply_counter[sender] += 1
         if self.human_input_mode != "NEVER":
-            print(colored("\n>>>>>>>> USING AUTO REPLY...", "red"), flush=True)
+            iostream.print(colored("\n>>>>>>>> USING AUTO REPLY...", "red"), flush=True)
 
         return False, None
 
@@ -2001,7 +2021,9 @@ def get_human_input(self, prompt: str) -> str:
         Returns:
             str: human input.
         """
-        reply = input(prompt)
+        iostream = IOStream.get_default()
+
+        reply = iostream.input(prompt)
         self._human_input.append(reply)
         return reply
 
@@ -2016,8 +2038,8 @@ async def a_get_human_input(self, prompt: str) -> str:
         Returns:
             str: human input.
         """
-        reply = input(prompt)
-        self._human_input.append(reply)
+        loop = asyncio.get_running_loop()
+        reply = await loop.run_in_executor(None, functools.partial(self.get_human_input, prompt))
         return reply
 
     def run_code(self, code, **kwargs):
@@ -2038,12 +2060,14 @@ def run_code(self, code, **kwargs):
 
     def execute_code_blocks(self, code_blocks):
         """Execute the code blocks and return the result."""
+        iostream = IOStream.get_default()
+
         logs_all = ""
         for i, code_block in enumerate(code_blocks):
             lang, code = code_block
             if not lang:
                 lang = infer_lang(code)
-            print(
+            iostream.print(
                 colored(
                     f"\n>>>>>>>> EXECUTING CODE BLOCK {i} (inferred language is {lang})...",
                     "red",
@@ -2124,6 +2148,8 @@ def execute_function(self, func_call, verbose: bool = False) -> Tuple[bool, Dict
         "function_call" deprecated as of [OpenAI API v1.1.0](https://github.com/openai/openai-python/releases/tag/v1.1.0)
         See https://platform.openai.com/docs/api-reference/chat/create#chat-create-function_call
         """
+        iostream = IOStream.get_default()
+
         func_name = func_call.get("name", "")
         func = self._function_map.get(func_name, None)
 
@@ -2139,7 +2165,7 @@ def execute_function(self, func_call, verbose: bool = False) -> Tuple[bool, Dict
 
             # Try to execute the function
             if arguments is not None:
-                print(
+                iostream.print(
                     colored(f"\n>>>>>>>> EXECUTING FUNCTION {func_name}...", "magenta"),
                     flush=True,
                 )
@@ -2152,7 +2178,7 @@ def execute_function(self, func_call, verbose: bool = False) -> Tuple[bool, Dict
             content = f"Error: Function {func_name} not found."
 
         if verbose:
-            print(
+            iostream.print(
                 colored(f"\nInput arguments: {arguments}\nOutput:\n{content}", "magenta"),
                 flush=True,
             )
@@ -2179,6 +2205,8 @@ async def a_execute_function(self, func_call):
         "function_call" deprecated as of [OpenAI API v1.1.0](https://github.com/openai/openai-python/releases/tag/v1.1.0)
         See https://platform.openai.com/docs/api-reference/chat/create#chat-create-function_call
         """
+        iostream = IOStream.get_default()
+
         func_name = func_call.get("name", "")
         func = self._function_map.get(func_name, None)
 
@@ -2194,7 +2222,7 @@ async def a_execute_function(self, func_call):
 
             # Try to execute the function
             if arguments is not None:
-                print(
+                iostream.print(
                     colored(f"\n>>>>>>>> EXECUTING ASYNC FUNCTION {func_name}...", "magenta"),
                     flush=True,
                 )
@@ -2639,10 +2667,12 @@ def process_last_received_message(self, messages):
 
     def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None:
         """Print the usage summary."""
+        iostream = IOStream.get_default()
+
         if self.client is None:
-            print(f"No cost incurred from agent '{self.name}'.")
+            iostream.print(f"No cost incurred from agent '{self.name}'.")
         else:
-            print(f"Agent '{self.name}':")
+            iostream.print(f"Agent '{self.name}':")
             self.client.print_usage_summary(mode)
 
     def get_actual_usage(self) -> Union[None, Dict[str, int]]:
diff --git a/autogen/agentchat/groupchat.py b/autogen/agentchat/groupchat.py
index 3db9b72cce3..6c0ecec90fe 100644
--- a/autogen/agentchat/groupchat.py
+++ b/autogen/agentchat/groupchat.py
@@ -5,9 +5,9 @@
 from dataclasses import dataclass, field
 from typing import Callable, Dict, List, Literal, Optional, Tuple, Union
 
-from autogen.agentchat.agent import Agent
-from autogen.agentchat.conversable_agent import ConversableAgent
-
+from .agent import Agent
+from .conversable_agent import ConversableAgent
+from ..io.base import IOStream
 from ..code_utils import content_str
 from ..exception_utils import AgentNameConflict, NoEligibleSpeaker, UndefinedNextAgent
 from ..graph_utils import check_graph_validity, invert_disallowed_to_allowed
@@ -257,22 +257,26 @@ def introductions_msg(self, agents: Optional[List[Agent]] = None) -> str:
 
     def manual_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[Agent, None]:
         """Manually select the next speaker."""
+        iostream = IOStream.get_default()
+
         if agents is None:
             agents = self.agents
 
-        print("Please select the next speaker from the following list:")
+        iostream.print("Please select the next speaker from the following list:")
         _n_agents = len(agents)
         for i in range(_n_agents):
-            print(f"{i+1}: {agents[i].name}")
+            iostream.print(f"{i+1}: {agents[i].name}")
         try_count = 0
         # Assume the user will enter a valid number within 3 tries, otherwise use auto selection to avoid blocking.
         while try_count <= 3:
             try_count += 1
             if try_count >= 3:
-                print(f"You have tried {try_count} times. The next speaker will be selected automatically.")
+                iostream.print(f"You have tried {try_count} times. The next speaker will be selected automatically.")
                 break
             try:
-                i = input("Enter the number of the next speaker (enter nothing or `q` to use auto selection): ")
+                i = iostream.input(
+                    "Enter the number of the next speaker (enter nothing or `q` to use auto selection): "
+                )
                 if i == "" or i == "q":
                     break
                 i = int(i)
@@ -281,7 +285,7 @@ def manual_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[A
                 else:
                     raise ValueError
             except ValueError:
-                print(f"Invalid input. Please enter a number between 1 and {_n_agents}.")
+                iostream.print(f"Invalid input. Please enter a number between 1 and {_n_agents}.")
         return None
 
     def random_select_speaker(self, agents: Optional[List[Agent]] = None) -> Union[Agent, None]:
@@ -740,6 +744,8 @@ def clear_agents_history(self, reply: dict, groupchat: GroupChat) -> str:
             reply (dict): reply message dict to analyze.
             groupchat (GroupChat): GroupChat object.
         """
+        iostream = IOStream.get_default()
+
         reply_content = reply["content"]
         # Split the reply into words
         words = reply_content.split()
@@ -775,21 +781,21 @@ def clear_agents_history(self, reply: dict, groupchat: GroupChat) -> str:
         # clear history
         if agent_to_memory_clear:
             if nr_messages_to_preserve:
-                print(
+                iostream.print(
                     f"Clearing history for {agent_to_memory_clear.name} except last {nr_messages_to_preserve} messages."
                 )
             else:
-                print(f"Clearing history for {agent_to_memory_clear.name}.")
+                iostream.print(f"Clearing history for {agent_to_memory_clear.name}.")
             agent_to_memory_clear.clear_history(nr_messages_to_preserve=nr_messages_to_preserve)
         else:
             if nr_messages_to_preserve:
-                print(f"Clearing history for all agents except last {nr_messages_to_preserve} messages.")
+                iostream.print(f"Clearing history for all agents except last {nr_messages_to_preserve} messages.")
                 # clearing history for groupchat here
                 temp = groupchat.messages[-nr_messages_to_preserve:]
                 groupchat.messages.clear()
                 groupchat.messages.extend(temp)
             else:
-                print("Clearing history for all agents.")
+                iostream.print("Clearing history for all agents.")
                 # clearing history for groupchat here
                 groupchat.messages.clear()
             # clearing history for agents
diff --git a/autogen/cache/cache.py b/autogen/cache/cache.py
index cea7b87f70e..1cb507ad79a 100644
--- a/autogen/cache/cache.py
+++ b/autogen/cache/cache.py
@@ -30,7 +30,7 @@ class Cache:
     ALLOWED_CONFIG_KEYS = ["cache_seed", "redis_url", "cache_path_root"]
 
     @staticmethod
-    def redis(cache_seed: Union[str, int] = 42, redis_url: str = "redis://localhost:6379/0") -> Cache:
+    def redis(cache_seed: Union[str, int] = 42, redis_url: str = "redis://localhost:6379/0") -> "Cache":
         """
         Create a Redis cache instance.
 
@@ -44,7 +44,7 @@ def redis(cache_seed: Union[str, int] = 42, redis_url: str = "redis://localhost:
         return Cache({"cache_seed": cache_seed, "redis_url": redis_url})
 
     @staticmethod
-    def disk(cache_seed: Union[str, int] = 42, cache_path_root: str = ".cache") -> Cache:
+    def disk(cache_seed: Union[str, int] = 42, cache_path_root: str = ".cache") -> "Cache":
         """
         Create a Disk cache instance.
 
@@ -81,7 +81,7 @@ def __init__(self, config: Dict[str, Any]):
             self.config.get("cache_path_root", None),
         )
 
-    def __enter__(self) -> AbstractCache:
+    def __enter__(self) -> "Cache":
         """
         Enter the runtime context related to the cache object.
 
diff --git a/autogen/io/__init__.py b/autogen/io/__init__.py
new file mode 100644
index 00000000000..20d6d5a578f
--- /dev/null
+++ b/autogen/io/__init__.py
@@ -0,0 +1,8 @@
+from .base import InputStream, IOStream, OutputStream
+from .console import IOConsole
+from .websockets import IOWebsockets
+
+# Set the default input/output stream to the console
+IOStream._default_io_stream.set(IOConsole())
+
+__all__ = ("IOConsole", "IOStream", "InputStream", "OutputStream", "IOWebsockets")
diff --git a/autogen/io/base.py b/autogen/io/base.py
new file mode 100644
index 00000000000..857d532e4f5
--- /dev/null
+++ b/autogen/io/base.py
@@ -0,0 +1,73 @@
+from contextlib import contextmanager
+from contextvars import ContextVar
+from typing import Any, Iterator, Optional, Protocol, runtime_checkable
+
+__all__ = ("OutputStream", "InputStream", "IOStream")
+
+
+@runtime_checkable
+class OutputStream(Protocol):
+    def print(self, *objects: Any, sep: str = " ", end: str = "\n", flush: bool = False) -> None:
+        """Print data to the output stream.
+
+        Args:
+            objects (any): The data to print.
+            sep (str, optional): The separator between objects. Defaults to " ".
+            end (str, optional): The end of the output. Defaults to "\n".
+            flush (bool, optional): Whether to flush the output. Defaults to False.
+        """
+        ...  # pragma: no cover
+
+
+@runtime_checkable
+class InputStream(Protocol):
+    def input(self, prompt: str = "", *, password: bool = False) -> str:
+        """Read a line from the input stream.
+
+        Args:
+            prompt (str, optional): The prompt to display. Defaults to "".
+            password (bool, optional): Whether to read a password. Defaults to False.
+
+        Returns:
+            str: The line read from the input stream.
+
+        """
+        ...  # pragma: no cover
+
+
+@runtime_checkable
+class IOStream(InputStream, OutputStream, Protocol):
+    """A protocol for input/output streams."""
+
+    @staticmethod
+    def get_default() -> "IOStream":
+        """Get the default input/output stream.
+
+        Returns:
+            IOStream: The default input/output stream.
+        """
+        iostream = IOStream._default_io_stream.get()
+        if iostream is None:
+            raise RuntimeError("No default IOStream has been set")
+        return iostream
+
+    # ContextVar must be used in multithreaded or async environments
+    _default_io_stream: ContextVar[Optional["IOStream"]] = ContextVar("default_iostream")
+    _default_io_stream.set(None)
+
+    @staticmethod
+    @contextmanager
+    def set_default(stream: Optional["IOStream"]) -> Iterator[None]:
+        """Set the default input/output stream.
+
+        Args:
+            stream (IOStream): The input/output stream to set as the default.
+        """
+        global _default_io_stream
+        try:
+            token = IOStream._default_io_stream.set(stream)
+            yield
+        finally:
+            IOStream._default_io_stream.reset(token)
+
+        return
diff --git a/autogen/io/console.py b/autogen/io/console.py
new file mode 100644
index 00000000000..e451fc1418a
--- /dev/null
+++ b/autogen/io/console.py
@@ -0,0 +1,37 @@
+import getpass
+from typing import Any
+
+from .base import IOStream
+
+__all__ = ("IOConsole",)
+
+
+class IOConsole(IOStream):
+    """A console input/output stream."""
+
+    def print(self, *objects: Any, sep: str = " ", end: str = "\n", flush: bool = False) -> None:
+        """Print data to the output stream.
+
+        Args:
+            objects (any): The data to print.
+            sep (str, optional): The separator between objects. Defaults to " ".
+            end (str, optional): The end of the output. Defaults to "\n".
+            flush (bool, optional): Whether to flush the output. Defaults to False.
+        """
+        print(*objects, sep=sep, end=end, flush=flush)
+
+    def input(self, prompt: str = "", *, password: bool = False) -> str:
+        """Read a line from the input stream.
+
+        Args:
+            prompt (str, optional): The prompt to display. Defaults to "".
+            password (bool, optional): Whether to read a password. Defaults to False.
+
+        Returns:
+            str: The line read from the input stream.
+
+        """
+
+        if password:
+            return getpass.getpass(prompt if prompt != "" else "Password: ")
+        return input(prompt)
diff --git a/autogen/io/websockets.py b/autogen/io/websockets.py
new file mode 100644
index 00000000000..45caffcdcc2
--- /dev/null
+++ b/autogen/io/websockets.py
@@ -0,0 +1,207 @@
+import logging
+import ssl
+import threading
+from contextlib import contextmanager
+from functools import partial
+from time import sleep
+from typing import Any, Callable, Dict, Iterable, Iterator, Optional, TYPE_CHECKING, Protocol, Union
+
+from .base import IOStream
+
+# Check if the websockets module is available
+try:
+    from websockets.sync.server import serve as ws_serve
+except ImportError as e:
+    _import_error: Optional[ImportError] = e
+else:
+    _import_error = None
+
+
+__all__ = ("IOWebsockets",)
+
+
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
+# The following type and protocols are used to define the ServerConnection and WebSocketServer classes
+# if websockets is not installed, they would be untyped
+Data = Union[str, bytes]
+
+
+class ServerConnection(Protocol):
+    def send(self, message: Union[Data, Iterable[Data]]) -> None:
+        """Send a message to the client.
+
+        Args:
+            message (Union[Data, Iterable[Data]]): The message to send.
+
+        """
+        ...  # pragma: no cover
+
+    def recv(self, timeout: Optional[float] = None) -> Data:
+        """Receive a message from the client.
+
+        Args:
+            timeout (Optional[float], optional): The timeout for the receive operation. Defaults to None.
+
+        Returns:
+            Data: The message received from the client.
+
+        """
+        ...  # pragma: no cover
+
+    def close(self) -> None:
+        """Close the connection."""
+        ...
+
+
+class WebSocketServer(Protocol):
+    def serve_forever(self) -> None:
+        """Run the server forever."""
+        ...  # pragma: no cover
+
+    def shutdown(self) -> None:
+        """Shutdown the server."""
+        ...  # pragma: no cover
+
+    def __enter__(self) -> "WebSocketServer":
+        """Enter the server context."""
+        ...  # pragma: no cover
+
+    def __exit__(self, exc_type: Any, exc_value: Any, traceback: Any) -> None:
+        """Exit the server context."""
+        ...  # pragma: no cover
+
+
+class IOWebsockets(IOStream):
+    """A websocket input/output stream."""
+
+    def __init__(self, websocket: ServerConnection) -> None:
+        """Initialize the websocket input/output stream.
+
+        Args:
+            websocket (ServerConnection): The websocket server.
+
+        Raises:
+            ImportError: If the websockets module is not available.
+        """
+        if _import_error is not None:
+            raise _import_error  # pragma: no cover
+
+        self._websocket = websocket
+
+    @staticmethod
+    def _handler(websocket: ServerConnection, on_connect: Callable[["IOWebsockets"], None]) -> None:
+        """The handler function for the websocket server."""
+        logger.info(f" - IOWebsockets._handler(): Client connected on {websocket}")
+        # create a new IOWebsockets instance using the websocket that is create when a client connects
+        try:
+            iowebsocket = IOWebsockets(websocket)
+            with IOStream.set_default(iowebsocket):
+                # call the on_connect function
+                try:
+                    on_connect(iowebsocket)
+                except Exception as e:
+                    logger.warning(f" - IOWebsockets._handler(): Error in on_connect: {e}")
+        except Exception as e:
+            logger.error(f" - IOWebsockets._handler(): Unexpected error in IOWebsockets: {e}")
+
+    @staticmethod
+    @contextmanager
+    def run_server_in_thread(
+        *,
+        host: str = "127.0.0.1",
+        port: int = 8765,
+        on_connect: Callable[["IOWebsockets"], None],
+        ssl_context: Optional[ssl.SSLContext] = None,
+        **kwargs: Any,
+    ) -> Iterator[str]:
+        """Factory function to create a websocket input/output stream.
+
+        Args:
+            host (str, optional): The host to bind the server to. Defaults to "127.0.0.1".
+            port (int, optional): The port to bind the server to. Defaults to 8765.
+            on_connect (Callable[[IOWebsockets], None]): The function to be executed on client connection. Typically creates agents and initiate chat.
+            ssl_context (Optional[ssl.SSLContext], optional): The SSL context to use for secure connections. Defaults to None.
+            kwargs (Any): Additional keyword arguments to pass to the websocket server.
+
+        Yields:
+            str: The URI of the websocket server.
+        """
+        server_dict: Dict[str, WebSocketServer] = {}
+
+        def _run_server() -> None:
+            if _import_error is not None:
+                raise _import_error
+
+            # print(f" - _run_server(): starting server on ws://{host}:{port}", flush=True)
+            with ws_serve(
+                handler=partial(IOWebsockets._handler, on_connect=on_connect),
+                host=host,
+                port=port,
+                ssl_context=ssl_context,
+                **kwargs,
+            ) as server:
+                # print(f" - _run_server(): server {server} started on ws://{host}:{port}", flush=True)
+
+                server_dict["server"] = server
+
+                # runs until the server is shutdown
+                server.serve_forever()
+
+                return
+
+        # start server in a separate thread
+        thread = threading.Thread(target=_run_server)
+        thread.start()
+        try:
+            while "server" not in server_dict:
+                sleep(0.1)
+
+            yield f"ws://{host}:{port}"
+
+        finally:
+            # print(f" - run_server_in_thread(): shutting down server on ws://{host}:{port}", flush=True)
+            # gracefully stop server
+            if "server" in server_dict:
+                # print(f" - run_server_in_thread(): shutting down server {server_dict['server']}", flush=True)
+                server_dict["server"].shutdown()
+
+            # wait for the thread to stop
+            if thread:
+                thread.join()
+
+    @property
+    def websocket(self) -> "ServerConnection":
+        """The URI of the websocket server."""
+        return self._websocket
+
+    def print(self, *objects: Any, sep: str = " ", end: str = "\n", flush: bool = False) -> None:
+        """Print data to the output stream.
+
+        Args:
+            objects (any): The data to print.
+            sep (str, optional): The separator between objects. Defaults to " ".
+            end (str, optional): The end of the output. Defaults to "\n".
+            flush (bool, optional): Whether to flush the output. Defaults to False.
+        """
+        xs = sep.join(map(str, objects)) + end
+        self._websocket.send(xs)
+
+    def input(self, prompt: str = "", *, password: bool = False) -> str:
+        """Read a line from the input stream.
+
+        Args:
+            prompt (str, optional): The prompt to display. Defaults to "".
+            password (bool, optional): Whether to read a password. Defaults to False.
+
+        Returns:
+            str: The line read from the input stream.
+
+        """
+        if prompt != "":
+            self._websocket.send(prompt)
+
+        msg = self._websocket.recv()
+
+        return msg.decode("utf-8") if isinstance(msg, bytes) else msg
diff --git a/autogen/oai/client.py b/autogen/oai/client.py
index 0ecc050fac6..ff7f8d37880 100644
--- a/autogen/oai/client.py
+++ b/autogen/oai/client.py
@@ -11,6 +11,7 @@
 from typing import Protocol
 
 from autogen.cache.cache import Cache
+from autogen.io.base import IOStream
 from autogen.oai.openai_utils import get_key, is_valid_api_key, OAI_PRICE1K
 from autogen.token_count_utils import count_token
 
@@ -156,6 +157,8 @@ def create(self, params: Dict[str, Any]) -> ChatCompletion:
         Returns:
             The completion.
         """
+        iostream = IOStream.get_default()
+
         completions: Completions = self._oai_client.chat.completions if "messages" in params else self._oai_client.completions  # type: ignore [attr-defined]
         # If streaming is enabled and has messages, then iterate over the chunks of the response.
         if params.get("stream", False) and "messages" in params:
@@ -164,7 +167,7 @@ def create(self, params: Dict[str, Any]) -> ChatCompletion:
             completion_tokens = 0
 
             # Set the terminal text color to green
-            print("\033[32m", end="")
+            iostream.print("\033[32m", end="")
 
             # Prepare for potential function call
             full_function_call: Optional[Dict[str, Any]] = None
@@ -216,15 +219,15 @@ def create(self, params: Dict[str, Any]) -> ChatCompletion:
 
                         # If content is present, print it to the terminal and update response variables
                         if content is not None:
-                            print(content, end="", flush=True)
+                            iostream.print(content, end="", flush=True)
                             response_contents[choice.index] += content
                             completion_tokens += 1
                         else:
-                            # print()
+                            # iostream.print()
                             pass
 
             # Reset the terminal text color
-            print("\033[0m\n")
+            iostream.print("\033[0m\n")
 
             # Prepare the final ChatCompletion object based on the accumulated data
             model = chunk.model.replace("gpt-35", "gpt-3.5")  # hack for Azure API
@@ -825,25 +828,26 @@ def update_usage(usage_summary, response_usage):
 
     def print_usage_summary(self, mode: Union[str, List[str]] = ["actual", "total"]) -> None:
         """Print the usage summary."""
+        iostream = IOStream.get_default()
 
         def print_usage(usage_summary: Optional[Dict[str, Any]], usage_type: str = "total") -> None:
             word_from_type = "including" if usage_type == "total" else "excluding"
             if usage_summary is None:
-                print("No actual cost incurred (all completions are using cache).", flush=True)
+                iostream.print("No actual cost incurred (all completions are using cache).", flush=True)
                 return
 
-            print(f"Usage summary {word_from_type} cached usage: ", flush=True)
-            print(f"Total cost: {round(usage_summary['total_cost'], 5)}", flush=True)
+            iostream.print(f"Usage summary {word_from_type} cached usage: ", flush=True)
+            iostream.print(f"Total cost: {round(usage_summary['total_cost'], 5)}", flush=True)
             for model, counts in usage_summary.items():
                 if model == "total_cost":
                     continue  #
-                print(
+                iostream.print(
                     f"* Model '{model}': cost: {round(counts['cost'], 5)}, prompt_tokens: {counts['prompt_tokens']}, completion_tokens: {counts['completion_tokens']}, total_tokens: {counts['total_tokens']}",
                     flush=True,
                 )
 
         if self.total_usage_summary is None:
-            print('No usage summary. Please call "create" first.', flush=True)
+            iostream.print('No usage summary. Please call "create" first.', flush=True)
             return
 
         if isinstance(mode, list):
@@ -856,14 +860,14 @@ def print_usage(usage_summary: Optional[Dict[str, Any]], usage_type: str = "tota
             elif "total" in mode:
                 mode = "total"
 
-        print("-" * 100, flush=True)
+        iostream.print("-" * 100, flush=True)
         if mode == "both":
             print_usage(self.actual_usage_summary, "actual")
-            print()
+            iostream.print()
             if self.total_usage_summary != self.actual_usage_summary:
                 print_usage(self.total_usage_summary, "total")
             else:
-                print(
+                iostream.print(
                     "All completions are non-cached: the total cost with cached completions is the same as actual cost.",
                     flush=True,
                 )
@@ -873,7 +877,7 @@ def print_usage(usage_summary: Optional[Dict[str, Any]], usage_type: str = "tota
             print_usage(self.actual_usage_summary, "actual")
         else:
             raise ValueError(f'Invalid mode: {mode}, choose from "actual", "total", ["actual", "total"]')
-        print("-" * 100, flush=True)
+        iostream.print("-" * 100, flush=True)
 
     def clear_usage_summary(self) -> None:
         """Clear the usage summary."""
diff --git a/notebook/agentchat_websockets.ipynb b/notebook/agentchat_websockets.ipynb
new file mode 100644
index 00000000000..7bf03f786de
--- /dev/null
+++ b/notebook/agentchat_websockets.ipynb
@@ -0,0 +1,572 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "9a71fa36",
+   "metadata": {},
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_websockets.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
+    "\n",
+    "# Websockets: Streaming input and output using websockets\n",
+    "\n",
+    "This notebook demonstrates how to use the [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) class to stream both input and output using websockets. The use of websockets allows you to build web clients that are more responsive than the one using web methods. The main difference is that the webosockets allows you to push data while you need to poll the server for new response using web mothods.\n",
+    "\n",
+    "\n",
+    "In this guide, we explore the capabilities of the [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) class. It is specifically designed to enhance the development of clients such as web clients which use  websockets for streaming both input and output. The [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) stands out by enabling a more dynamic and interactive user experience for web applications.\n",
+    "\n",
+    "Websockets technology is at the core of this functionality, offering a significant advancement over traditional web methods by allowing data to be \"pushed\" to the client in real-time. This is a departure from the conventional approach where clients must repeatedly \"poll\" the server to check for any new responses. By employing the underlining [websockets](https://websockets.readthedocs.io/) library, the IOStream class facilitates a continuous, two-way communication channel between the server and client. This ensures that updates are received instantly, without the need for constant polling, thereby making web clients more efficient and responsive.\n",
+    "\n",
+    "The real power of websockets, leveraged through the [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) class, lies in its ability to create highly responsive web clients. This responsiveness is critical for applications requiring real-time data updates such as chat applications. By integrating the [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) class into your web application, you not only enhance user experience through immediate data transmission but also reduce the load on your server by eliminating unnecessary polling.\n",
+    "\n",
+    "In essence, the transition to using websockets through the [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) class marks a significant enhancement in web client development. This approach not only streamlines the data exchange process between clients and servers but also opens up new possibilities for creating more interactive and engaging web applications. By following this guide, developers can harness the full potential of websockets and the [`IOStream`](https://microsoft.github.io/autogen/docs/reference/io/base/IOStream) class to push the boundaries of what is possible with web client responsiveness and interactivity.\n",
+    "\n",
+    "## Requirements\n",
+    "\n",
+    "````{=mdx}\n",
+    ":::info Requirements\n",
+    "Some extra dependencies are needed for this notebook, which can be installed via pip:\n",
+    "\n",
+    "```bash\n",
+    "pip install pyautogen[websockets] fastapi uvicorn\n",
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n",
+    ":::\n",
+    "````"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "5ebd2397",
+   "metadata": {},
+   "source": [
+    "## Set your API Endpoint\n",
+    "\n",
+    "The [`config_list_from_json`](https://microsoft.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dca301a4",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "gpt-4\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tempfile import TemporaryDirectory\n",
+    "\n",
+    "from websockets.sync.client import connect as ws_connect\n",
+    "\n",
+    "import autogen\n",
+    "from autogen.cache import Cache\n",
+    "from autogen.io.websockets import IOStream, IOWebsockets\n",
+    "\n",
+    "config_list = autogen.config_list_from_json(\n",
+    "    \"OAI_CONFIG_LIST\",\n",
+    "    filter_dict={\n",
+    "        \"model\": [\"gpt-4\", \"gpt-3.5-turbo\", \"gpt-3.5-turbo-16k\"],\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "print(config_list[0][\"model\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "92fde41f",
+   "metadata": {},
+   "source": [
+    "````{=mdx}\n",
+    ":::tip\n",
+    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n",
+    ":::\n",
+    "````"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "2b9526e7",
+   "metadata": {},
+   "source": [
+    "## Defining `on_connect` function\n",
+    "\n",
+    "An `on_connect` function is a crucial part of applications that utilize websockets, acting as an event handler that is called whenever a new client connection is established. This function is designed to initiate any necessary setup, communication protocols, or data exchange procedures specific to the newly connected client. Essentially, it lays the groundwork for the interactive session that follows, configuring how the server and the client will communicate and what initial actions are to be taken once a connection is made. Now, let's delve into the details of how to define this function, especially in the context of using the AutoGen framework with websockets.\n",
+    "\n",
+    "\n",
+    "Upon a client's connection to the websocket server, the server automatically initiates a new instance of the [`IOWebsockets`](https://microsoft.github.io/autogen/docs/reference/io/websockets/IOWebsockets) class. This instance is crucial for managing the data flow between the server and the client. The `on_connect` function leverages this instance to set up the communication protocol, define interaction rules, and initiate any preliminary data exchanges or configurations required for the client-server interaction to proceed smoothly.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "9fb85afb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def on_connect(iostream: IOWebsockets) -> None:\n",
+    "    print(f\" - on_connect(): Connected to client using IOWebsockets {iostream}\", flush=True)\n",
+    "\n",
+    "    print(\" - on_connect(): Receiving message from client.\", flush=True)\n",
+    "\n",
+    "    initial_msg = iostream.input()\n",
+    "\n",
+    "    llm_config = {\n",
+    "        \"config_list\": config_list,\n",
+    "        \"stream\": True,\n",
+    "    }\n",
+    "\n",
+    "    agent = autogen.ConversableAgent(\n",
+    "        name=\"chatbot\",\n",
+    "        system_message=\"Complete a task given to you and reply TERMINATE when the task is done. If asked about the weather, use tool weather_forecast(city) to get the weather forecast for a city.\",\n",
+    "        llm_config=llm_config,\n",
+    "    )\n",
+    "\n",
+    "    # create a UserProxyAgent instance named \"user_proxy\"\n",
+    "    user_proxy = autogen.UserProxyAgent(\n",
+    "        name=\"user_proxy\",\n",
+    "        system_message=\"A proxy for the user.\",\n",
+    "        is_termination_msg=lambda x: x.get(\"content\", \"\") and x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
+    "        human_input_mode=\"NEVER\",\n",
+    "        max_consecutive_auto_reply=10,\n",
+    "        code_execution_config=False,\n",
+    "    )\n",
+    "\n",
+    "    @user_proxy.register_for_execution()\n",
+    "    @agent.register_for_llm(description=\"Weather forecats for a city\")\n",
+    "    def weather_forecast(city: str) -> str:\n",
+    "        return f\"The weather forecast for {city} is sunny.\"\n",
+    "\n",
+    "    # we will use a temporary directory as the cache path root to ensure fresh completion each time\n",
+    "    with TemporaryDirectory() as cache_path_root:\n",
+    "        with Cache.disk(cache_path_root=cache_path_root) as cache:\n",
+    "            print(\n",
+    "                f\" - on_connect(): Initiating chat with agent {agent} using message '{initial_msg}'\",\n",
+    "                flush=True,\n",
+    "            )\n",
+    "            user_proxy.initiate_chat(  # noqa: F704\n",
+    "                agent,\n",
+    "                message=initial_msg,\n",
+    "                cache=cache,\n",
+    "            )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1124796",
+   "metadata": {},
+   "source": [
+    "Here's an explanation on how a typical `on_connect` function such as the one in the example above is defined:\n",
+    "\n",
+    "1. **Receiving Initial Message**: Immediately after establishing a connection, receive an initial message from the client. This step is crucial for understanding the client's request or initiating the conversation flow.\n",
+    "\n",
+    "2. **Receiving Initial Message**: Immediately after establishing a connection, receive an initial message from the client. This step is crucial for understanding the client's request or initiating the conversation flow.\n",
+    "\n",
+    "3. **Configure the LLM**: Define the configuration for your large language model (LLM), specifying the list of configurations and the streaming capability. This configuration will be used to tailor the behavior of your conversational agent.\n",
+    "\n",
+    "4. **Instantiate ConversableAgent and UserProxyAgent**: Create an instance of ConversableAgent with a specific system message and the LLM configuration. Similarly, create a UserProxyAgent instance, defining its termination condition, human input mode, and other relevant parameters.\n",
+    "\n",
+    "5. **Define Agent-specific Functions**: If your conversable agent requires executing specific tasks, such as fetching a weather forecast in the example below, define these functions within the on_connect scope. Decorate these functions accordingly to link them with your agents.\n",
+    "\n",
+    "5. **Initiate Conversation**: Finally, use the `initiate_chat` method of your `UserProxyAgent` to start the interaction with the conversable agent, passing the initial message and a cache mechanism for efficiency."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ef868a",
+   "metadata": {},
+   "source": [
+    "## Testing websockets server with Python client\n",
+    "\n",
+    "Testing an `on_connect` function with a Python client involves simulating a client-server interaction to ensure the setup, data exchange, and communication protocols function as intended. Here’s a brief explanation on how to conduct this test using a Python client:\n",
+    "\n",
+    "1. **Start the Websocket Server**: Use the `IOWebsockets.run_server_in_thread method` to start the server in a separate thread, specifying the on_connect function and the port. This method returns the URI of the running websocket server.\n",
+    "\n",
+    "2. **Connect to the Server**: Open a connection to the server using the returned URI. This simulates a client initiating a connection to your websocket server.\n",
+    "\n",
+    "3. **Send a Message to the Server**: Once connected, send a message from the client to the server. This tests the server's ability to receive messages through the established websocket connection.\n",
+    "\n",
+    "4. **Receive and Process Messages**: Implement a loop to continuously receive messages from the server. Decode the messages if necessary, and process them accordingly. This step verifies the server's ability to respond back to the client's request.\n",
+    "\n",
+    "This test scenario effectively evaluates the interaction between a client and a server using the `on_connect` function, by simulating a realistic message exchange. It ensures that the server can handle incoming connections, process messages, and communicate responses back to the client, all critical functionalities for a robust websocket-based application."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "4fbe004d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " - test_setup() with websocket server running on ws://127.0.0.1:8765.\n",
+      " - on_connect(): Connected to client using IOWebsockets <autogen.io.websockets.IOWebsockets object at 0x75ad84aa0d60>\n",
+      " - on_connect(): Receiving message from client.\n",
+      " - Connected to server on ws://127.0.0.1:8765\n",
+      " - Sending message to server.\n",
+      " - on_connect(): Initiating chat with agent <autogen.agentchat.conversable_agent.ConversableAgent object at 0x75ad84a72b30> using message 'Check out the weather in Paris and write a poem about it.'\n",
+      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
+      "\n",
+      "Check out the weather in Paris and write a poem about it.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[32m\u001b[32m\u001b[0m\n",
+      "\n",
+      "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
+      "\n",
+      "\n",
+      "\u001b[32m***** Suggested tool Call (call_U5VR0hck9KhDFWPdvmo1Eoke): weather_forecast *****\u001b[0m\n",
+      "Arguments: \n",
+      "{\n",
+      "  \"city\": \"Paris\"\n",
+      "}\n",
+      "\u001b[32m*********************************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[35m\n",
+      ">>>>>>>> EXECUTING FUNCTION weather_forecast...\u001b[0m\n",
+      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
+      "\n",
+      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
+      "\n",
+      "\u001b[32m***** Response from calling tool \"call_U5VR0hck9KhDFWPdvmo1Eoke\" *****\u001b[0m\n",
+      "The weather forecast for Paris is sunny.\n",
+      "\u001b[32m**********************************************************************\u001b[0m\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> USING AUTO REPLY...\u001b[0m\n",
+      "\u001b[32m\u001b[32mIn the city of love, shines the sun above,\n",
+      "Paris basks in golden rays, a beautiful day to praise.\n",
+      "Strolling down the Champs Elysées, the warm light leads the way,\n",
+      "In the glow, silhouettes dance, a perfect setting for romance.\n",
+      "\n",
+      "In the sunlight, the Seine sparkles bright, reflecting the City of Light,\n",
+      "Not a cloud in the crystal-clear blue sky, as the doves sail high.\n",
+      "Sunny Paris so profound, beauty all around,\n",
+      "Alive under the radiant crown, she wears her sunlight like a gown.\n",
+      "\n",
+      "TERMINATE\u001b[0m\n",
+      "\n",
+      "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
+      "\n",
+      "In the city of love, shines the sun above,\n",
+      "Paris basks in golden rays, a beautiful day to praise.\n",
+      "Strolling down the Champs Elysées, the warm light leads the way,\n",
+      "In the glow, silhouettes dance, a perfect setting for romance.\n",
+      "\n",
+      "In the sunlight, the Seine sparkles bright, reflecting the City of Light,\n",
+      "Not a cloud in the crystal-clear blue sky, as the doves sail high.\n",
+      "Sunny Paris so profound, beauty all around,\n",
+      "Alive under the radiant crown, she wears her sunlight like a gown.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      " - Received TERMINATE message. Exiting.\n"
+     ]
+    }
+   ],
+   "source": [
+    "with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8765) as uri:\n",
+    "    print(f\" - test_setup() with websocket server running on {uri}.\", flush=True)\n",
+    "\n",
+    "    with ws_connect(uri) as websocket:\n",
+    "        print(f\" - Connected to server on {uri}\", flush=True)\n",
+    "\n",
+    "        print(\" - Sending message to server.\", flush=True)\n",
+    "        # websocket.send(\"2+2=?\")\n",
+    "        websocket.send(\"Check out the weather in Paris and write a poem about it.\")\n",
+    "\n",
+    "        while True:\n",
+    "            message = websocket.recv()\n",
+    "            message = message.decode(\"utf-8\") if isinstance(message, bytes) else message\n",
+    "\n",
+    "            print(message, end=\"\", flush=True)\n",
+    "\n",
+    "            if \"TERMINATE\" in message:\n",
+    "                print()\n",
+    "                print(\" - Received TERMINATE message. Exiting.\", flush=True)\n",
+    "                break"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a656564",
+   "metadata": {},
+   "source": [
+    "## Testing websockets server running inside FastAPI server with HTML/JS client\n",
+    "\n",
+    "The code snippets below outlines an approach for testing an `on_connect` function in a web environment using [FastAPI](https://fastapi.tiangolo.com/) to serve a simple interactive HTML page. This method allows users to send messages through a web interface, which are then processed by the server running the AutoGen framework via websockets. Here's a step-by-step explanation:\n",
+    "\n",
+    "1. **FastAPI Application Setup**: The code initiates by importing necessary libraries and setting up a FastAPI application. FastAPI is a modern, fast web framework for building APIs with Python 3.7+ based on standard Python type hints.\n",
+    "\n",
+    "2. **HTML Template for User Interaction**: An HTML template is defined as a multi-line Python string, which includes a basic form for message input and a script for managing websocket communication. This template creates a user interface where messages can be sent to the server and responses are displayed dynamically.\n",
+    "\n",
+    "3. **Running the Websocket Server**: The `run_websocket_server` async context manager starts the websocket server using `IOWebsockets.run_server_in_thread` with the specified `on_connect` function and port. This server listens for incoming websocket connections.\n",
+    "\n",
+    "4. **FastAPI Route for Serving HTML Page**: A FastAPI route (`@app.get(\"/\")`) is defined to serve the HTML page to users. When a user accesses the root URL, the HTML content for the websocket chat is returned, allowing them to interact with the websocket server.\n",
+    "\n",
+    "5. **Starting the FastAPI Application**: Lastly, the FastAPI application is started using Uvicorn, an ASGI server, configured with the app and additional parameters as needed. The server is then launched to serve the FastAPI application, making the interactive HTML page accessible to users.\n",
+    "\n",
+    "This method of testing allows for interactive communication between the user and the server, providing a practical way to demonstrate and evaluate the behavior of the on_connect function in real-time. Users can send messages through the webpage, and the server processes these messages as per the logic defined in the on_connect function, showcasing the capabilities and responsiveness of the AutoGen framework's websocket handling in a user-friendly manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "5e55dc06",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'fastapi'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 4\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mcontextlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m asynccontextmanager  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FastAPI  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m      5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mfastapi\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mresponses\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m HTMLResponse  \u001b[38;5;66;03m# noqa: E402\u001b[39;00m\n\u001b[1;32m      7\u001b[0m PORT \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m8000\u001b[39m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'fastapi'"
+     ]
+    }
+   ],
+   "source": [
+    "from contextlib import asynccontextmanager  # noqa: E402\n",
+    "from pathlib import Path  # noqa: E402\n",
+    "\n",
+    "from fastapi import FastAPI  # noqa: E402\n",
+    "from fastapi.responses import HTMLResponse  # noqa: E402\n",
+    "\n",
+    "PORT = 8000\n",
+    "\n",
+    "html = \"\"\"\n",
+    "<!DOCTYPE html>\n",
+    "<html>\n",
+    "    <head>\n",
+    "        <title>Autogen websocket test</title>\n",
+    "    </head>\n",
+    "    <body>\n",
+    "        <h1>WebSocket Chat</h1>\n",
+    "        <form action=\"\" onsubmit=\"sendMessage(event)\">\n",
+    "            <input type=\"text\" id=\"messageText\" autocomplete=\"off\"/>\n",
+    "            <button>Send</button>\n",
+    "        </form>\n",
+    "        <ul id='messages'>\n",
+    "        </ul>\n",
+    "        <script>\n",
+    "            var ws = new WebSocket(\"ws://localhost:8080/ws\");\n",
+    "            ws.onmessage = function(event) {\n",
+    "                var messages = document.getElementById('messages')\n",
+    "                var message = document.createElement('li')\n",
+    "                var content = document.createTextNode(event.data)\n",
+    "                message.appendChild(content)\n",
+    "                messages.appendChild(message)\n",
+    "            };\n",
+    "            function sendMessage(event) {\n",
+    "                var input = document.getElementById(\"messageText\")\n",
+    "                ws.send(input.value)\n",
+    "                input.value = ''\n",
+    "                event.preventDefault()\n",
+    "            }\n",
+    "        </script>\n",
+    "    </body>\n",
+    "</html>\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "@asynccontextmanager\n",
+    "async def run_websocket_server(app):\n",
+    "    with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8080) as uri:\n",
+    "        print(f\"Websocket server started at {uri}.\", flush=True)\n",
+    "\n",
+    "        yield\n",
+    "\n",
+    "\n",
+    "app = FastAPI(lifespan=run_websocket_server)\n",
+    "\n",
+    "\n",
+    "@app.get(\"/\")\n",
+    "async def get():\n",
+    "    return HTMLResponse(html)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d92e50b5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import uvicorn  # noqa: E402\n",
+    "\n",
+    "config = uvicorn.Config(app)\n",
+    "server = uvicorn.Server(config)\n",
+    "await server.serve()  # noqa: F704"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c8c9f61",
+   "metadata": {},
+   "source": [
+    "The testing setup described above, leveraging FastAPI and websockets, not only serves as a robust testing framework for the on_connect function but also lays the groundwork for developing real-world applications. This approach exemplifies how web-based interactions can be made dynamic and real-time, a critical aspect of modern application development.\n",
+    "\n",
+    "For instance, this setup can be directly applied or adapted to build interactive chat applications, real-time data dashboards, or live support systems. The integration of websockets enables the server to push updates to clients instantly, a key feature for applications that rely on the timely delivery of information. For example, a chat application built on this framework can support instantaneous messaging between users, enhancing user engagement and satisfaction.\n",
+    "\n",
+    "Moreover, the simplicity and interactivity of the HTML page used for testing reflect how user interfaces can be designed to provide seamless experiences. Developers can expand upon this foundation to incorporate more sophisticated elements such as user authentication, message encryption, and custom user interactions, further tailoring the application to meet specific use case requirements.\n",
+    "\n",
+    "The flexibility of the FastAPI framework, combined with the real-time communication enabled by websockets, provides a powerful toolset for developers looking to build scalable, efficient, and highly interactive web applications. Whether it's for creating collaborative platforms, streaming services, or interactive gaming experiences, this testing setup offers a glimpse into the potential applications that can be developed with these technologies."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cfb50946",
+   "metadata": {},
+   "source": [
+    "## Testing  websockets server with HTML/JS client\n",
+    "\n",
+    "The provided code snippet below is an example of how to create an interactive testing environment for an `on_connect` function using Python's built-in `http.server` module. This setup allows for real-time interaction within a web browser, enabling developers to test the websocket functionality in a more user-friendly and practical manner. Here's a breakdown of how this code operates and its potential applications:\n",
+    "\n",
+    "1. **Serving a Simple HTML Page**: The code starts by defining an HTML page that includes a form for sending messages and a list to display incoming messages. JavaScript is used to handle the form submission and websocket communication.\n",
+    "\n",
+    "2. **Temporary Directory for HTML File**: A temporary directory is created to store the HTML file. This approach ensures that the testing environment is clean and isolated, minimizing conflicts with existing files or configurations.\n",
+    "\n",
+    "3. **Custom HTTP Request Handler**: A custom subclass of `SimpleHTTPRequestHandler` is defined to serve the HTML file. This handler overrides the do_GET method to redirect the root path (`/`) to the `chat.html` page, ensuring that visitors to the server's root URL are immediately presented with the chat interface.\n",
+    "\n",
+    "4. **Starting the Websocket Server**: Concurrently, a websocket server is started on a different port using the `IOWebsockets.run_server_in_thread` method, with the previously defined `on_connect` function as the callback for new connections.\n",
+    "\n",
+    "5. **HTTP Server for the HTML Interface**: An HTTP server is instantiated to serve the HTML chat interface, enabling users to interact with the websocket server through a web browser.\n",
+    "\n",
+    "This setup showcases a practical application of integrating websockets with a simple HTTP server to create a dynamic and interactive web application. By using Python's standard library modules, it demonstrates a low-barrier entry to developing real-time applications such as chat systems, live notifications, or interactive dashboards.\n",
+    "\n",
+    "The key takeaway from this code example is how easily Python's built-in libraries can be leveraged to prototype and test complex web functionalities. For developers looking to build real-world applications, this approach offers a straightforward method to validate and refine websocket communication logic before integrating it into larger frameworks or systems. The simplicity and accessibility of this testing setup make it an excellent starting point for developing a wide range of interactive web applications.\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "708a98de",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from http.server import HTTPServer, SimpleHTTPRequestHandler  # noqa: E402\n",
+    "\n",
+    "PORT = 8000\n",
+    "\n",
+    "html = \"\"\"\n",
+    "<!DOCTYPE html>\n",
+    "<html>\n",
+    "    <head>\n",
+    "        <title>Autogen websocket test</title>\n",
+    "    </head>\n",
+    "    <body>\n",
+    "        <h1>WebSocket Chat</h1>\n",
+    "        <form action=\"\" onsubmit=\"sendMessage(event)\">\n",
+    "            <input type=\"text\" id=\"messageText\" autocomplete=\"off\"/>\n",
+    "            <button>Send</button>\n",
+    "        </form>\n",
+    "        <ul id='messages'>\n",
+    "        </ul>\n",
+    "        <script>\n",
+    "            var ws = new WebSocket(\"ws://localhost:8080/ws\");\n",
+    "            ws.onmessage = function(event) {\n",
+    "                var messages = document.getElementById('messages')\n",
+    "                var message = document.createElement('li')\n",
+    "                var content = document.createTextNode(event.data)\n",
+    "                message.appendChild(content)\n",
+    "                messages.appendChild(message)\n",
+    "            };\n",
+    "            function sendMessage(event) {\n",
+    "                var input = document.getElementById(\"messageText\")\n",
+    "                ws.send(input.value)\n",
+    "                input.value = ''\n",
+    "                event.preventDefault()\n",
+    "            }\n",
+    "        </script>\n",
+    "    </body>\n",
+    "</html>\n",
+    "\"\"\"\n",
+    "\n",
+    "with TemporaryDirectory() as temp_dir:\n",
+    "    # create a simple HTTP webpage\n",
+    "    path = Path(temp_dir) / \"chat.html\"\n",
+    "    with open(path, \"w\") as f:\n",
+    "        f.write(html)\n",
+    "\n",
+    "    #\n",
+    "    class MyRequestHandler(SimpleHTTPRequestHandler):\n",
+    "        def __init__(self, *args, **kwargs):\n",
+    "            super().__init__(*args, directory=temp_dir, **kwargs)\n",
+    "\n",
+    "        def do_GET(self):\n",
+    "            if self.path == \"/\":\n",
+    "                self.path = \"/chat.html\"\n",
+    "            return SimpleHTTPRequestHandler.do_GET(self)\n",
+    "\n",
+    "    handler = MyRequestHandler\n",
+    "\n",
+    "    with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8080) as uri:\n",
+    "        print(f\"Websocket server started at {uri}.\", flush=True)\n",
+    "\n",
+    "        with HTTPServer((\"\", PORT), handler) as httpd:\n",
+    "            print(\"HTTP server started at http://localhost:\" + str(PORT))\n",
+    "            httpd.serve_forever()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19656d0e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "front_matter": {
+   "description": "Websockets facilitate real-time, bidirectional communication between web clients and servers, enhancing the responsiveness and interactivity of AutoGen-powered applications.",
+   "tags": [
+    "websockets",
+    "streaming"
+   ]
+  },
+  "kernelspec": {
+   "display_name": "flaml_dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pyproject.toml b/pyproject.toml
index f62668659e2..838e956c043 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -67,6 +67,8 @@ files = [
     "autogen/exception_utils.py",
     "autogen/coding",
     "autogen/oai/openai_utils.py",
+    "autogen/io",
+    "test/io",
 ]
 
 exclude = [
diff --git a/setup.py b/setup.py
index 4f94ad5e589..78e788dead9 100644
--- a/setup.py
+++ b/setup.py
@@ -65,8 +65,9 @@
         "graph": ["networkx", "matplotlib"],
         "websurfer": ["beautifulsoup4", "markdownify", "pdfminer.six", "pathvalidate"],
         "redis": ["redis"],
+        "websockets": ["websockets>=12.0,<13"],
         "jupyter-executor": jupyter_executor,
-        "types": ["mypy==1.9.0"] + jupyter_executor,
+        "types": ["mypy==1.9.0", "pytest>=6.1.1,<8"] + jupyter_executor,
     },
     classifiers=[
         "Programming Language :: Python :: 3",
diff --git a/test/agentchat/test_agent_logging.py b/test/agentchat/test_agent_logging.py
index dfeb631092c..0a974ff8dc5 100644
--- a/test/agentchat/test_agent_logging.py
+++ b/test/agentchat/test_agent_logging.py
@@ -157,7 +157,8 @@ def test_two_agents_logging(db_connection):
         assert row["session_id"] and row["session_id"] == session_id
         assert row["class"] in ["AzureOpenAI", "OpenAI"]
         init_args = json.loads(row["init_args"])
-        assert "api_version" in init_args
+        if row["class"] == "AzureOpenAI":
+            assert "api_version" in init_args
         assert row["timestamp"], "timestamp is empty"
 
     # Verify oai wrapper table
diff --git a/test/io/test_base.py b/test/io/test_base.py
new file mode 100644
index 00000000000..a96f8cb3c79
--- /dev/null
+++ b/test/io/test_base.py
@@ -0,0 +1,28 @@
+from typing import Any
+
+from autogen.io import IOConsole, IOStream, IOWebsockets
+
+
+class TestIOStream:
+    def test_initial_default_io_stream(self) -> None:
+        assert isinstance(IOStream.get_default(), IOConsole)
+
+    def test_set_default_io_stream(self) -> None:
+        class MyIOStream(IOStream):
+            def print(self, *objects: Any, sep: str = " ", end: str = "\n", flush: bool = False) -> None:
+                pass
+
+            def input(self, prompt: str = "", *, password: bool = False) -> str:
+                return "Hello, World!"
+
+        assert isinstance(IOStream.get_default(), IOConsole)
+
+        with IOStream.set_default(MyIOStream()):
+            assert isinstance(IOStream.get_default(), MyIOStream)
+
+            with IOStream.set_default(IOConsole()):
+                assert isinstance(IOStream.get_default(), IOConsole)
+
+            assert isinstance(IOStream.get_default(), MyIOStream)
+
+        assert isinstance(IOStream.get_default(), IOConsole)
diff --git a/test/io/test_console.py b/test/io/test_console.py
new file mode 100644
index 00000000000..a0b95a5bec4
--- /dev/null
+++ b/test/io/test_console.py
@@ -0,0 +1,38 @@
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from autogen.io import IOConsole
+
+
+class TestConsoleIO:
+    def setup_method(self) -> None:
+        self.console_io = IOConsole()
+
+    @patch("builtins.print")
+    def test_print(self, mock_print: MagicMock) -> None:
+        # calling the print method should call the mock of the builtin print function
+        self.console_io.print("Hello, World!", flush=True)
+        mock_print.assert_called_once_with("Hello, World!", end="\n", sep=" ", flush=True)
+
+    @patch("builtins.input")
+    def test_input(self, mock_input: MagicMock) -> None:
+        # calling the input method should call the mock of the builtin input function
+        mock_input.return_value = "Hello, World!"
+
+        actual = self.console_io.input("Hi!")
+        assert actual == "Hello, World!"
+        mock_input.assert_called_once_with("Hi!")
+
+    @pytest.mark.parametrize("prompt", ["", "Password: ", "Enter you password:"])
+    def test_input_password(self, monkeypatch: pytest.MonkeyPatch, prompt: str) -> None:
+        mock_getpass = MagicMock()
+        mock_getpass.return_value = "123456"
+        monkeypatch.setattr("getpass.getpass", mock_getpass)
+
+        actual = self.console_io.input(prompt, password=True)
+        assert actual == "123456"
+        if prompt == "":
+            mock_getpass.assert_called_once_with("Password: ")
+        else:
+            mock_getpass.assert_called_once_with(prompt)
diff --git a/test/io/test_websockets.py b/test/io/test_websockets.py
new file mode 100644
index 00000000000..2c7fbbc5f4a
--- /dev/null
+++ b/test/io/test_websockets.py
@@ -0,0 +1,176 @@
+from tempfile import TemporaryDirectory
+from typing import Dict
+
+import pytest
+from autogen.io.base import IOStream
+from conftest import skip_openai
+
+import autogen
+from autogen.cache.cache import Cache
+from autogen.io import IOWebsockets
+
+KEY_LOC = "notebook"
+OAI_CONFIG_LIST = "OAI_CONFIG_LIST"
+
+# Check if the websockets module is available
+try:
+    from websockets.sync.client import connect as ws_connect
+except ImportError:  # pragma: no cover
+    skip_test = True
+else:
+    skip_test = False
+
+
+@pytest.mark.skipif(skip_test, reason="websockets module is not available")
+class TestConsoleIOWithWebsockets:
+    def test_input_print(self) -> None:
+        print()
+        print("Testing input/print", flush=True)
+
+        def on_connect(iostream: IOWebsockets) -> None:
+            print(f" - on_connect(): Connected to client using IOWebsockets {iostream}", flush=True)
+
+            print(" - on_connect(): Receiving message from client.", flush=True)
+
+            msg = iostream.input()
+
+            print(f" - on_connect(): Received message '{msg}' from client.", flush=True)
+
+            assert msg == "Hello world!"
+
+            for msg in ["Hello, World!", "Over and out!"]:
+                print(f" - on_connect(): Sending message '{msg}' to client.", flush=True)
+
+                iostream.print(msg)
+
+            print(" - on_connect(): Receiving message from client.", flush=True)
+
+            msg = iostream.input("May I?")
+
+            print(f" - on_connect(): Received message '{msg}' from client.", flush=True)
+            assert msg == "Yes"
+
+            return
+
+        with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8765) as uri:
+            print(f" - test_setup() with websocket server running on {uri}.", flush=True)
+
+            with ws_connect(uri) as websocket:
+                print(f" - Connected to server on {uri}", flush=True)
+
+                print(" - Sending message to server.", flush=True)
+                websocket.send("Hello world!")
+
+                for expected in ["Hello, World!", "Over and out!", "May I?"]:
+                    print(" - Receiving message from server.", flush=True)
+                    message = websocket.recv()
+                    message = message.decode("utf-8") if isinstance(message, bytes) else message
+                    # drop the newline character
+                    if message.endswith("\n"):
+                        message = message[:-1]
+
+                    print(
+                        f"   - Asserting received message '{message}' is the same as the expected message '{expected}'",
+                        flush=True,
+                    )
+                    assert message == expected
+
+                print(" - Sending message 'Yes' to server.", flush=True)
+                websocket.send("Yes")
+
+        print("Test passed.", flush=True)
+
+    @pytest.mark.skipif(skip_openai, reason="requested to skip")
+    def test_chat(self) -> None:
+        print("Testing setup", flush=True)
+
+        success_dict = {"success": False}
+
+        def on_connect(iostream: IOWebsockets, success_dict: Dict[str, bool] = success_dict) -> None:
+            print(f" - on_connect(): Connected to client using IOWebsockets {iostream}", flush=True)
+
+            print(" - on_connect(): Receiving message from client.", flush=True)
+
+            initial_msg = iostream.input()
+
+            config_list = autogen.config_list_from_json(
+                OAI_CONFIG_LIST,
+                filter_dict={
+                    "model": [
+                        "gpt-3.5-turbo",
+                        "gpt-3.5-turbo-16k",
+                        "gpt-4",
+                        "gpt-4-0314",
+                        "gpt4",
+                        "gpt-4-32k",
+                        "gpt-4-32k-0314",
+                        "gpt-4-32k-v0314",
+                    ],
+                },
+                file_location=KEY_LOC,
+            )
+
+            llm_config = {
+                "config_list": config_list,
+                "stream": True,
+            }
+
+            agent = autogen.ConversableAgent(
+                name="chatbot",
+                system_message="Complete a task given to you and reply TERMINATE when the task is done.",
+                llm_config=llm_config,
+            )
+
+            # create a UserProxyAgent instance named "user_proxy"
+            user_proxy = autogen.UserProxyAgent(
+                name="user_proxy",
+                system_message="A proxy for the user.",
+                is_termination_msg=lambda x: x.get("content", "")
+                and x.get("content", "").rstrip().endswith("TERMINATE"),
+                human_input_mode="NEVER",
+                max_consecutive_auto_reply=10,
+            )
+
+            # we will use a temporary directory as the cache path root to ensure fresh completion each time
+            with TemporaryDirectory() as cache_path_root:
+                with Cache.disk(cache_path_root=cache_path_root) as cache:
+                    print(
+                        f" - on_connect(): Initiating chat with agent {agent} using message '{initial_msg}'",
+                        flush=True,
+                    )
+                    user_proxy.initiate_chat(  # noqa: F704
+                        agent,
+                        message=initial_msg,
+                        cache=cache,
+                    )
+
+            success_dict["success"] = True
+
+            return
+
+        with IOWebsockets.run_server_in_thread(on_connect=on_connect, port=8765) as uri:
+            print(f" - test_setup() with websocket server running on {uri}.", flush=True)
+
+            with ws_connect(uri) as websocket:
+                print(f" - Connected to server on {uri}", flush=True)
+
+                print(" - Sending message to server.", flush=True)
+                # websocket.send("2+2=?")
+                websocket.send("Please write a poem about spring in a city of your choice.")
+
+                while True:
+                    message = websocket.recv()
+                    message = message.decode("utf-8") if isinstance(message, bytes) else message
+                    # drop the newline character
+                    if message.endswith("\n"):
+                        message = message[:-1]
+
+                    print(message, end="", flush=True)
+
+                    if "TERMINATE" in message:
+                        print()
+                        print(" - Received TERMINATE message. Exiting.", flush=True)
+                        break
+
+        assert success_dict["success"]
+        print("Test passed.", flush=True)

From 9d03b98712ae1efd55ba5f4563bd4731d44ff9cc Mon Sep 17 00:00:00 2001
From: Rajan <rajan.chari@yahoo.com>
Date: Wed, 27 Mar 2024 18:14:39 -0400
Subject: [PATCH 12/27] [CAP] [Feature] Get list of actors from directory
 service. (#2073)

* Search directory for list of actors using regex '.*' gets all actors

* docs changes

* pre-commit fixes

* Use ActorInfo from protobuf

* pre-commit

* Added zmq tests to work on removing sleeps

* minor refactor of zmq tests

* 1) Change DirSvr to user Broker.  2) Add req-router to broker 3) In ActorConnector use handshake and req/resp to remove sleep

* 1) Change DirSvr to user Broker.  2) Add req-router to broker 3) In ActorConnector use handshake and req/resp to remove sleep

* move socket creation to thread with recv

* move socket creation to thread with recv

* Better logging for DirectorySvc

* better logging for directory svc

* Use logging config

* Start removing sleeps

* pre-commit

* Cleanup monitor socket
---
 samples/apps/cap/README.md                    |   5 +-
 .../apps/cap/py/autogencap/ActorConnector.py  |  47 ++++--
 samples/apps/cap/py/autogencap/Broker.py      |  35 ++++-
 samples/apps/cap/py/autogencap/Config.py      |   2 +
 samples/apps/cap/py/autogencap/DebugLog.py    |  12 +-
 .../apps/cap/py/autogencap/DirectorySvc.py    |  98 ++++++------
 .../cap/py/autogencap/LocalActorNetwork.py    |  14 +-
 samples/apps/cap/py/demo/App.py               |  11 +-
 samples/apps/cap/py/demo/SimpleActorDemo.py   |   2 -
 samples/apps/cap/py/demo/list_agents.py       |  29 ++++
 samples/apps/cap/py/demo/zmq_tests.py         | 145 ++++++++++++++++++
 11 files changed, 319 insertions(+), 81 deletions(-)
 create mode 100644 samples/apps/cap/py/demo/list_agents.py
 create mode 100644 samples/apps/cap/py/demo/zmq_tests.py

diff --git a/samples/apps/cap/README.md b/samples/apps/cap/README.md
index db93821203e..7fe7a6c7469 100644
--- a/samples/apps/cap/README.md
+++ b/samples/apps/cap/README.md
@@ -49,6 +49,7 @@ Actors can register themselves with CAP, find other agents, construct arbitrary
 2) Many CAP Actors interacting with each other
 3) A pair of interacting AutoGen Agents wrapped in CAP Actors
 4) CAP wrapped AutoGen Agents in a group chat
-
+5) Two AutoGen Agents running in different processes and communicating through CAP
+6) List all registered agents in CAP
 ### Coming soon. Stay tuned! ###
-1) Two AutoGen Agents running in different processes and communicating through CAP
+1) AutoGen integration to list all registered agents
diff --git a/samples/apps/cap/py/autogencap/ActorConnector.py b/samples/apps/cap/py/autogencap/ActorConnector.py
index f48f9f8e84b..cbd55a1da0a 100644
--- a/samples/apps/cap/py/autogencap/ActorConnector.py
+++ b/samples/apps/cap/py/autogencap/ActorConnector.py
@@ -2,27 +2,57 @@
 # socket that can publish to that topic. It exposes this functionality
 # using send_msg method
 import zmq
+from zmq.utils.monitor import recv_monitor_message
 import time
 import uuid
 from .DebugLog import Debug, Error
-from .Config import xsub_url, xpub_url
+from .Config import xsub_url, xpub_url, router_url
+from typing import Any, Dict
 
 
 class ActorConnector:
     def __init__(self, context, topic):
-        self._pub_socket = context.socket(zmq.PUB)
-        self._pub_socket.setsockopt(zmq.LINGER, 0)
-        self._pub_socket.connect(xsub_url)
+        self._context = context
 
-        self._resp_socket = context.socket(zmq.SUB)
+        self._resp_socket = self._context.socket(zmq.SUB)
         self._resp_socket.setsockopt(zmq.LINGER, 0)
-        self._resp_socket.setsockopt(zmq.RCVTIMEO, 10000)
+        self._resp_socket.setsockopt(zmq.RCVTIMEO, 250)
         self._resp_socket.connect(xpub_url)
         self._resp_topic = str(uuid.uuid4())
         Debug("AgentConnector", f"subscribe to: {self._resp_topic}")
         self._resp_socket.setsockopt_string(zmq.SUBSCRIBE, f"{self._resp_topic}")
         self._topic = topic
-        time.sleep(0.01)  # Let the network do things.
+
+        self._connect_pub_socket()
+
+    def _send_recv_router_msg(self):
+        # Send a request to the router and wait for a response
+        req_socket = self._context.socket(zmq.REQ)
+        req_socket.connect(router_url)
+        try:
+            Debug("ActorConnector", "Broker Check Request Sent")
+            req_socket.send_string("Request")
+            _ = req_socket.recv_string()
+            Debug("ActorConnector", "Broker Check Response Received")
+        finally:
+            req_socket.close()
+
+    def _connect_pub_socket(self):
+        self._pub_socket = self._context.socket(zmq.PUB)
+        self._pub_socket.setsockopt(zmq.LINGER, 0)
+        monitor = self._pub_socket.get_monitor_socket()
+        self._pub_socket.connect(xsub_url)
+        # Monitor handshake on the pub socket
+        while monitor.poll():
+            evt: Dict[str, Any] = {}
+            mon_evt = recv_monitor_message(monitor)
+            evt.update(mon_evt)
+            if evt["event"] == zmq.EVENT_MONITOR_STOPPED or evt["event"] == zmq.EVENT_HANDSHAKE_SUCCEEDED:
+                Debug("ActorConnector", "Handshake received (Or Monitor stopped)")
+                break
+        self._pub_socket.disable_monitor()
+        monitor.close()
+        self._send_recv_router_msg()
 
     def send_txt_msg(self, msg):
         self._pub_socket.send_multipart(
@@ -35,14 +65,11 @@ def send_bin_msg(self, msg_type: str, msg):
         )
 
     def binary_request(self, msg_type: str, msg, retry=5):
-        time.sleep(0.5)  # Let the network do things.
         self._pub_socket.send_multipart(
             [self._topic.encode("utf8"), msg_type.encode("utf8"), self._resp_topic.encode("utf8"), msg]
         )
-        time.sleep(0.5)  # Let the network do things.
         for i in range(retry + 1):
             try:
-                self._resp_socket.setsockopt(zmq.RCVTIMEO, 10000)
                 resp_topic, resp_msg_type, resp_sender_topic, resp = self._resp_socket.recv_multipart()
                 return resp_topic, resp_msg_type, resp_sender_topic, resp
             except zmq.Again:
diff --git a/samples/apps/cap/py/autogencap/Broker.py b/samples/apps/cap/py/autogencap/Broker.py
index 0bb844d2a04..8f276519335 100644
--- a/samples/apps/cap/py/autogencap/Broker.py
+++ b/samples/apps/cap/py/autogencap/Broker.py
@@ -2,7 +2,7 @@
 import zmq
 import threading
 from autogencap.DebugLog import Debug, Info, Warn
-from autogencap.Config import xsub_url, xpub_url
+from autogencap.Config import xsub_url, xpub_url, router_url
 
 
 class Broker:
@@ -11,19 +11,23 @@ def __init__(self, context: zmq.Context = zmq.Context()):
         self._run: bool = False
         self._xpub: zmq.Socket = None
         self._xsub: zmq.Socket = None
+        self._router: zmq.Socket = None
 
-    def start(self) -> bool:
+    def _init_sockets(self):
         try:
             # XPUB setup
             self._xpub = self._context.socket(zmq.XPUB)
             self._xpub.setsockopt(zmq.LINGER, 0)
             self._xpub.bind(xpub_url)
-
             # XSUB setup
             self._xsub = self._context.socket(zmq.XSUB)
             self._xsub.setsockopt(zmq.LINGER, 0)
             self._xsub.bind(xsub_url)
-
+            # ROUTER setup
+            self._router = self._context.socket(zmq.ROUTER)
+            self._router.setsockopt(zmq.LINGER, 0)
+            self._router.bind(router_url)
+            return True
         except zmq.ZMQError as e:
             Debug("BROKER", f"Unable to start.  Check details: {e}")
             # If binding fails, close the sockets and return False
@@ -31,8 +35,12 @@ def start(self) -> bool:
                 self._xpub.close()
             if self._xsub:
                 self._xsub.close()
+            if self._router:
+                self._router.close()
             return False
 
+    def start(self) -> bool:
+        Debug("BROKER", "Trying to start broker.")
         self._run = True
         self._broker_thread: threading.Thread = threading.Thread(target=self.thread_fn)
         self._broker_thread.start()
@@ -40,6 +48,8 @@ def start(self) -> bool:
         return True
 
     def stop(self):
+        if not self._run:
+            return
         # Error("BROKER_ERR", "fix cleanup self._context.term()")
         Debug("BROKER", "stopped")
         self._run = False
@@ -48,15 +58,24 @@ def stop(self):
             self._xpub.close()
         if self._xsub:
             self._xsub.close()
+        if self._router:
+            self._router.close()
         # self._context.term()
 
     def thread_fn(self):
         try:
+            if not self._init_sockets():
+                Debug("BROKER", "Receive thread not started since sockets were not initialized")
+                self._run = False
+                return
+
             # Poll sockets for events
             self._poller: zmq.Poller = zmq.Poller()
             self._poller.register(self._xpub, zmq.POLLIN)
             self._poller.register(self._xsub, zmq.POLLIN)
+            self._poller.register(self._router, zmq.POLLIN)
 
+            Info("BROKER", "Started.  Waiting for events")
             # Receive msgs, forward and process
             while self._run:
                 events = dict(self._poller.poll(500))
@@ -70,6 +89,14 @@ def thread_fn(self):
                     Debug("BROKER", f"publishing message: {message}")
                     self._xpub.send_multipart(message)
 
+                if self._router in events:
+                    message = self._router.recv_multipart()
+                    Debug("BROKER", f"router message: {message}")
+                    # Mirror it back for now to confirm connectivity
+                    # More interesting reserved point to point
+                    # routing coming in the the future
+                    self._router.send_multipart(message)
+
         except Exception as e:
             Debug("BROKER", f"thread encountered an error: {e}")
         finally:
diff --git a/samples/apps/cap/py/autogencap/Config.py b/samples/apps/cap/py/autogencap/Config.py
index 299e4e273b8..5584a8d29cb 100644
--- a/samples/apps/cap/py/autogencap/Config.py
+++ b/samples/apps/cap/py/autogencap/Config.py
@@ -3,3 +3,5 @@
 IGNORED_LOG_CONTEXTS = []
 xpub_url: str = "tcp://127.0.0.1:5555"
 xsub_url: str = "tcp://127.0.0.1:5556"
+router_url: str = "tcp://127.0.0.1:5557"
+dealer_url: str = "tcp://127.0.0.1:5558"
diff --git a/samples/apps/cap/py/autogencap/DebugLog.py b/samples/apps/cap/py/autogencap/DebugLog.py
index b31512f7ffa..b86a497c658 100644
--- a/samples/apps/cap/py/autogencap/DebugLog.py
+++ b/samples/apps/cap/py/autogencap/DebugLog.py
@@ -1,6 +1,6 @@
 import threading
 import datetime
-from autogencap.Config import LOG_LEVEL, IGNORED_LOG_CONTEXTS
+import autogencap.Config as Config
 from termcolor import colored
 
 # Define log levels as constants
@@ -18,19 +18,21 @@
 
 def Log(level, context, msg):
     # Check if the current level meets the threshold
-    if level >= LOG_LEVEL:  # Use the LOG_LEVEL from the Config module
+    if level >= Config.LOG_LEVEL:  # Use the LOG_LEVEL from the Config module
         # Check if the context is in the list of ignored contexts
-        if context in IGNORED_LOG_CONTEXTS:
+        if context in Config.IGNORED_LOG_CONTEXTS:
             return
         with console_lock:
             timestamp = colored(datetime.datetime.now().strftime("%m/%d/%y %H:%M:%S"), "dark_grey")
             # Translate level number to name and color
             level_name = colored(LEVEL_NAMES[level], LEVEL_COLOR[level])
-            # Center justify the context and color it blue
+            # Left justify the context and color it blue
             context = colored(context.ljust(14), "blue")
+            # Left justify the threadid and color it blue
+            thread_id = colored(str(threading.get_ident()).ljust(5), "blue")
             # color the msg based on the level
             msg = colored(msg, LEVEL_COLOR[level])
-            print(f"{threading.get_ident()} {timestamp} {level_name}: [{context}] {msg}")
+            print(f"{thread_id} {timestamp} {level_name}: [{context}] {msg}")
 
 
 def Debug(context, message):
diff --git a/samples/apps/cap/py/autogencap/DirectorySvc.py b/samples/apps/cap/py/autogencap/DirectorySvc.py
index 21d01c71207..87833c91ae3 100644
--- a/samples/apps/cap/py/autogencap/DirectorySvc.py
+++ b/samples/apps/cap/py/autogencap/DirectorySvc.py
@@ -1,12 +1,22 @@
 from autogencap.Constants import Directory_Svc_Topic
-from autogencap.Config import xpub_url, xsub_url
+from autogencap.Config import xpub_url, xsub_url, router_url
 from autogencap.DebugLog import Debug, Info, Error
 from autogencap.ActorConnector import ActorConnector
 from autogencap.Actor import Actor
-from autogencap.proto.CAP_pb2 import ActorRegistration, ActorInfo, ActorLookup, ActorLookupResponse, Ping, Pong
+from autogencap.Broker import Broker
+from autogencap.proto.CAP_pb2 import (
+    ActorRegistration,
+    ActorInfo,
+    ActorLookup,
+    ActorLookupResponse,
+    Ping,
+    Pong,
+    ActorInfoCollection,
+)
 import zmq
 import threading
 import time
+import re
 
 # TODO (Future DirectorySv PR) use actor description, network_id, other properties to make directory
 # service more generic and powerful
@@ -52,18 +62,27 @@ def _actor_lookup_msg_handler(self, topic: str, msg_type: str, msg: bytes, sende
         actor_lookup = ActorLookup()
         actor_lookup.ParseFromString(msg)
         Debug("DirectorySvc", f"Actor lookup: {actor_lookup.actor_info.name}")
-        actor: ActorInfo = None
-        if actor_lookup.actor_info.name in self._registered_actors:
-            Info("DirectorySvc", f"Actor found: {actor_lookup.actor_info.name}")
-            actor = self._registered_actors[actor_lookup.actor_info.name]
-        else:
-            Error("DirectorySvc", f"Actor not found: {actor_lookup.actor_info.name}")
         actor_lookup_resp = ActorLookupResponse()
-        if actor is not None:
-            actor_lookup_resp.actor.info_coll.extend([actor])
+        actor_lookup_resp.found = False
+        try:
+            pattern = re.compile(actor_lookup.actor_info.name)
+        except re.error:
+            Error("DirectorySvc", f"Invalid regex pattern: {actor_lookup.actor_info.name}")
+        else:
+            found_actor_list = [
+                self._registered_actors[registered_actor]
+                for registered_actor in self._registered_actors
+                if pattern.match(registered_actor)
+            ]
+
+        if found_actor_list:
+            for actor in found_actor_list:
+                Info("DirectorySvc", f"Actor found: {actor.name}")
             actor_lookup_resp.found = True
+            actor_lookup_resp.actor.info_coll.extend(found_actor_list)
         else:
-            actor_lookup_resp.found = False
+            Error("DirectorySvc", f"Actor not found: {actor_lookup.actor_info.name}")
+
         sender_connection = ActorConnector(self._context, sender_topic)
         serialized_msg = actor_lookup_resp.SerializeToString()
         sender_connection.send_bin_msg(ActorLookupResponse.__name__, serialized_msg)
@@ -76,6 +95,7 @@ def __init__(self, context: zmq.Context = zmq.Context()):
         self._directory_actor: DirectoryActor = None
 
     def _no_other_directory(self) -> bool:
+        Debug("DirectorySvc", "Pinging existing DirectorySvc")
         ping = Ping()
         serialized_msg = ping.SerializeToString()
         _, _, _, resp = self._directory_connector.binary_request(Ping.__name__, serialized_msg, retry=0)
@@ -110,69 +130,39 @@ def register_actor_by_name(self, actor_name: str):
         actor_info = ActorInfo(name=actor_name)
         self.register_actor(actor_info)
 
-    def lookup_actor_by_name(self, actor_name: str) -> ActorInfo:
-        actor_info = ActorInfo(name=actor_name)
+    def _lookup_actors_by_name(self, name_regex: str):
+        actor_info = ActorInfo(name=name_regex)
         actor_lookup = ActorLookup(actor_info=actor_info)
         serialized_msg = actor_lookup.SerializeToString()
         _, _, _, resp = self._directory_connector.binary_request(ActorLookup.__name__, serialized_msg)
         actor_lookup_resp = ActorLookupResponse()
         actor_lookup_resp.ParseFromString(resp)
+        return actor_lookup_resp
+
+    def lookup_actor_by_name(self, actor_name: str) -> ActorInfo:
+        actor_lookup_resp = self._lookup_actors_by_name(actor_name)
         if actor_lookup_resp.found:
             if len(actor_lookup_resp.actor.info_coll) > 0:
                 return actor_lookup_resp.actor.info_coll[0]
         return None
 
-
-# Standalone min proxy for a standalone directory service
-class MinProxy:
-    def __init__(self, context: zmq.Context):
-        self._context: zmq.Context = context
-        self._xpub: zmq.Socket = None
-        self._xsub: zmq.Socket = None
-
-    def start(self):
-        # Start the proxy thread
-        proxy_thread = threading.Thread(target=self.proxy_thread_fn)
-        proxy_thread.start()
-        time.sleep(0.01)
-
-    def stop(self):
-        self._xsub.setsockopt(zmq.LINGER, 0)
-        self._xpub.setsockopt(zmq.LINGER, 0)
-        self._xpub.close()
-        self._xsub.close()
-        time.sleep(0.01)
-
-    def proxy_thread_fn(self):
-        self._xpub: zmq.Socket = self._context.socket(zmq.XPUB)
-        self._xsub: zmq.Socket = self._context.socket(zmq.XSUB)
-        try:
-            self._xpub.bind(xpub_url)
-            self._xsub.bind(xsub_url)
-            zmq.proxy(self._xpub, self._xsub)
-        except zmq.ContextTerminated:
-            self._xpub.close()
-            self._xsub.close()
-        except Exception as e:
-            Error("proxy_thread_fn", f"proxy_thread_fn encountered an error: {e}")
-            self._xpub.setsockopt(zmq.LINGER, 0)
-            self._xsub.setsockopt(zmq.LINGER, 0)
-            self._xpub.close()
-            self._xsub.close()
-        finally:
-            Info("proxy_thread_fn", "proxy_thread_fn terminated.")
+    def lookup_actor_info_by_name(self, actor_name: str) -> ActorInfoCollection:
+        actor_lookup_resp = self._lookup_actors_by_name(actor_name)
+        if actor_lookup_resp.found:
+            if len(actor_lookup_resp.actor.info_coll) > 0:
+                return actor_lookup_resp.actor
+        return None
 
 
 # Run a standalone directory service
 def main():
     context: zmq.Context = zmq.Context()
     # Start simple broker (will exit if real broker is running)
-    proxy: MinProxy = MinProxy(context)
+    proxy: Broker = Broker(context)
     proxy.start()
     # Start the directory service
     directory_svc = DirectorySvc(context)
     directory_svc.start()
-
     # # How do you register an actor?
     # directory_svc.register_actor_by_name("my_actor")
     #
diff --git a/samples/apps/cap/py/autogencap/LocalActorNetwork.py b/samples/apps/cap/py/autogencap/LocalActorNetwork.py
index d1133bdb7e7..685972119ff 100644
--- a/samples/apps/cap/py/autogencap/LocalActorNetwork.py
+++ b/samples/apps/cap/py/autogencap/LocalActorNetwork.py
@@ -5,7 +5,8 @@
 from .DirectorySvc import DirectorySvc
 from .Constants import Termination_Topic
 from .Actor import Actor
-from .proto.CAP_pb2 import ActorInfo
+from .proto.CAP_pb2 import ActorInfo, ActorInfoCollection
+from typing import List
 import time
 
 # TODO: remove time import
@@ -69,3 +70,14 @@ def lookup_actor(self, name: str) -> ActorConnector:
     def lookup_termination(self) -> ActorConnector:
         termination_topic: str = Termination_Topic
         return self.actor_connector_by_topic(termination_topic)
+
+    def lookup_actor_info(self, name_regex) -> List[ActorInfo]:
+        actor_info: ActorInfoCollection = self._directory_svc.lookup_actor_info_by_name(name_regex)
+        if actor_info is None:
+            Warn("Local_Actor_Network", f"{name_regex}, not found in the network.")
+            return None
+        Debug("Local_Actor_Network", f"[{name_regex}] found in the network.")
+        actor_list = []
+        for actor in actor_info.info_coll:
+            actor_list.append(actor)
+        return actor_list
diff --git a/samples/apps/cap/py/demo/App.py b/samples/apps/cap/py/demo/App.py
index e9a49ba4a26..a952a27c311 100644
--- a/samples/apps/cap/py/demo/App.py
+++ b/samples/apps/cap/py/demo/App.py
@@ -4,7 +4,7 @@
 
 import argparse
 import _paths
-from autogencap.Config import LOG_LEVEL, IGNORED_LOG_CONTEXTS
+import autogencap.Config as Config
 import autogencap.DebugLog as DebugLog
 from SimpleActorDemo import simple_actor_demo
 from AGDemo import ag_demo
@@ -13,6 +13,7 @@
 from CAPAutoGenPairDemo import cap_ag_pair_demo
 from ComplexActorDemo import complex_actor_demo
 from RemoteAGDemo import remote_ag_demo
+from list_agents import list_agents
 
 ####################################################################################################
 
@@ -26,7 +27,8 @@ def parse_args():
     # Set the log level
     # Print log level string based on names in debug_log.py
     print(f"Log level: {DebugLog.LEVEL_NAMES[args.log_level]}")
-    # IGNORED_LOG_CONTEXTS.extend(["BROKER"])
+    Config.LOG_LEVEL = args.log_level
+    # Config.IGNORED_LOG_CONTEXTS.extend(["BROKER"])
 
 
 ####################################################################################################
@@ -42,7 +44,8 @@ def main():
         print("3. AutoGen Pair")
         print("4. AutoGen GroupChat")
         print("5. AutoGen Agents in different processes")
-        choice = input("Enter your choice (1-5): ")
+        print("6. List Actors in CAP")
+        choice = input("Enter your choice (1-6): ")
 
         if choice == "1":
             simple_actor_demo()
@@ -58,6 +61,8 @@ def main():
             cap_ag_group_demo()
         elif choice == "5":
             remote_ag_demo()
+        elif choice == "6":
+            list_agents()
         else:
             print("Quitting...")
             break
diff --git a/samples/apps/cap/py/demo/SimpleActorDemo.py b/samples/apps/cap/py/demo/SimpleActorDemo.py
index 26a67814d31..a7031a4bbe0 100644
--- a/samples/apps/cap/py/demo/SimpleActorDemo.py
+++ b/samples/apps/cap/py/demo/SimpleActorDemo.py
@@ -17,10 +17,8 @@ def simple_actor_demo():
     network.connect()
     # Get a channel to the actor
     greeter_link = network.lookup_actor("Greeter")
-    time.sleep(1)
     # Send a message to the actor
     greeter_link.send_txt_msg("Hello World!")
-    time.sleep(1)
     # Cleanup
     greeter_link.close()
     network.disconnect()
diff --git a/samples/apps/cap/py/demo/list_agents.py b/samples/apps/cap/py/demo/list_agents.py
new file mode 100644
index 00000000000..2535678e047
--- /dev/null
+++ b/samples/apps/cap/py/demo/list_agents.py
@@ -0,0 +1,29 @@
+import time
+from typing import List
+from AppAgents import GreeterAgent, FidelityAgent
+from autogencap.LocalActorNetwork import LocalActorNetwork
+from autogencap.proto.CAP_pb2 import ActorInfo
+
+
+def list_agents():
+    """
+    Demonstrates the usage of the CAP platform by registering an actor, connecting to the actor,
+    sending a message, and performing cleanup operations.
+    """
+    # CAP Platform
+
+    network = LocalActorNetwork()
+    # Register an actor
+    network.register(GreeterAgent())
+    # Register an actor
+    network.register(FidelityAgent())
+    # Tell actor to connect to other actors
+    network.connect()
+    # Get a list of actors
+    actor_infos: List[ActorInfo] = network.lookup_actor_info(name_regex=".*")
+    # Print out all actors found
+    for actor_info in actor_infos:
+        print(f"Name: {actor_info.name}, Namespace: {actor_info.namespace}, Description: {actor_info.description}")
+    time.sleep(1)
+    # Cleanup
+    network.disconnect()
diff --git a/samples/apps/cap/py/demo/zmq_tests.py b/samples/apps/cap/py/demo/zmq_tests.py
new file mode 100644
index 00000000000..b53adb00c48
--- /dev/null
+++ b/samples/apps/cap/py/demo/zmq_tests.py
@@ -0,0 +1,145 @@
+import _paths
+import sys
+import time
+from typing import Any, Dict
+import zmq
+from zmq.utils.monitor import recv_monitor_message
+from autogencap.Config import xsub_url, xpub_url, router_url, dealer_url
+
+
+def zmq_sub_test():
+    context = zmq.Context()
+    sub_socket = context.socket(zmq.SUB)
+    sub_socket.setsockopt(zmq.LINGER, 0)
+    sub_socket.setsockopt(zmq.RCVTIMEO, 10000)
+    sub_socket.connect(xpub_url)
+    sub_socket.setsockopt_string(zmq.SUBSCRIBE, "")
+    start_time = time.time()
+    while True:
+        try:
+            msg = sub_socket.recv_string()
+            print(f"Received: {msg}")
+            start_time = time.time()
+        except KeyboardInterrupt:
+            print("Interrupted by user. Exiting...")
+            break
+        except zmq.Again:
+            elapsed_time = time.time() - start_time
+            if elapsed_time > 60000:  # in seconds
+                break
+            print(f"No message received in {elapsed_time:.2f} seconds")
+    sub_socket.close()
+
+
+def event_monitor(pub_socket: zmq.Socket) -> None:
+    monitor = pub_socket.get_monitor_socket()
+    while monitor.poll():
+        evt: Dict[str, Any] = {}
+        mon_evt = recv_monitor_message(monitor)
+        evt.update(mon_evt)
+        print(evt)
+        if evt["event"] == zmq.EVENT_MONITOR_STOPPED or evt["event"] == zmq.EVENT_HANDSHAKE_SUCCEEDED:
+            break
+    monitor.close()
+
+
+def zmq_pub_test():
+    context = zmq.Context()
+    pub_socket = context.socket(zmq.PUB)
+    pub_socket.setsockopt(zmq.XPUB_VERBOSE, 1)
+    pub_socket.setsockopt(zmq.LINGER, 0)
+    pub_socket.connect(xsub_url)
+    event_monitor(pub_socket)
+    zmq_req_test(context)
+    for i in range(1, 11):
+        pub_socket.send_string(str(i))
+    pub_socket.close()
+
+
+def zmq_router_dealer_test():
+    context = zmq.Context()
+    router_socket = context.socket(zmq.ROUTER)
+    router_socket.bind(router_url)
+    dealer_socket = context.socket(zmq.DEALER)
+    dealer_socket.bind(dealer_url)
+    try:
+        # Poll sockets for events
+        poller: zmq.Poller = zmq.Poller()
+        poller.register(router_socket, zmq.POLLIN)
+        poller.register(dealer_socket, zmq.POLLIN)
+
+        print("Running...")
+        # Receive msgs, forward and process
+        start_time = time.time()
+        last_time = start_time
+        while True:
+            events = dict(poller.poll(500))
+            since_last_time = time.time() - last_time
+            if since_last_time > 60:  # in seconds
+                elapsed_time = time.time() - start_time
+                print(f"Elapsed time: {elapsed_time:.2f} seconds")
+                last_time = time.time()
+
+            if router_socket in events:
+                message = router_socket.recv_multipart()
+                print("BROKER", f"subscription message: {message[0]}")
+                dealer_socket.send_multipart(message)
+
+            if dealer_socket in events:
+                message = dealer_socket.recv_multipart()
+                print("BROKER", f"publishing message: {message[0]}")
+                router_socket.send_multipart(message)
+
+    except Exception as e:
+        print("BROKER", f"thread encountered an error: {e}")
+    finally:
+        print("BROKER", "thread ended")
+    return
+
+
+def zmq_req_test(context: zmq.Context = None):
+    if context is None:
+        context = zmq.Context()
+    req_socket = context.socket(zmq.REQ)
+    req_socket.connect(router_url)
+    try:
+        req_socket.send_string("Request ")
+        message = req_socket.recv_string()
+        print(f"Received: {message}")
+    except KeyboardInterrupt:
+        print("Interrupted by user. Exiting...")
+    finally:
+        req_socket.close()
+
+
+def zmq_rep_test():
+    context = zmq.Context()
+    rep_socket = context.socket(zmq.REP)
+    rep_socket.connect(dealer_url)
+    try:
+        while True:
+            message = rep_socket.recv_string()
+            print(f"Received: {message}")
+            rep_socket.send_string("Acknowledged: " + message)
+    except KeyboardInterrupt:
+        print("Interrupted by user. Exiting...")
+    finally:
+        rep_socket.close()
+
+
+if __name__ == "__main__":
+    if len(sys.argv) > 1:
+        if sys.argv[1] == "pub":
+            zmq_pub_test()
+        elif sys.argv[1] == "sub":
+            zmq_sub_test()
+        elif sys.argv[1] == "router":
+            zmq_router_dealer_test()
+        elif sys.argv[1] == "req":
+            zmq_req_test()
+        elif sys.argv[1] == "rep":
+            zmq_rep_test()
+        else:
+            print("Invalid argument. Please use 'pub', 'sub' 'router', 'req', 'rep'")
+    else:
+        print("Please provide an argument. Please use 'pub', 'sub' 'router', 'req', 'rep'")

From 033fc28462b9070acf74185007424356f0f9bf1c Mon Sep 17 00:00:00 2001
From: Jack Gerrits <jackgerrits@users.noreply.github.com>
Date: Wed, 27 Mar 2024 18:15:24 -0400
Subject: [PATCH 13/27] Mark cache as a protocol and update type hints to
 reflect (#2168)

* Mark cache as a protocl and update type hints to reflect

* int

* undo init change

	modified:   autogen/agentchat/chat.py
---
 autogen/agentchat/chat.py                     | 32 ++++++------
 .../contrib/capabilities/generate_images.py   |  6 +--
 autogen/agentchat/conversable_agent.py        | 14 ++---
 autogen/cache/__init__.py                     |  4 +-
 autogen/cache/abstract_cache_base.py          | 51 ++++---------------
 autogen/cache/cache.py                        |  2 +-
 autogen/oai/client.py                         |  4 +-
 test/agentchat/contrib/test_web_surfer.py     |  1 -
 8 files changed, 41 insertions(+), 73 deletions(-)

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index 6c39160444c..0fc321b4379 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -149,27 +149,27 @@ def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
         Each dictionary should contain the input arguments for [`ConversableAgent.initiate_chat`]
         (/docs/reference/agentchat/conversable_agent#initiate_chat).
         For example:
-            - "sender": the sender agent.
-            - "recipient": the recipient agent.
-            - "clear_history" (bool): whether to clear the chat history with the agent.
+            - `"sender"` - the sender agent.
+            - `"recipient"` - the recipient agent.
+            - `"clear_history"  (bool) - whether to clear the chat history with the agent.
                Default is True.
-            - "silent" (bool or None): (Experimental) whether to print the messages in this
+            - `"silent"` (bool or None) - (Experimental) whether to print the messages in this
                conversation. Default is False.
-            - "cache" (Cache or None): the cache client to use for this conversation.
+            - `"cache"` (Cache or None) - the cache client to use for this conversation.
                Default is None.
-            - "max_turns" (int or None): maximum number of turns for the chat. If None, the chat
+            - `"max_turns"` (int or None) - maximum number of turns for the chat. If None, the chat
                will continue until a termination condition is met. Default is None.
-            - "summary_method" (str or callable): a string or callable specifying the method to get
+            - `"summary_method"` (str or callable) - a string or callable specifying the method to get
                a summary from the chat. Default is DEFAULT_summary_method, i.e., "last_msg".
-            - "summary_args" (dict): a dictionary of arguments to be passed to the summary_method.
+            - `"summary_args"` (dict) - a dictionary of arguments to be passed to the summary_method.
                Default is {}.
-            - "message" (str, callable or None): if None, input() will be called to get the
+            - `"message"` (str, callable or None) - if None, input() will be called to get the
                initial message.
-            - **context: additional context information to be passed to the chat.
-                - "carryover": It can be used to specify the carryover information to be passed
-                   to this chat.
-                   If provided, we will combine this carryover with the "message" content when
-                   generating the initial chat message in `generate_init_message`.
+            - `**context` - additional context information to be passed to the chat.
+            - `"carryover"` - It can be used to specify the carryover information to be passed
+                to this chat.
+                If provided, we will combine this carryover with the "message" content when
+                generating the initial chat message in `generate_init_message`.
 
     Returns:
         (list): a list of ChatResult objects corresponding to the finished chats in the chat_queue.
@@ -239,11 +239,11 @@ async def a_initiate_chats(chat_queue: List[Dict[str, Any]]) -> Dict[int, ChatRe
     """(async) Initiate a list of chats.
 
     args:
-        Please refer to `initiate_chats`.
+        - Please refer to `initiate_chats`.
 
 
     returns:
-        (Dict): a dict of ChatId: ChatResult corresponding to the finished chats in the chat_queue.
+        - (Dict): a dict of ChatId: ChatResult corresponding to the finished chats in the chat_queue.
     """
     consolidate_chat_info(chat_queue)
     _validate_recipients(chat_queue)
diff --git a/autogen/agentchat/contrib/capabilities/generate_images.py b/autogen/agentchat/contrib/capabilities/generate_images.py
index 778e7256558..d16121ddb9a 100644
--- a/autogen/agentchat/contrib/capabilities/generate_images.py
+++ b/autogen/agentchat/contrib/capabilities/generate_images.py
@@ -5,7 +5,7 @@
 from PIL.Image import Image
 
 from autogen import Agent, ConversableAgent, code_utils
-from autogen.cache import Cache
+from autogen.cache import AbstractCache
 from autogen.agentchat.contrib import img_utils
 from autogen.agentchat.contrib.capabilities.agent_capability import AgentCapability
 from autogen.agentchat.contrib.text_analyzer_agent import TextAnalyzerAgent
@@ -142,7 +142,7 @@ class ImageGeneration(AgentCapability):
     def __init__(
         self,
         image_generator: ImageGenerator,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
         text_analyzer_llm_config: Optional[Dict] = None,
         text_analyzer_instructions: str = PROMPT_INSTRUCTIONS,
         verbosity: int = 0,
@@ -151,7 +151,7 @@ def __init__(
         """
         Args:
             image_generator (ImageGenerator): The image generator you would like to use to generate images.
-            cache (None or Cache): The cache client to use to store and retrieve generated images. If None,
+            cache (None or AbstractCache): The cache client to use to store and retrieve generated images. If None,
                 no caching will be used.
             text_analyzer_llm_config (Dict or None): The LLM config for the text analyzer. If None, the LLM config will
                 be retrieved from the agent you're adding the ability to.
diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index eb9d1c9cbcb..2b051fc0694 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -15,7 +15,7 @@
 from autogen.exception_utils import InvalidCarryOverType, SenderRequired
 
 from .._pydantic import model_dump
-from ..cache.cache import Cache
+from ..cache.cache import AbstractCache
 from ..code_utils import (
     UNKNOWN,
     check_can_use_docker_or_throw,
@@ -865,7 +865,7 @@ def initiate_chat(
         recipient: "ConversableAgent",
         clear_history: bool = True,
         silent: Optional[bool] = False,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
         max_turns: Optional[int] = None,
         summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
         summary_args: Optional[dict] = {},
@@ -882,7 +882,7 @@ def initiate_chat(
             recipient: the recipient agent.
             clear_history (bool): whether to clear the chat history with the agent. Default is True.
             silent (bool or None): (Experimental) whether to print the messages for this conversation. Default is False.
-            cache (Cache or None): the cache client to be used for this conversation. Default is None.
+            cache (AbstractCache or None): the cache client to be used for this conversation. Default is None.
             max_turns (int or None): the maximum number of turns for the chat between the two agents. One turn means one conversation round trip. Note that this is different from
                 [max_consecutive_auto_reply](#max_consecutive_auto_reply) which is the maximum number of consecutive auto replies; and it is also different from [max_rounds in GroupChat](./groupchat#groupchat-objects) which is the maximum number of rounds in a group chat session.
                 If max_turns is set to None, the chat will continue until a termination condition is met. Default is None.
@@ -1007,7 +1007,7 @@ async def a_initiate_chat(
         recipient: "ConversableAgent",
         clear_history: bool = True,
         silent: Optional[bool] = False,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
         max_turns: Optional[int] = None,
         summary_method: Optional[Union[str, Callable]] = DEFAULT_SUMMARY_METHOD,
         summary_args: Optional[dict] = {},
@@ -1073,7 +1073,7 @@ def _summarize_chat(
         summary_method,
         summary_args,
         recipient: Optional[Agent] = None,
-        cache: Optional[Cache] = None,
+        cache: Optional[AbstractCache] = None,
     ) -> str:
         """Get a chat summary from an agent participating in a chat.
 
@@ -1141,7 +1141,7 @@ def _relfection_with_llm_as_summary(sender, recipient, summary_args):
         return summary
 
     def _reflection_with_llm(
-        self, prompt, messages, llm_agent: Optional[Agent] = None, cache: Optional[Cache] = None
+        self, prompt, messages, llm_agent: Optional[Agent] = None, cache: Optional[AbstractCache] = None
     ) -> str:
         """Get a chat summary using reflection with an llm client based on the conversation history.
 
@@ -1149,7 +1149,7 @@ def _reflection_with_llm(
             prompt (str): The prompt (in this method it is used as system prompt) used to get the summary.
             messages (list): The messages generated as part of a chat conversation.
             llm_agent: the agent with an llm client.
-            cache (Cache or None): the cache client to be used for this conversation.
+            cache (AbstractCache or None): the cache client to be used for this conversation.
         """
         system_msg = [
             {
diff --git a/autogen/cache/__init__.py b/autogen/cache/__init__.py
index 0eb8cfa71c8..febfa8c7c5d 100644
--- a/autogen/cache/__init__.py
+++ b/autogen/cache/__init__.py
@@ -1,3 +1,3 @@
-from .cache import Cache
+from .cache import Cache, AbstractCache
 
-__all__ = ["Cache"]
+__all__ = ["Cache", "AbstractCache"]
diff --git a/autogen/cache/abstract_cache_base.py b/autogen/cache/abstract_cache_base.py
index 233702e777a..ebf1cecfa40 100644
--- a/autogen/cache/abstract_cache_base.py
+++ b/autogen/cache/abstract_cache_base.py
@@ -1,6 +1,5 @@
-from abc import ABC, abstractmethod
 from types import TracebackType
-from typing import Any, Optional, Type
+from typing import Any, Optional, Protocol, Type
 import sys
 
 if sys.version_info >= (3, 11):
@@ -9,23 +8,17 @@
     from typing_extensions import Self
 
 
-class AbstractCache(ABC):
+class AbstractCache(Protocol):
     """
-    Abstract base class for cache implementations.
-
-    This class defines the basic interface for cache operations.
+    This protocol defines the basic interface for cache operations.
     Implementing classes should provide concrete implementations for
     these methods to handle caching mechanisms.
     """
 
-    @abstractmethod
     def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]:
         """
         Retrieve an item from the cache.
 
-        Abstract method that must be implemented by subclasses to
-        retrieve an item from the cache.
-
         Args:
             key (str): The key identifying the item in the cache.
             default (optional): The default value to return if the key is not found.
@@ -33,53 +26,35 @@ def get(self, key: str, default: Optional[Any] = None) -> Optional[Any]:
 
         Returns:
             The value associated with the key if found, else the default value.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def set(self, key: str, value: Any) -> None:
         """
         Set an item in the cache.
 
-        Abstract method that must be implemented by subclasses to
-        store an item in the cache.
-
         Args:
             key (str): The key under which the item is to be stored.
             value: The value to be stored in the cache.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def close(self) -> None:
         """
-        Close the cache.
-
-        Abstract method that should be implemented by subclasses to
-        perform any necessary cleanup, such as closing network connections or
+        Close the cache. Perform any necessary cleanup, such as closing network connections or
         releasing resources.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def __enter__(self) -> Self:
         """
         Enter the runtime context related to this object.
 
-        The with statement will bind this method’s return value to the target(s)
+        The with statement will bind this method's return value to the target(s)
         specified in the as clause of the statement, if any.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
 
-    @abstractmethod
     def __exit__(
         self,
         exc_type: Optional[Type[BaseException]],
@@ -89,15 +64,9 @@ def __exit__(
         """
         Exit the runtime context and close the cache.
 
-        Abstract method that should be implemented by subclasses to handle
-        the exit from a with statement. It is responsible for resource
-        release and cleanup.
-
         Args:
             exc_type: The exception type if an exception was raised in the context.
             exc_value: The exception value if an exception was raised in the context.
             traceback: The traceback if an exception was raised in the context.
-
-        Raises:
-            NotImplementedError: If the subclass does not implement this method.
         """
+        ...
diff --git a/autogen/cache/cache.py b/autogen/cache/cache.py
index 1cb507ad79a..31bbfa13529 100644
--- a/autogen/cache/cache.py
+++ b/autogen/cache/cache.py
@@ -14,7 +14,7 @@
     from typing_extensions import Self
 
 
-class Cache:
+class Cache(AbstractCache):
     """
     A wrapper class for managing cache configuration and instances.
 
diff --git a/autogen/oai/client.py b/autogen/oai/client.py
index ff7f8d37880..f288ece3961 100644
--- a/autogen/oai/client.py
+++ b/autogen/oai/client.py
@@ -10,7 +10,7 @@
 from pydantic import BaseModel
 from typing import Protocol
 
-from autogen.cache.cache import Cache
+from autogen.cache import Cache
 from autogen.io.base import IOStream
 from autogen.oai.openai_utils import get_key, is_valid_api_key, OAI_PRICE1K
 from autogen.token_count_utils import count_token
@@ -517,7 +517,7 @@ def create(self, **config: Any) -> ModelClient.ModelClientResponseProtocol:
                 The actual prompt will be:
                 "Complete the following sentence: Today I feel".
                 More examples can be found at [templating](/docs/Use-Cases/enhanced_inference#templating).
-            - cache (Cache | None): A Cache object to use for response cache. Default to None.
+            - cache (AbstractCache | None): A Cache object to use for response cache. Default to None.
                 Note that the cache argument overrides the legacy cache_seed argument: if this argument is provided,
                 then the cache_seed argument is ignored. If this argument is not provided or None,
                 then the cache_seed argument is used.
diff --git a/test/agentchat/contrib/test_web_surfer.py b/test/agentchat/contrib/test_web_surfer.py
index 71325fc9c15..e53342fd8e0 100755
--- a/test/agentchat/contrib/test_web_surfer.py
+++ b/test/agentchat/contrib/test_web_surfer.py
@@ -6,7 +6,6 @@
 import pytest
 from autogen import UserProxyAgent, config_list_from_json
 from autogen.oai.openai_utils import filter_config
-from autogen.cache import Cache
 
 sys.path.append(os.path.join(os.path.dirname(__file__), "../.."))
 from conftest import MOCK_OPEN_AI_API_KEY, skip_openai  # noqa: E402

From fd10d9e85b2cf0649e16b7f8a9c76b9fb132ab5e Mon Sep 17 00:00:00 2001
From: calm <1191465097@qq.com>
Date: Thu, 28 Mar 2024 06:38:00 +0800
Subject: [PATCH 14/27] fix(): fix word spelling errors (#2171)

---
 autogen/agentchat/conversable_agent.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/autogen/agentchat/conversable_agent.py b/autogen/agentchat/conversable_agent.py
index 2b051fc0694..20398965c0f 100644
--- a/autogen/agentchat/conversable_agent.py
+++ b/autogen/agentchat/conversable_agent.py
@@ -1101,7 +1101,7 @@ def my_summary_method(
         if "cache" not in summary_args:
             summary_args["cache"] = cache
         if summary_method == "reflection_with_llm":
-            summary_method = self._relfection_with_llm_as_summary
+            summary_method = self._reflection_with_llm_as_summary
         elif summary_method == "last_msg":
             summary_method = self._last_msg_as_summary
 
@@ -1124,7 +1124,7 @@ def _last_msg_as_summary(sender, recipient, summary_args) -> str:
         return summary
 
     @staticmethod
-    def _relfection_with_llm_as_summary(sender, recipient, summary_args):
+    def _reflection_with_llm_as_summary(sender, recipient, summary_args):
         prompt = summary_args.get("summary_prompt")
         prompt = ConversableAgent.DEFAULT_SUMMARY_PROMPT if prompt is None else prompt
         if not isinstance(prompt, str):

From 11f69a58dd65ec56f180840f0add1921407178ef Mon Sep 17 00:00:00 2001
From: Jack Gerrits <jackgerrits@users.noreply.github.com>
Date: Wed, 27 Mar 2024 19:45:17 -0400
Subject: [PATCH 15/27] Implement User Defined Functions for Local CLI Executor
 (#2102)

* Implement user defined functions feature for local cli exec, add docs

* add tests, update docs

* fixes

* fix test

* add pandas test dep

* install test

* provide template as func

* formatting

* undo change

* address comments

* add test deps

* formatting

* test only in 1 env

* formatting

* remove test for local only

---------

Co-authored-by: Eric Zhu <ekzhu@users.noreply.github.com>
---
 .github/workflows/build.yml                   |   4 +-
 autogen/coding/func_with_reqs.py              | 128 +++++
 .../coding/local_commandline_code_executor.py |  97 +++-
 setup.py                                      |   1 +
 test/coding/test_user_defined_functions.py    | 151 ++++++
 .../user-defined-functions.ipynb              | 463 ++++++++++++++++++
 6 files changed, 837 insertions(+), 7 deletions(-)
 create mode 100644 autogen/coding/func_with_reqs.py
 create mode 100644 test/coding/test_user_defined_functions.py
 create mode 100644 website/docs/topics/code-execution/user-defined-functions.ipynb

diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
index 7d5d6e882b0..5be7688b06e 100644
--- a/.github/workflows/build.yml
+++ b/.github/workflows/build.yml
@@ -43,10 +43,10 @@ jobs:
           python -c "import autogen"
           pip install pytest mock
       - name: Install optional dependencies for code executors
-        # code executors auto skip without deps, so only run for python 3.11
+        # code executors and udfs auto skip without deps, so only run for python 3.11
         if: matrix.python-version == '3.11'
         run: |
-          pip install -e ".[jupyter-executor]"
+          pip install -e ".[jupyter-executor,test]"
           python -m ipykernel install --user --name python3
       - name: Set AUTOGEN_USE_DOCKER based on OS
         shell: bash
diff --git a/autogen/coding/func_with_reqs.py b/autogen/coding/func_with_reqs.py
new file mode 100644
index 00000000000..c37c12c1e2f
--- /dev/null
+++ b/autogen/coding/func_with_reqs.py
@@ -0,0 +1,128 @@
+from __future__ import annotations
+import inspect
+import functools
+from typing import Any, Callable, List, TypeVar, Generic, Union
+from typing_extensions import ParamSpec
+from textwrap import indent, dedent
+from dataclasses import dataclass, field
+
+T = TypeVar("T")
+P = ParamSpec("P")
+
+
+def _to_code(func: Union[FunctionWithRequirements[T, P], Callable[P, T]]) -> str:
+    code = inspect.getsource(func)
+    # Strip the decorator
+    if code.startswith("@"):
+        code = code[code.index("\n") + 1 :]
+    return code
+
+
+@dataclass
+class Alias:
+    name: str
+    alias: str
+
+
+@dataclass
+class ImportFromModule:
+    module: str
+    imports: List[Union[str, Alias]]
+
+
+Import = Union[str, ImportFromModule, Alias]
+
+
+def _import_to_str(im: Import) -> str:
+    if isinstance(im, str):
+        return f"import {im}"
+    elif isinstance(im, Alias):
+        return f"import {im.name} as {im.alias}"
+    else:
+
+        def to_str(i: Union[str, Alias]) -> str:
+            if isinstance(i, str):
+                return i
+            else:
+                return f"{i.name} as {i.alias}"
+
+        imports = ", ".join(map(to_str, im.imports))
+        return f"from {im.module} import {imports}"
+
+
+@dataclass
+class FunctionWithRequirements(Generic[T, P]):
+    func: Callable[P, T]
+    python_packages: List[str] = field(default_factory=list)
+    global_imports: List[Import] = field(default_factory=list)
+
+    @classmethod
+    def from_callable(
+        cls, func: Callable[P, T], python_packages: List[str] = [], global_imports: List[Import] = []
+    ) -> FunctionWithRequirements[T, P]:
+        return cls(python_packages=python_packages, global_imports=global_imports, func=func)
+
+    # Type this based on F
+    def __call__(self, *args: P.args, **kwargs: P.kwargs) -> T:
+        return self.func(*args, **kwargs)
+
+
+def with_requirements(
+    python_packages: List[str] = [], global_imports: List[Import] = []
+) -> Callable[[Callable[P, T]], FunctionWithRequirements[T, P]]:
+    """Decorate a function with package and import requirements
+
+    Args:
+        python_packages (List[str], optional): Packages required to function. Can include version info.. Defaults to [].
+        global_imports (List[Import], optional): Required imports. Defaults to [].
+
+    Returns:
+        Callable[[Callable[P, T]], FunctionWithRequirements[T, P]]: The decorated function
+    """
+
+    def wrapper(func: Callable[P, T]) -> FunctionWithRequirements[T, P]:
+        func_with_reqs = FunctionWithRequirements(
+            python_packages=python_packages, global_imports=global_imports, func=func
+        )
+
+        functools.update_wrapper(func_with_reqs, func)
+        return func_with_reqs
+
+    return wrapper
+
+
+def _build_python_functions_file(funcs: List[Union[FunctionWithRequirements[Any, P], Callable[..., Any]]]) -> str:
+    # First collect all global imports
+    global_imports = set()
+    for func in funcs:
+        if isinstance(func, FunctionWithRequirements):
+            global_imports.update(func.global_imports)
+
+    content = "\n".join(map(_import_to_str, global_imports)) + "\n\n"
+
+    for func in funcs:
+        content += _to_code(func) + "\n\n"
+
+    return content
+
+
+def to_stub(func: Callable[..., Any]) -> str:
+    """Generate a stub for a function as a string
+
+    Args:
+        func (Callable[..., Any]): The function to generate a stub for
+
+    Returns:
+        str: The stub for the function
+    """
+    content = f"def {func.__name__}{inspect.signature(func)}:\n"
+    docstring = func.__doc__
+
+    if docstring:
+        docstring = dedent(docstring)
+        docstring = '"""' + docstring + '"""'
+        docstring = indent(docstring, "    ")
+        content += docstring + "\n"
+
+    content += "    ..."
+    return content
diff --git a/autogen/coding/local_commandline_code_executor.py b/autogen/coding/local_commandline_code_executor.py
index b927638865b..b75f54ff121 100644
--- a/autogen/coding/local_commandline_code_executor.py
+++ b/autogen/coding/local_commandline_code_executor.py
@@ -1,14 +1,14 @@
 from hashlib import md5
-import os
 from pathlib import Path
 import re
+from string import Template
 import sys
-import uuid
 import warnings
-from typing import ClassVar, List, Union
+from typing import Any, Callable, ClassVar, List, TypeVar, Union, cast
+from typing_extensions import ParamSpec
+from autogen.coding.func_with_reqs import FunctionWithRequirements, _build_python_functions_file, to_stub
 
-from ..agentchat.agent import LLMAgent
-from ..code_utils import TIMEOUT_MSG, WIN32, _cmd, execute_code
+from ..code_utils import TIMEOUT_MSG, WIN32, _cmd
 from .base import CodeBlock, CodeExecutor, CodeExtractor, CommandLineCodeResult
 from .markdown_code_extractor import MarkdownCodeExtractor
 
@@ -16,16 +16,30 @@
 
 import subprocess
 
+import logging
+
 __all__ = ("LocalCommandLineCodeExecutor",)
 
+A = ParamSpec("A")
+
 
 class LocalCommandLineCodeExecutor(CodeExecutor):
     SUPPORTED_LANGUAGES: ClassVar[List[str]] = ["bash", "shell", "sh", "pwsh", "powershell", "ps1", "python"]
+    FUNCTIONS_MODULE: ClassVar[str] = "functions"
+    FUNCTIONS_FILENAME: ClassVar[str] = "functions.py"
+    FUNCTION_PROMPT_TEMPLATE: ClassVar[
+        str
+    ] = """You have access to the following user defined functions. They can be accessed from the module called `$module_name` by their function names.
+
+For example, if there was a function called `foo` you could import it by writing `from $module_name import foo`
+
+$functions"""
 
     def __init__(
         self,
         timeout: int = 60,
         work_dir: Union[Path, str] = Path("."),
+        functions: List[Union[FunctionWithRequirements[Any, A], Callable[..., Any]]] = [],
     ):
         """(Experimental) A code executor class that executes code through a local command line
         environment.
@@ -48,6 +62,7 @@ def __init__(
             work_dir (str): The working directory for the code execution. If None,
                 a default working directory will be used. The default working
                 directory is the current directory ".".
+            functions (List[Union[FunctionWithRequirements[Any, A], Callable[..., Any]]]): A list of functions that are available to the code executor. Default is an empty list.
         """
 
         if timeout < 1:
@@ -62,6 +77,38 @@ def __init__(
         self._timeout = timeout
         self._work_dir: Path = work_dir
 
+        self._functions = functions
+        # Setup could take some time so we intentionally wait for the first code block to do it.
+        if len(functions) > 0:
+            self._setup_functions_complete = False
+        else:
+            self._setup_functions_complete = True
+
+    def format_functions_for_prompt(self, prompt_template: str = FUNCTION_PROMPT_TEMPLATE) -> str:
+        """(Experimental) Format the functions for a prompt.
+
+        The template includes two variables:
+        - `$module_name`: The module name.
+        - `$functions`: The functions formatted as stubs with two newlines between each function.
+
+        Args:
+            prompt_template (str): The prompt template. Default is the class default.
+
+        Returns:
+            str: The formatted prompt.
+        """
+
+        template = Template(prompt_template)
+        return template.substitute(
+            module_name=self.FUNCTIONS_MODULE,
+            functions="\n\n".join([to_stub(func) for func in self._functions]),
+        )
+
+    @property
+    def functions(self) -> List[Union[FunctionWithRequirements[Any, A], Callable[..., Any]]]:
+        """(Experimental) The functions that are available to the code executor."""
+        return self._functions
+
     @property
     def timeout(self) -> int:
         """(Experimental) The timeout for code execution."""
@@ -99,6 +146,39 @@ def sanitize_command(lang: str, code: str) -> None:
                 if re.search(pattern, code):
                     raise ValueError(f"Potentially dangerous command detected: {message}")
 
+    def _setup_functions(self) -> None:
+        func_file_content = _build_python_functions_file(self._functions)
+        func_file = self._work_dir / self.FUNCTIONS_FILENAME
+        func_file.write_text(func_file_content)
+
+        # Collect requirements
+        lists_of_packages = [x.python_packages for x in self._functions if isinstance(x, FunctionWithRequirements)]
+        flattened_packages = [item for sublist in lists_of_packages for item in sublist]
+        required_packages = list(set(flattened_packages))
+        if len(required_packages) > 0:
+            logging.info("Ensuring packages are installed in executor.")
+
+            cmd = [sys.executable, "-m", "pip", "install"]
+            cmd.extend(required_packages)
+
+            try:
+                result = subprocess.run(
+                    cmd, cwd=self._work_dir, capture_output=True, text=True, timeout=float(self._timeout)
+                )
+            except subprocess.TimeoutExpired as e:
+                raise ValueError("Pip install timed out") from e
+
+            if result.returncode != 0:
+                raise ValueError(f"Pip install failed. {result.stdout}, {result.stderr}")
+
+        # Attempt to load the function file to check for syntax errors, imports etc.
+        exec_result = self._execute_code_dont_check_setup([CodeBlock(code=func_file_content, language="python")])
+
+        if exec_result.exit_code != 0:
+            raise ValueError(f"Functions failed to load: {exec_result.output}")
+
+        self._setup_functions_complete = True
+
     def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CommandLineCodeResult:
         """(Experimental) Execute the code blocks and return the result.
 
@@ -107,6 +187,13 @@ def execute_code_blocks(self, code_blocks: List[CodeBlock]) -> CommandLineCodeRe
 
         Returns:
             CommandLineCodeResult: The result of the code execution."""
+
+        if not self._setup_functions_complete:
+            self._setup_functions()
+
+        return self._execute_code_dont_check_setup(code_blocks)
+
+    def _execute_code_dont_check_setup(self, code_blocks: List[CodeBlock]) -> CommandLineCodeResult:
         logs_all = ""
         file_names = []
         for code_block in code_blocks:
diff --git a/setup.py b/setup.py
index 78e788dead9..811cf6e581d 100644
--- a/setup.py
+++ b/setup.py
@@ -55,6 +55,7 @@
             "pre-commit",
             "pytest-asyncio",
             "pytest>=6.1.1,<8",
+            "pandas",
         ],
         "blendsearch": ["flaml[blendsearch]"],
         "mathchat": ["sympy", "pydantic==1.10.9", "wolframalpha"],
diff --git a/test/coding/test_user_defined_functions.py b/test/coding/test_user_defined_functions.py
new file mode 100644
index 00000000000..8e88b909a84
--- /dev/null
+++ b/test/coding/test_user_defined_functions.py
@@ -0,0 +1,151 @@
+import tempfile
+import pytest
+from autogen.coding.base import CodeBlock
+from autogen.coding.local_commandline_code_executor import LocalCommandLineCodeExecutor
+
+try:
+    import pandas
+except ImportError:
+    skip = True
+else:
+    skip = False
+
+from autogen.coding.func_with_reqs import with_requirements
+
+classes_to_test = [LocalCommandLineCodeExecutor]
+
+
+def add_two_numbers(a: int, b: int) -> int:
+    """Add two numbers together."""
+    return a + b
+
+
+@with_requirements(python_packages=["pandas"], global_imports=["pandas"])
+def load_data() -> "pandas.DataFrame":
+    """Load some sample data.
+
+    Returns:
+        pandas.DataFrame: A DataFrame with the following columns: name(str), location(str), age(int)
+    """
+    data = {
+        "name": ["John", "Anna", "Peter", "Linda"],
+        "location": ["New York", "Paris", "Berlin", "London"],
+        "age": [24, 13, 53, 33],
+    }
+    return pandas.DataFrame(data)
+
+
+@with_requirements(global_imports=["NOT_A_REAL_PACKAGE"])
+def function_incorrect_import() -> "pandas.DataFrame":
+    return pandas.DataFrame()
+
+
+@with_requirements(python_packages=["NOT_A_REAL_PACKAGE"])
+def function_incorrect_dep() -> "pandas.DataFrame":
+    return pandas.DataFrame()
+
+
+def function_missing_reqs() -> "pandas.DataFrame":
+    return pandas.DataFrame()
+
+
+@pytest.mark.parametrize("cls", classes_to_test)
+@pytest.mark.skipif(skip, reason="pandas not installed")
+def test_can_load_function_with_reqs(cls) -> None:
+    with tempfile.TemporaryDirectory() as temp_dir:
+        executor = cls(work_dir=temp_dir, functions=[load_data])
+        code = f"""from {cls.FUNCTIONS_MODULE} import load_data
+import pandas
+
+# Get first row's name
+print(load_data().iloc[0]['name'])"""
+
+        result = executor.execute_code_blocks(
+            code_blocks=[
+                CodeBlock(language="python", code=code),
+            ]
+        )
+        assert result.output == "John\n"
+        assert result.exit_code == 0
+
+
+@pytest.mark.parametrize("cls", classes_to_test)
+@pytest.mark.skipif(skip, reason="pandas not installed")
+def test_can_load_function(cls) -> None:
+    with tempfile.TemporaryDirectory() as temp_dir:
+        executor = cls(work_dir=temp_dir, functions=[add_two_numbers])
+        code = f"""from {cls.FUNCTIONS_MODULE} import add_two_numbers
+print(add_two_numbers(1, 2))"""
+
+        result = executor.execute_code_blocks(
+            code_blocks=[
+                CodeBlock(language="python", code=code),
+            ]
+        )
+        assert result.output == "3\n"
+        assert result.exit_code == 0
+
+
+# TODO - only run this test for containerized executors, as the environment is not guaranteed to have pandas installed
+# It is common for the local environment to have pandas installed, so this test will not work as expected
+# @pytest.mark.parametrize("cls", classes_to_test)
+# @pytest.mark.skipif(skip, reason="pandas not installed")
+# def test_fails_for_missing_reqs(cls) -> None:
+#     with tempfile.TemporaryDirectory() as temp_dir:
+#         executor = cls(work_dir=temp_dir, functions=[function_missing_reqs])
+#         code = f"""from {cls.FUNCTIONS_MODULE} import function_missing_reqs
+# function_missing_reqs()"""
+
+#         with pytest.raises(ValueError):
+#             executor.execute_code_blocks(
+#                 code_blocks=[
+#                     CodeBlock(language="python", code=code),
+#                 ]
+#             )
+
+
+@pytest.mark.parametrize("cls", classes_to_test)
+@pytest.mark.skipif(skip, reason="pandas not installed")
+def test_fails_for_function_incorrect_import(cls) -> None:
+    with tempfile.TemporaryDirectory() as temp_dir:
+        executor = cls(work_dir=temp_dir, functions=[function_incorrect_import])
+        code = f"""from {cls.FUNCTIONS_MODULE} import function_incorrect_import
+function_incorrect_import()"""
+
+        with pytest.raises(ValueError):
+            executor.execute_code_blocks(
+                code_blocks=[
+                    CodeBlock(language="python", code=code),
+                ]
+            )
+
+
+@pytest.mark.parametrize("cls", classes_to_test)
+@pytest.mark.skipif(skip, reason="pandas not installed")
+def test_fails_for_function_incorrect_dep(cls) -> None:
+    with tempfile.TemporaryDirectory() as temp_dir:
+        executor = cls(work_dir=temp_dir, functions=[function_incorrect_dep])
+        code = f"""from {cls.FUNCTIONS_MODULE} import function_incorrect_dep
+function_incorrect_dep()"""
+
+        with pytest.raises(ValueError):
+            executor.execute_code_blocks(
+                code_blocks=[
+                    CodeBlock(language="python", code=code),
+                ]
+            )
+
+
+@pytest.mark.parametrize("cls", classes_to_test)
+@pytest.mark.skipif(skip, reason="pandas not installed")
+def test_formatted_prompt(cls) -> None:
+    with tempfile.TemporaryDirectory() as temp_dir:
+        executor = cls(work_dir=temp_dir, functions=[add_two_numbers])
+
+        result = executor.format_functions_for_prompt()
+        assert (
+            '''def add_two_numbers(a: int, b: int) -> int:
+    """Add two numbers together."""
+'''
+            in result
+        )
diff --git a/website/docs/topics/code-execution/user-defined-functions.ipynb b/website/docs/topics/code-execution/user-defined-functions.ipynb
new file mode 100644
index 00000000000..3e681dcff94
--- /dev/null
+++ b/website/docs/topics/code-execution/user-defined-functions.ipynb
@@ -0,0 +1,463 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# User Defined Functions\n",
+    "\n",
+    "```mdx\n",
+    ":::note\n",
+    "This is experimental and not *yet* supported by all executors. At this stage only [`LocalCommandLineCodeExecutor`](/docs/reference/coding/local_commandline_code_executor#localcommandlinecodeexecutor) is supported.\n",
+    "\n",
+    "\n",
+    "Currently, the method of registering tools and using this feature are different. We would like to unify them. See Github issue [here](https://github.com/microsoft/autogen/issues/2101)\n",
+    ":::\n",
+    "```\n",
+    "\n",
+    "User defined functions allow you to define Python functions in your AutoGen program and then provide these to be used by your executor. This allows you to provide your agents with tools without using traditional tool calling APIs. Currently only Python is supported for this feature.\n",
+    "\n",
+    "There are several steps involved:\n",
+    "\n",
+    "1. Define the function\n",
+    "2. Provide the function to the executor\n",
+    "3. Explain to the code writing agent how to use the function\n",
+    "\n",
+    "\n",
+    "## Define the function\n",
+    "\n",
+    "```mdx\n",
+    ":::warning\n",
+    "Keep in mind that the entire source code of these functions will be available to the executor. This means that you should not include any sensitive information in the function as an LLM agent may be able to access it.\n",
+    ":::\n",
+    "```\n",
+    "\n",
+    "If the function does not require any external imports or dependencies then you can simply use the function. For example:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def add_two_numbers(a: int, b: int) -> int:\n",
+    "    \"\"\"Add two numbers together.\"\"\"\n",
+    "    return a + b"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "This would be a valid standalone function.\n",
+    "\n",
+    "```mdx\n",
+    ":::tip\n",
+    "Using type hints and docstrings are not required but are highly recommended. They will help the code writing agent understand the function and how to use it.\n",
+    ":::\n",
+    "```\n",
+    "\n",
+    "If the function requires external imports or dependencies then you can use the `@with_requirements` decorator to specify the requirements. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas\n",
+    "from autogen.coding.func_with_reqs import with_requirements\n",
+    "\n",
+    "\n",
+    "@with_requirements(python_packages=[\"pandas\"], global_imports=[\"pandas\"])\n",
+    "def load_data() -> pandas.DataFrame:\n",
+    "    \"\"\"Load some sample data.\n",
+    "\n",
+    "    Returns:\n",
+    "        pandas.DataFrame: A DataFrame with the following columns: name(str), location(str), age(int)\n",
+    "    \"\"\"\n",
+    "    data = {\n",
+    "        \"name\": [\"John\", \"Anna\", \"Peter\", \"Linda\"],\n",
+    "        \"location\": [\"New York\", \"Paris\", \"Berlin\", \"London\"],\n",
+    "        \"age\": [24, 13, 53, 33],\n",
+    "    }\n",
+    "    return pandas.DataFrame(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "If you wanted to rename `pandas` to `pd` or import `DataFrame` directly you could do the following:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from pandas import DataFrame\n",
+    "from pandas import DataFrame as df\n",
+    "\n",
+    "from autogen.coding.func_with_reqs import Alias, ImportFromModule, with_requirements\n",
+    "\n",
+    "\n",
+    "@with_requirements(python_packages=[\"pandas\"], global_imports=[Alias(\"pandas\", \"pd\")])\n",
+    "def some_func1() -> pd.DataFrame: ...\n",
+    "\n",
+    "\n",
+    "@with_requirements(python_packages=[\"pandas\"], global_imports=[ImportFromModule(\"pandas\", \"DataFrame\")])\n",
+    "def some_func2() -> DataFrame: ...\n",
+    "\n",
+    "\n",
+    "@with_requirements(python_packages=[\"pandas\"], global_imports=[ImportFromModule(\"pandas\", Alias(\"DataFrame\", \"df\"))])\n",
+    "def some_func3() -> df: ..."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Provide the function to the executor\n",
+    "\n",
+    "Functions can be loaded into the executor in its constructor. For example:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pathlib import Path\n",
+    "from autogen.coding import CodeBlock\n",
+    "from autogen.coding import LocalCommandLineCodeExecutor\n",
+    "\n",
+    "work_dir = Path(\"coding\")\n",
+    "work_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "executor = LocalCommandLineCodeExecutor(work_dir=work_dir, functions=[add_two_numbers, load_data])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Before we get an agent involved, we can sanity check that when the agent writes code that looks like this the executor will be able to handle it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "exit_code=0 output='3\\n' code_file='/Users/jackgerrits/w/autogen/website/docs/topics/code-execution/coding/tmp_code_1958fe3aea3e8e3c6e907fe951b5f6ab.py'\n"
+     ]
+    }
+   ],
+   "source": [
+    "code = f\"\"\"\n",
+    "from {LocalCommandLineCodeExecutor.FUNCTIONS_MODULE} import add_two_numbers\n",
+    "\n",
+    "print(add_two_numbers(1, 2))\n",
+    "\"\"\"\n",
+    "\n",
+    "print(\n",
+    "    executor.execute_code_blocks(\n",
+    "        code_blocks=[\n",
+    "            CodeBlock(language=\"python\", code=code),\n",
+    "        ]\n",
+    "    )\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can try the function that required a dependency and import too."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "    name  location  age\n",
+      "0   John  New York   24\n",
+      "1   Anna     Paris   13\n",
+      "2  Peter    Berlin   53\n",
+      "3  Linda    London   33\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "code = f\"\"\"\n",
+    "from {LocalCommandLineCodeExecutor.FUNCTIONS_MODULE} import load_data\n",
+    "\n",
+    "print(load_data())\n",
+    "\"\"\"\n",
+    "\n",
+    "result = executor.execute_code_blocks(\n",
+    "    code_blocks=[\n",
+    "        CodeBlock(language=\"python\", code=code),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "print(result.output)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Limitations\n",
+    "\n",
+    "- Only Python is supported currently\n",
+    "- The function must not depend on any globals or external state as it is loaded as source code \n",
+    "\n",
+    "## Explain to the code writing agent how to use the function\n",
+    "\n",
+    "Now that the function is available to be called by the executor, you can explain to the code writing agent how to use the function. This step is very important as by default it will not know about it.\n",
+    "\n",
+    "There is a utility function that you can use to generate a default prompt that describes the available functions and how to use them. This function can have its template overridden to provide a custom message, or you can use a different prompt all together.\n",
+    "\n",
+    "For example, you could extend the system message from the page about local execution with a new section that describes the functions available."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "You have been given coding capability to solve tasks using Python code.\n",
+      "In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.\n",
+      "    1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.\n",
+      "    2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.\n",
+      "Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.\n",
+      "When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.\n",
+      "If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.\n",
+      "You have access to the following user defined functions. They can be accessed from the module called `functions` by their function names.\n",
+      "\n",
+      "For example, if there was a function called `foo` you could import it by writing `from functions import foo`\n",
+      "\n",
+      "def add_two_numbers(a: int, b: int) -> int:\n",
+      "    \"\"\"Add two numbers together.\"\"\"\n",
+      "    ...\n",
+      "\n",
+      "def load_data() -> pandas.core.frame.DataFrame:\n",
+      "    \"\"\"Load some sample data.\n",
+      "\n",
+      "        Returns:\n",
+      "            pandas.DataFrame: A DataFrame with the following columns: name(str), location(str), age(int)\n",
+      "    \"\"\"\n",
+      "    ...\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "nlnl = \"\\n\\n\"\n",
+    "code_writer_system_message = \"\"\"\n",
+    "You have been given coding capability to solve tasks using Python code.\n",
+    "In the following cases, suggest python code (in a python coding block) or shell script (in a sh coding block) for the user to execute.\n",
+    "    1. When you need to collect info, use the code to output the info you need, for example, browse or search the web, download/read a file, print the content of a webpage or a file, get the current date/time, check the operating system. After sufficient info is printed and the task is ready to be solved based on your language skill, you can solve the task by yourself.\n",
+    "    2. When you need to perform some task with code, use the code to perform the task and output the result. Finish the task smartly.\n",
+    "Solve the task step by step if you need to. If a plan is not provided, explain your plan first. Be clear which step uses code, and which step uses your language skill.\n",
+    "When using code, you must indicate the script type in the code block. The user cannot provide any other feedback or perform any other action beyond executing the code you suggest. The user can't modify your code. So do not suggest incomplete code which requires users to modify. Don't use a code block if it's not intended to be executed by the user.\n",
+    "If you want the user to save the code in a file before executing it, put # filename: <filename> inside the code block as the first line. Don't include multiple code blocks in one response. Do not ask users to copy and paste the result. Instead, use 'print' function for the output when relevant. Check the execution result returned by the user.\n",
+    "\"\"\"\n",
+    "\n",
+    "# Add on the new functions\n",
+    "code_writer_system_message += executor.format_functions_for_prompt()\n",
+    "\n",
+    "print(code_writer_system_message)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then you can use this system message for your code writing agent."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from autogen import ConversableAgent\n",
+    "\n",
+    "import os\n",
+    "\n",
+    "code_writer_agent = ConversableAgent(\n",
+    "    \"code_writer\",\n",
+    "    system_message=code_writer_system_message,\n",
+    "    llm_config={\"config_list\": [{\"model\": \"gpt-4\", \"api_key\": os.environ[\"OPENAI_API_KEY\"]}]},\n",
+    "    code_execution_config=False,  # Turn off code execution for this agent.\n",
+    "    max_consecutive_auto_reply=2,\n",
+    "    human_input_mode=\"NEVER\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now, we can setup the code execution agent using the local command line executor we defined earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "code_executor_agent = ConversableAgent(\n",
+    "    name=\"code_executor_agent\",\n",
+    "    llm_config=False,\n",
+    "    code_execution_config={\n",
+    "        \"executor\": executor,\n",
+    "    },\n",
+    "    human_input_mode=\"NEVER\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then, we can start the conversation and get the agent to process the dataframe we provided."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mcode_executor_agent\u001b[0m (to code_writer):\n",
+      "\n",
+      "Please use the load_data function to load the data and please calculate the average age of all people.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mcode_writer\u001b[0m (to code_executor_agent):\n",
+      "\n",
+      "Below is the python code to load the data using the `load_data()` function and calculate the average age of all people. \n",
+      "\n",
+      "```python\n",
+      "# python code\n",
+      "from functions import load_data\n",
+      "import numpy as np\n",
+      "\n",
+      "# Load the data\n",
+      "df = load_data()\n",
+      "\n",
+      "# Calculate the average age\n",
+      "avg_age = np.mean(df['age'])\n",
+      "\n",
+      "print(\"The average age is\", avg_age)\n",
+      "```\n",
+      "\n",
+      "This code starts by importing the `load_data()` function. It then uses this function to load the data into a variable `df`. Afterwards, it calculates the average (mean) of the 'age' column in the DataFrame, before printing the result.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK (inferred language is python)...\u001b[0m\n",
+      "\u001b[33mcode_executor_agent\u001b[0m (to code_writer):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: The average age is 30.75\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mcode_writer\u001b[0m (to code_executor_agent):\n",
+      "\n",
+      "Great! The code worked fine. So, the average age of all people in the dataset is 30.75 years.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mcode_executor_agent\u001b[0m (to code_writer):\n",
+      "\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "chat_result = code_executor_agent.initiate_chat(\n",
+    "    code_writer_agent,\n",
+    "    message=\"Please use the load_data function to load the data and please calculate the average age of all people.\",\n",
+    "    summary_method=\"reflection_with_llm\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can see the summary of the calculation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The average age of all people in the dataset is 30.75 years.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(chat_result.summary)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "autogen",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 812c67a79085bbf10472be688bb429ea91877dc8 Mon Sep 17 00:00:00 2001
From: Chi Wang <wang.chi@microsoft.com>
Date: Wed, 27 Mar 2024 20:43:01 -0700
Subject: [PATCH 16/27] simplify getting-started; update news (#2175)

* simplify getting-started; update news

* bug fix
---
 README.md                                     | 12 +++--
 .../docker_commandline_code_executor.py       |  3 +-
 .../coding/local_commandline_code_executor.py |  3 +-
 autogen/version.py                            |  2 +-
 test/coding/test_commandline_code_executor.py |  6 ++-
 website/docs/Getting-Started.mdx              | 49 +++++++++----------
 6 files changed, 38 insertions(+), 37 deletions(-)

diff --git a/README.md b/README.md
index 76f469ecef5..a5f4ea36f73 100644
--- a/README.md
+++ b/README.md
@@ -12,23 +12,25 @@
     <img src="https://github.com/microsoft/autogen/blob/main/website/static/img/flaml.svg"  width=200>
     <br>
 </p> -->
+:fire: Mar 26: Andrew Ng gave a shoutout to AutoGen in [What's next for AI agentic workflows](https://youtu.be/sal78ACtGTc?si=JduUzN_1kDnMq0vF) at Sequoia Capital's AI Ascent.
+
 :fire: Mar 3: What's new in AutoGen? 📰[Blog](https://microsoft.github.io/autogen/blog/2024/03/03/AutoGen-Update); 📺[Youtube](https://www.youtube.com/watch?v=j_mtwQiaLGU).
 
 :fire: Mar 1: the first AutoGen multi-agent experiment on the challenging [GAIA](https://huggingface.co/spaces/gaia-benchmark/leaderboard) benchmark achieved the No. 1 accuracy in all the three levels.
 
-:fire: Jan 30: AutoGen is highlighted by Peter Lee in Microsoft Research Forum [Keynote](https://t.co/nUBSjPDjqD).
+:tada: Jan 30: AutoGen is highlighted by Peter Lee in Microsoft Research Forum [Keynote](https://t.co/nUBSjPDjqD).
 
-:fire: Dec 31: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) is selected by [TheSequence: My Five Favorite AI Papers of 2023](https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023).
+:tada: Dec 31: [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155) is selected by [TheSequence: My Five Favorite AI Papers of 2023](https://thesequence.substack.com/p/my-five-favorite-ai-papers-of-2023).
 
 <!-- :fire: Nov 24: pyautogen [v0.2](https://github.com/microsoft/autogen/releases/tag/v0.2.0) is released with many updates and new features compared to v0.1.1. It switches to using openai-python v1. Please read the [migration guide](https://microsoft.github.io/autogen/docs/Installation#python). -->
 
 <!-- :fire: Nov 11: OpenAI's Assistants are available in AutoGen and interoperatable with other AutoGen agents! Checkout our [blogpost](https://microsoft.github.io/autogen/blog/2023/11/13/OAI-assistants) for details and examples. -->
 
-:fire: Nov 8: AutoGen is selected into [Open100: Top 100 Open Source achievements](https://www.benchcouncil.org/evaluation/opencs/annual.html) 35 days after spinoff.
+:tada: Nov 8: AutoGen is selected into [Open100: Top 100 Open Source achievements](https://www.benchcouncil.org/evaluation/opencs/annual.html) 35 days after spinoff.
 
-:fire: Nov 6: AutoGen is mentioned by Satya Nadella in a [fireside chat](https://youtu.be/0pLBvgYtv6U).
+:tada: Nov 6: AutoGen is mentioned by Satya Nadella in a [fireside chat](https://youtu.be/0pLBvgYtv6U).
 
-:fire: Nov 1: AutoGen is the top trending repo on GitHub in October 2023.
+:tada: Nov 1: AutoGen is the top trending repo on GitHub in October 2023.
 
 :tada: Oct 03: AutoGen spins off from FLAML on GitHub and has a major paper update (first version on Aug 16).
 
diff --git a/autogen/coding/docker_commandline_code_executor.py b/autogen/coding/docker_commandline_code_executor.py
index f1db7cd07e7..8d8931a830f 100644
--- a/autogen/coding/docker_commandline_code_executor.py
+++ b/autogen/coding/docker_commandline_code_executor.py
@@ -83,8 +83,7 @@ def __init__(
         if isinstance(work_dir, str):
             work_dir = Path(work_dir)
 
-        if not work_dir.exists():
-            raise ValueError(f"Working directory {work_dir} does not exist.")
+        work_dir.mkdir(exist_ok=True)
 
         client = docker.from_env()
 
diff --git a/autogen/coding/local_commandline_code_executor.py b/autogen/coding/local_commandline_code_executor.py
index b75f54ff121..4bcf988746e 100644
--- a/autogen/coding/local_commandline_code_executor.py
+++ b/autogen/coding/local_commandline_code_executor.py
@@ -71,8 +71,7 @@ def __init__(
         if isinstance(work_dir, str):
             work_dir = Path(work_dir)
 
-        if not work_dir.exists():
-            raise ValueError(f"Working directory {work_dir} does not exist.")
+        work_dir.mkdir(exist_ok=True)
 
         self._timeout = timeout
         self._work_dir: Path = work_dir
diff --git a/autogen/version.py b/autogen/version.py
index 198d6db6273..f2892741976 100644
--- a/autogen/version.py
+++ b/autogen/version.py
@@ -1 +1 @@
-__version__ = "0.2.20"
+__version__ = "0.2.21"
diff --git a/test/coding/test_commandline_code_executor.py b/test/coding/test_commandline_code_executor.py
index aeb62349b38..49d4d35cf10 100644
--- a/test/coding/test_commandline_code_executor.py
+++ b/test/coding/test_commandline_code_executor.py
@@ -1,5 +1,6 @@
 from pathlib import Path
 import sys
+import os
 import tempfile
 import uuid
 import pytest
@@ -10,7 +11,8 @@
 from autogen.coding.docker_commandline_code_executor import DockerCommandLineCodeExecutor
 from autogen.coding.local_commandline_code_executor import LocalCommandLineCodeExecutor
 
-from conftest import MOCK_OPEN_AI_API_KEY, skip_docker
+sys.path.append(os.path.join(os.path.dirname(__file__), ".."))
+from conftest import MOCK_OPEN_AI_API_KEY, skip_docker  # noqa: E402
 
 if skip_docker or not is_docker_running():
     classes_to_test = [LocalCommandLineCodeExecutor]
@@ -52,7 +54,7 @@ def test_commandline_executor_init(cls) -> None:
     assert executor.timeout == 10 and str(executor.work_dir) == "."
 
     # Try invalid working directory.
-    with pytest.raises(ValueError, match="Working directory .* does not exist."):
+    with pytest.raises(FileNotFoundError):
         executor = cls(timeout=111, work_dir="/invalid/directory")
 
 
diff --git a/website/docs/Getting-Started.mdx b/website/docs/Getting-Started.mdx
index 0e90a322f55..a933ae3cc50 100644
--- a/website/docs/Getting-Started.mdx
+++ b/website/docs/Getting-Started.mdx
@@ -38,30 +38,37 @@ pip install pyautogen
 ```
 
 <Tabs>
+    <TabItem value="nocode" label="No code execution" default>
+
+```python
+from autogen import AssistantAgent, UserProxyAgent
+
+llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
+assistant = AssistantAgent("assistant", llm_config=llm_config)
+user_proxy = UserProxyAgent("user_proxy", code_execution_config=False)
+
+# Start the chat
+user_proxy.initiate_chat(
+    assistant,
+    message="Tell me a joke about NVDA and TESLA stock prices.",
+)
+```
+
+    </TabItem>
     <TabItem value="local" label="Local execution" default>
 :::warning
 When asked, be sure to check the generated code before continuing to ensure it is safe to run.
 :::
 
 ```python
+import autogen
 from autogen import AssistantAgent, UserProxyAgent
-from autogen.coding import LocalCommandLineCodeExecutor
-
-import os
-from pathlib import Path
-
-llm_config = {
-    "config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}],
-}
-
-work_dir = Path("coding")
-work_dir.mkdir(exist_ok=True)
 
+llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
 assistant = AssistantAgent("assistant", llm_config=llm_config)
 
-code_executor = LocalCommandLineCodeExecutor(work_dir=work_dir)
 user_proxy = UserProxyAgent(
-    "user_proxy", code_execution_config={"executor": code_executor}
+    "user_proxy", code_execution_config={"executor": autogen.coding.LocalCommandLineCodeExecutor(work_dir="coding")}
 )
 
 # Start the chat
@@ -75,20 +82,12 @@ user_proxy.initiate_chat(
     <TabItem value="docker" label="Docker execution" default>
 
 ```python
+import autogen
 from autogen import AssistantAgent, UserProxyAgent
-from autogen.coding import DockerCommandLineCodeExecutor
-
-import os
-from pathlib import Path
 
-llm_config = {
-    "config_list": [{"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}],
-}
+llm_config = {"model": "gpt-4", "api_key": os.environ["OPENAI_API_KEY"]}
 
-work_dir = Path("coding")
-work_dir.mkdir(exist_ok=True)
-
-with DockerCommandLineCodeExecutor(work_dir=work_dir) as code_executor:
+with autogen.coding.DockerCommandLineCodeExecutor(work_dir="coding") as code_executor:
     assistant = AssistantAgent("assistant", llm_config=llm_config)
     user_proxy = UserProxyAgent(
         "user_proxy", code_execution_config={"executor": code_executor}
@@ -103,7 +102,7 @@ with DockerCommandLineCodeExecutor(work_dir=work_dir) as code_executor:
 
 Open `coding/plot.png` to see the generated plot.
 
-  </TabItem>
+    </TabItem>
 </Tabs>
 
 :::tip

From 160d474a4d5003c9946c0060111f881b35efe6e3 Mon Sep 17 00:00:00 2001
From: Shaokun Zhang <shaokunzhang529@gmail.com>
Date: Thu, 28 Mar 2024 01:11:20 -0400
Subject: [PATCH 17/27] update (#2178)
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Co-authored-by: AnonymousRepoSub <“shaokunzhang529@outlook.com” >
---
 website/blog/2023-12-23-AgentOptimizer/index.mdx | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/website/blog/2023-12-23-AgentOptimizer/index.mdx b/website/blog/2023-12-23-AgentOptimizer/index.mdx
index 23e348fa30c..caafda75790 100644
--- a/website/blog/2023-12-23-AgentOptimizer/index.mdx
+++ b/website/blog/2023-12-23-AgentOptimizer/index.mdx
@@ -12,10 +12,12 @@ tags: [LLM, research]
 **TL;DR:**
 Introducing **AgentOptimizer**, a new class for training LLM agents in the era of LLMs as a service.
 **AgentOptimizer** is able to prompt LLMs to iteratively optimize function/skills of AutoGen agents according to the historical conversation and performance.
-Checkout one implementation for **AgentOptimizer** on [MATH](https://github.com/hendrycks/math) dataset
-[here](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb).
 
-More information could be found in the [paper](https://arxiv.org/abs/2402.11359).
+More information could be found in:
+
+**Paper**: https://arxiv.org/abs/2402.11359.
+
+**Notebook**: https://github.com/microsoft/autogen/blob/main/notebook/agentchat_agentoptimizer.ipynb.
 
 ## Introduction
 In the traditional ML pipeline, we train a model by updating its weights according to the loss on the training set, while in the era of LLM agents, how should we train an agent?

From 751fc92cebde69f0903080611538845ec12bfd9d Mon Sep 17 00:00:00 2001
From: Jack Gerrits <jackgerrits@users.noreply.github.com>
Date: Thu, 28 Mar 2024 09:48:24 -0400
Subject: [PATCH 18/27] Fix formatting of admonitions in udf docs (#2188)

---
 .../code-execution/user-defined-functions.ipynb      | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/website/docs/topics/code-execution/user-defined-functions.ipynb b/website/docs/topics/code-execution/user-defined-functions.ipynb
index 3e681dcff94..ce48031a675 100644
--- a/website/docs/topics/code-execution/user-defined-functions.ipynb
+++ b/website/docs/topics/code-execution/user-defined-functions.ipynb
@@ -6,14 +6,14 @@
    "source": [
     "# User Defined Functions\n",
     "\n",
-    "```mdx\n",
+    "````{=mdx}\n",
     ":::note\n",
     "This is experimental and not *yet* supported by all executors. At this stage only [`LocalCommandLineCodeExecutor`](/docs/reference/coding/local_commandline_code_executor#localcommandlinecodeexecutor) is supported.\n",
     "\n",
     "\n",
     "Currently, the method of registering tools and using this feature are different. We would like to unify them. See Github issue [here](https://github.com/microsoft/autogen/issues/2101)\n",
     ":::\n",
-    "```\n",
+    "````\n",
     "\n",
     "User defined functions allow you to define Python functions in your AutoGen program and then provide these to be used by your executor. This allows you to provide your agents with tools without using traditional tool calling APIs. Currently only Python is supported for this feature.\n",
     "\n",
@@ -26,11 +26,11 @@
     "\n",
     "## Define the function\n",
     "\n",
-    "```mdx\n",
+    "````{=mdx}\n",
     ":::warning\n",
     "Keep in mind that the entire source code of these functions will be available to the executor. This means that you should not include any sensitive information in the function as an LLM agent may be able to access it.\n",
     ":::\n",
-    "```\n",
+    "````\n",
     "\n",
     "If the function does not require any external imports or dependencies then you can simply use the function. For example:\n"
    ]
@@ -52,11 +52,11 @@
    "source": [
     "This would be a valid standalone function.\n",
     "\n",
-    "```mdx\n",
+    "````{=mdx}\n",
     ":::tip\n",
     "Using type hints and docstrings are not required but are highly recommended. They will help the code writing agent understand the function and how to use it.\n",
     ":::\n",
-    "```\n",
+    "````\n",
     "\n",
     "If the function requires external imports or dependencies then you can use the `@with_requirements` decorator to specify the requirements. For example:"
    ]

From 491903c72d946a22aff57f6ceaefaa0dff0dacb2 Mon Sep 17 00:00:00 2001
From: Davor Runje <davor@airt.ai>
Date: Thu, 28 Mar 2024 15:26:01 +0100
Subject: [PATCH 19/27] Fix iostream on new thread (#2181)

* fixed get_stream in new thread by introducing a global default

* fixed get_stream in new thread by introducing a global default

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>
---
 autogen/io/__init__.py |  3 ++-
 autogen/io/base.py     | 35 ++++++++++++++++++++++++++++++-----
 test/io/test_base.py   | 23 ++++++++++++++++++++++-
 3 files changed, 54 insertions(+), 7 deletions(-)

diff --git a/autogen/io/__init__.py b/autogen/io/__init__.py
index 20d6d5a578f..6bb8a35680f 100644
--- a/autogen/io/__init__.py
+++ b/autogen/io/__init__.py
@@ -3,6 +3,7 @@
 from .websockets import IOWebsockets
 
 # Set the default input/output stream to the console
-IOStream._default_io_stream.set(IOConsole())
+IOStream.set_global_default(IOConsole())
+IOStream.set_default(IOConsole())
 
 __all__ = ("IOConsole", "IOStream", "InputStream", "OutputStream", "IOWebsockets")
diff --git a/autogen/io/base.py b/autogen/io/base.py
index 857d532e4f5..db25560e0e8 100644
--- a/autogen/io/base.py
+++ b/autogen/io/base.py
@@ -1,9 +1,12 @@
 from contextlib import contextmanager
 from contextvars import ContextVar
+import logging
 from typing import Any, Iterator, Optional, Protocol, runtime_checkable
 
 __all__ = ("OutputStream", "InputStream", "IOStream")
 
+logger = logging.getLogger(__name__)
+
 
 @runtime_checkable
 class OutputStream(Protocol):
@@ -39,6 +42,31 @@ def input(self, prompt: str = "", *, password: bool = False) -> str:
 class IOStream(InputStream, OutputStream, Protocol):
     """A protocol for input/output streams."""
 
+    # ContextVar must be used in multithreaded or async environments
+    _default_io_stream: ContextVar[Optional["IOStream"]] = ContextVar("default_iostream", default=None)
+    _default_io_stream.set(None)
+    _global_default: Optional["IOStream"] = None
+
+    @staticmethod
+    def set_global_default(stream: "IOStream") -> None:
+        """Set the default input/output stream.
+
+        Args:
+            stream (IOStream): The input/output stream to set as the default.
+        """
+        IOStream._global_default = stream
+
+    @staticmethod
+    def get_global_default() -> "IOStream":
+        """Get the default input/output stream.
+
+        Returns:
+            IOStream: The default input/output stream.
+        """
+        if IOStream._global_default is None:
+            raise RuntimeError("No global default IOStream has been set")
+        return IOStream._global_default
+
     @staticmethod
     def get_default() -> "IOStream":
         """Get the default input/output stream.
@@ -48,13 +76,10 @@ def get_default() -> "IOStream":
         """
         iostream = IOStream._default_io_stream.get()
         if iostream is None:
-            raise RuntimeError("No default IOStream has been set")
+            logger.warning("No default IOStream has been set, defaulting to IOConsole.")
+            return IOStream.get_global_default()
         return iostream
 
-    # ContextVar must be used in multithreaded or async environments
-    _default_io_stream: ContextVar[Optional["IOStream"]] = ContextVar("default_iostream")
-    _default_io_stream.set(None)
-
     @staticmethod
     @contextmanager
     def set_default(stream: Optional["IOStream"]) -> Iterator[None]:
diff --git a/test/io/test_base.py b/test/io/test_base.py
index a96f8cb3c79..ba05955164c 100644
--- a/test/io/test_base.py
+++ b/test/io/test_base.py
@@ -1,4 +1,5 @@
-from typing import Any
+from threading import Thread
+from typing import Any, List
 
 from autogen.io import IOConsole, IOStream, IOWebsockets
 
@@ -26,3 +27,23 @@ def input(self, prompt: str = "", *, password: bool = False) -> str:
             assert isinstance(IOStream.get_default(), MyIOStream)
 
         assert isinstance(IOStream.get_default(), IOConsole)
+
+    def test_get_default_on_new_thread(self) -> None:
+        exceptions: List[Exception] = []
+
+        def on_new_thread(exceptions: List[Exception] = exceptions) -> None:
+            try:
+                assert isinstance(IOStream.get_default(), IOConsole)
+            except Exception as e:
+                exceptions.append(e)
+
+        # create a new thread and run the function
+        thread = Thread(target=on_new_thread)
+
+        thread.start()
+
+        # get exception from the thread
+        thread.join()
+
+        if exceptions:
+            raise exceptions[0]

From 7f1c547525c0535dd6943ac76923082a2daf3e85 Mon Sep 17 00:00:00 2001
From: Jack Gerrits <jackgerrits@users.noreply.github.com>
Date: Thu, 28 Mar 2024 11:17:14 -0400
Subject: [PATCH 20/27] Add link for rendering notebooks docs on website
 (#2191)

---
 website/docs/Contribute.md | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/website/docs/Contribute.md b/website/docs/Contribute.md
index a6fc5b68039..c25d50b11cb 100644
--- a/website/docs/Contribute.md
+++ b/website/docs/Contribute.md
@@ -208,6 +208,11 @@ Then you can see the coverage report by
 `coverage report -m` or `coverage html`.
 
 ### Documentation
+
+#### How to get a notebook rendered on the website
+
+See [here](https://github.com/microsoft/autogen/blob/main/notebook/contributing.md#how-to-get-a-notebook-displayed-on-the-website) for instructions on how to get a notebook in the `notebook` directory rendered on the website.
+
 #### Build documentation locally
 1\. To build and test documentation locally, first install [Node.js](https://nodejs.org/en/download/). For example,
 ```bash

From e363080d7d8366861a9a5f2a226e4c2b64e484ce Mon Sep 17 00:00:00 2001
From: Wael Karkoub <wael.karkoub96@gmail.com>
Date: Thu, 28 Mar 2024 17:16:17 +0100
Subject: [PATCH 21/27] Transform Messages Capability (#1923)

* wip

* Adds docstrings

* fixed spellings

* wip

* fixed errors

* better class names

* adds tests

* added tests to workflow

* improved token counting

* improved notebook

* improved token counting in test

* improved docstrings

* fix inconsistencies

* changed by mistake

* fixed docstring

* fixed details

* improves tests + adds openai contrib test

* fix spelling oai contrib test

* clearer docstrings

* remove repeated docstr

* improved notebook

* adds metadata to notebook

* Improve outline and description (#2125)

* better dir structure

* clip max tokens to allowed tokens

* more accurate comments/docstrs

* add deperecation warning

* fix front matter desc

* add deperecation warning notebook

* undo local notebook settings changes

* format notebook

* format workflow

---------

Co-authored-by: gagb <gagb@users.noreply.github.com>
---
 .github/workflows/contrib-tests.yml           |  37 ++
 .../contrib/capabilities/context_handling.py  |  17 +-
 .../capabilities/transform_messages.py        |  90 ++++
 .../contrib/capabilities/transforms.py        | 210 ++++++++
 ...hat_capability_long_context_handling.ipynb |   4 +
 notebook/agentchat_transform_messages.ipynb   | 502 ++++++++++++++++++
 .../capabilities/test_transform_messages.py   | 154 ++++++
 7 files changed, 1010 insertions(+), 4 deletions(-)
 create mode 100644 autogen/agentchat/contrib/capabilities/transform_messages.py
 create mode 100644 autogen/agentchat/contrib/capabilities/transforms.py
 create mode 100644 notebook/agentchat_transform_messages.ipynb
 create mode 100644 test/agentchat/contrib/capabilities/test_transform_messages.py

diff --git a/.github/workflows/contrib-tests.yml b/.github/workflows/contrib-tests.yml
index ced35dc115b..942c36ef523 100644
--- a/.github/workflows/contrib-tests.yml
+++ b/.github/workflows/contrib-tests.yml
@@ -291,3 +291,40 @@ jobs:
         with:
           file: ./coverage.xml
           flags: unittests
+
+  TransformMessages:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      fail-fast: false
+      matrix:
+        os: [ubuntu-latest, macos-latest, windows-2019]
+        python-version: ["3.11"]
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install packages and dependencies for all tests
+        run: |
+          python -m pip install --upgrade pip wheel
+          pip install pytest
+      - name: Install packages and dependencies for Transform Messages
+        run: |
+          pip install -e .
+      - name: Set AUTOGEN_USE_DOCKER based on OS
+        shell: bash
+        run: |
+          if [[ ${{ matrix.os }} != ubuntu-latest ]]; then
+            echo "AUTOGEN_USE_DOCKER=False" >> $GITHUB_ENV
+          fi
+      - name: Coverage
+        run: |
+          pip install coverage>=5.3
+          coverage run -a -m pytest test/agentchat/contrib/capabilities/test_transform_messages.py --skip-openai
+          coverage xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3
+        with:
+          file: ./coverage.xml
+          flags: unittest
diff --git a/autogen/agentchat/contrib/capabilities/context_handling.py b/autogen/agentchat/contrib/capabilities/context_handling.py
index 1510ae5fcd6..dcdf307587c 100644
--- a/autogen/agentchat/contrib/capabilities/context_handling.py
+++ b/autogen/agentchat/contrib/capabilities/context_handling.py
@@ -1,9 +1,18 @@
 import sys
-from termcolor import colored
-from typing import Dict, Optional, List
-from autogen import ConversableAgent
-from autogen import token_count_utils
+from typing import Dict, List, Optional
+from warnings import warn
+
 import tiktoken
+from termcolor import colored
+
+from autogen import ConversableAgent, token_count_utils
+
+warn(
+    "Context handling with TransformChatHistory is deprecated. "
+    "Please use TransformMessages from autogen/agentchat/contrib/capabilities/transform_messages.py instead.",
+    DeprecationWarning,
+    stacklevel=2,
+)
 
 
 class TransformChatHistory:
diff --git a/autogen/agentchat/contrib/capabilities/transform_messages.py b/autogen/agentchat/contrib/capabilities/transform_messages.py
new file mode 100644
index 00000000000..db12fe688ed
--- /dev/null
+++ b/autogen/agentchat/contrib/capabilities/transform_messages.py
@@ -0,0 +1,90 @@
+import copy
+from typing import Dict, List
+
+from termcolor import colored
+
+from autogen import ConversableAgent
+from .transforms import MessageTransform
+
+
+class TransformMessages:
+    """Agent capability for transforming messages before reply generation.
+
+    This capability allows you to apply a series of message transformations to
+    a ConversableAgent's incoming messages before they are processed for response
+    generation. This is useful for tasks such as:
+
+    - Limiting the number of messages considered for context.
+    - Truncating messages to meet token limits.
+    - Filtering sensitive information.
+    - Customizing message formatting.
+
+    To use `TransformMessages`:
+
+    1. Create message transformations (e.g., `MessageHistoryLimiter`, `MessageTokenLimiter`).
+    2. Instantiate `TransformMessages` with a list of these transformations.
+    3. Add the `TransformMessages` instance to your `ConversableAgent` using `add_to_agent`.
+
+    NOTE: Order of message transformations is important. You could get different results based on
+        the order of transformations.
+
+    Example:
+        ```python
+        from agentchat import ConversableAgent
+        from agentchat.contrib.capabilities import TransformMessages, MessageHistoryLimiter, MessageTokenLimiter
+
+        max_messages = MessageHistoryLimiter(max_messages=2)
+        truncate_messages = MessageTokenLimiter(max_tokens=500)
+        transform_messages = TransformMessages(transforms=[max_messages, truncate_messages])
+
+        agent = ConversableAgent(...)
+        transform_messages.add_to_agent(agent)
+        ```
+    """
+
+    def __init__(self, *, transforms: List[MessageTransform] = []):
+        """
+        Args:
+            transforms: A list of message transformations to apply.
+        """
+        self._transforms = transforms
+
+    def add_to_agent(self, agent: ConversableAgent):
+        """Adds the message transformations capability to the specified ConversableAgent.
+
+        This function performs the following modifications to the agent:
+
+        1. Registers a hook that automatically transforms all messages before they are processed for
+            response generation.
+        """
+        agent.register_hook(hookable_method="process_all_messages_before_reply", hook=self._transform_messages)
+
+    def _transform_messages(self, messages: List[Dict]) -> List[Dict]:
+        temp_messages = copy.deepcopy(messages)
+        system_message = None
+
+        if messages[0]["role"] == "system":
+            system_message = copy.deepcopy(messages[0])
+            temp_messages.pop(0)
+
+        for transform in self._transforms:
+            temp_messages = transform.apply_transform(temp_messages)
+
+        if system_message:
+            temp_messages.insert(0, system_message)
+
+        self._print_stats(messages, temp_messages)
+
+        return temp_messages
+
+    def _print_stats(self, pre_transform_messages: List[Dict], post_transform_messages: List[Dict]):
+        pre_transform_messages_len = len(pre_transform_messages)
+        post_transform_messages_len = len(post_transform_messages)
+
+        if pre_transform_messages_len < post_transform_messages_len:
+            print(
+                colored(
+                    f"Number of messages reduced from {pre_transform_messages_len} to {post_transform_messages_len}.",
+                    "yellow",
+                )
+            )
diff --git a/autogen/agentchat/contrib/capabilities/transforms.py b/autogen/agentchat/contrib/capabilities/transforms.py
new file mode 100644
index 00000000000..d87963ec82e
--- /dev/null
+++ b/autogen/agentchat/contrib/capabilities/transforms.py
@@ -0,0 +1,210 @@
+import sys
+from typing import Any, Dict, List, Optional, Protocol, Union
+
+import tiktoken
+from termcolor import colored
+
+from autogen import token_count_utils
+
+
+class MessageTransform(Protocol):
+    """Defines a contract for message transformation.
+
+    Classes implementing this protocol should provide an `apply_transform` method
+    that takes a list of messages and returns the transformed list.
+    """
+
+    def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+        """Applies a transformation to a list of messages.
+
+        Args:
+            messages: A list of dictionaries representing messages.
+
+        Returns:
+            A new list of dictionaries containing the transformed messages.
+        """
+        ...
+
+
+class MessageHistoryLimiter:
+    """Limits the number of messages considered by an agent for response generation.
+
+    This transform keeps only the most recent messages up to the specified maximum number of messages (max_messages).
+    It trims the conversation history by removing older messages, retaining only the most recent messages.
+    """
+
+    def __init__(self, max_messages: Optional[int] = None):
+        """
+        Args:
+            max_messages (None or int): Maximum number of messages to keep in the context.
+            Must be greater than 0 if not None.
+        """
+        self._validate_max_messages(max_messages)
+        self._max_messages = max_messages
+
+    def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+        """Truncates the conversation history to the specified maximum number of messages.
+
+        This method returns a new list containing the most recent messages up to the specified
+        maximum number of messages (max_messages). If max_messages is None, it returns the
+        original list of messages unmodified.
+
+        Args:
+            messages (List[Dict]): The list of messages representing the conversation history.
+
+        Returns:
+            List[Dict]: A new list containing the most recent messages up to the specified maximum.
+        """
+        if self._max_messages is None:
+            return messages
+
+        return messages[-self._max_messages :]
+
+    def _validate_max_messages(self, max_messages: Optional[int]):
+        if max_messages is not None and max_messages < 1:
+            raise ValueError("max_messages must be None or greater than 1")
+
+
+class MessageTokenLimiter:
+    """Truncates messages to meet token limits for efficient processing and response generation.
+
+    This transformation applies two levels of truncation to the conversation history:
+
+    1. Truncates each individual message to the maximum number of tokens specified by max_tokens_per_message.
+    2. Truncates the overall conversation history to the maximum number of tokens specified by max_tokens.
+
+    NOTE: Tokens are counted using the encoder for the specified model. Different models may yield different token
+        counts for the same text.
+
+    NOTE: For multimodal LLMs, the token count may be inaccurate as it does not account for the non-text input
+        (e.g images).
+
+    The truncation process follows these steps in order:
+
+    1. Messages are processed in reverse order (newest to oldest).
+    2. Individual messages are truncated based on max_tokens_per_message. For multimodal messages containing both text
+        and other types of content, only the text content is truncated.
+    3. The overall conversation history is truncated based on the max_tokens limit. Once the accumulated token count
+        exceeds this limit, the current message being processed as well as any remaining messages are discarded.
+    4. The truncated conversation history is reconstructed by prepending the messages to a new list to preserve the
+        original message order.
+    """
+
+    def __init__(
+        self,
+        max_tokens_per_message: Optional[int] = None,
+        max_tokens: Optional[int] = None,
+        model: str = "gpt-3.5-turbo-0613",
+    ):
+        """
+        Args:
+            max_tokens_per_message (None or int): Maximum number of tokens to keep in each message.
+                Must be greater than or equal to 0 if not None.
+            max_tokens (Optional[int]): Maximum number of tokens to keep in the chat history.
+                Must be greater than or equal to 0 if not None.
+            model (str): The target OpenAI model for tokenization alignment.
+        """
+        self._model = model
+        self._max_tokens_per_message = self._validate_max_tokens(max_tokens_per_message)
+        self._max_tokens = self._validate_max_tokens(max_tokens)
+
+    def apply_transform(self, messages: List[Dict]) -> List[Dict]:
+        """Applies token truncation to the conversation history.
+
+        Args:
+            messages (List[Dict]): The list of messages representing the conversation history.
+
+        Returns:
+            List[Dict]: A new list containing the truncated messages up to the specified token limits.
+        """
+        assert self._max_tokens_per_message is not None
+        assert self._max_tokens is not None
+
+        temp_messages = messages.copy()
+        processed_messages = []
+        processed_messages_tokens = 0
+
+        # calculate tokens for all messages
+        total_tokens = sum(_count_tokens(msg["content"]) for msg in temp_messages)
+
+        for msg in reversed(temp_messages):
+            msg["content"] = self._truncate_str_to_tokens(msg["content"])
+            msg_tokens = _count_tokens(msg["content"])
+
+            # If adding this message would exceed the token limit, discard it and all remaining messages
+            if processed_messages_tokens + msg_tokens > self._max_tokens:
+                break
+
+            # prepend the message to the list to preserve order
+            processed_messages_tokens += msg_tokens
+            processed_messages.insert(0, msg)
+
+        if total_tokens > processed_messages_tokens:
+            print(
+                colored(
+                    f"Truncated {total_tokens - processed_messages_tokens} tokens. Tokens reduced from {total_tokens} to {processed_messages_tokens}",
+                    "yellow",
+                )
+            )
+
+        return processed_messages
+
+    def _truncate_str_to_tokens(self, contents: Union[str, List]) -> Union[str, List]:
+        if isinstance(contents, str):
+            return self._truncate_tokens(contents)
+        elif isinstance(contents, list):
+            return self._truncate_multimodal_text(contents)
+        else:
+            raise ValueError(f"Contents must be a string or a list of dictionaries. Received type: {type(contents)}")
+
+    def _truncate_multimodal_text(self, contents: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
+        """Truncates text content within a list of multimodal elements, preserving the overall structure."""
+        tmp_contents = []
+        for content in contents:
+            if content["type"] == "text":
+                truncated_text = self._truncate_tokens(content["text"])
+                tmp_contents.append({"type": "text", "text": truncated_text})
+            else:
+                tmp_contents.append(content)
+        return tmp_contents
+
+    def _truncate_tokens(self, text: str) -> str:
+        encoding = tiktoken.encoding_for_model(self._model)  # Get the appropriate tokenizer
+
+        encoded_tokens = encoding.encode(text)
+        truncated_tokens = encoded_tokens[: self._max_tokens_per_message]
+        truncated_text = encoding.decode(truncated_tokens)  # Decode back to text
+
+        return truncated_text
+
+    def _validate_max_tokens(self, max_tokens: Optional[int] = None) -> Optional[int]:
+        if max_tokens is not None and max_tokens < 0:
+            raise ValueError("max_tokens and max_tokens_per_message must be None or greater than or equal to 0")
+
+        try:
+            allowed_tokens = token_count_utils.get_max_token_limit(self._model)
+        except Exception:
+            print(colored(f"Model {self._model} not found in token_count_utils.", "yellow"))
+            allowed_tokens = None
+
+        if max_tokens is not None and allowed_tokens is not None:
+            if max_tokens > allowed_tokens:
+                print(
+                    colored(
+                        f"Max token was set to {max_tokens}, but {self._model} can only accept {allowed_tokens} tokens. Capping it to {allowed_tokens}.",
+                        "yellow",
+                    )
+                )
+                return allowed_tokens
+
+        return max_tokens if max_tokens is not None else sys.maxsize
+
+
+def _count_tokens(content: Union[str, List[Dict[str, Any]]]) -> int:
+    token_count = 0
+    if isinstance(content, str):
+        token_count = token_count_utils.count_token(content)
+    elif isinstance(content, list):
+        for item in content:
+            token_count += _count_tokens(item.get("text", ""))
+    return token_count
diff --git a/notebook/agentchat_capability_long_context_handling.ipynb b/notebook/agentchat_capability_long_context_handling.ipynb
index 7d2ef12a561..4848990b1d3 100644
--- a/notebook/agentchat_capability_long_context_handling.ipynb
+++ b/notebook/agentchat_capability_long_context_handling.ipynb
@@ -6,6 +6,10 @@
    "source": [
     "# Handling A Long Context via `TransformChatHistory`\n",
     "\n",
+    "<div class=\"alert alert-warning\" role=\"alert\">\n",
+    "  <strong>Deprecation Notice:</strong> <code>TransformChatHistory</code> is no longer supported. Please use <code>TransformMessages</code> as the new standard method. For the latest examples, visit the notebook at <a href=\"https://github.com/microsoft/autogen/blob/main/notebook/agentchat_transform_messages.ipynb\" target=\"_blank\">notebook/agentchat_transform_messages.ipynb</a>.\n",
+    "</div>\n",
+    "\n",
     "This notebook illustrates how you can use the `TransformChatHistory` capability to give any `Conversable` agent an ability to handle a long context. \n",
     "\n",
     "````{=mdx}\n",
diff --git a/notebook/agentchat_transform_messages.ipynb b/notebook/agentchat_transform_messages.ipynb
new file mode 100644
index 00000000000..37b58b42748
--- /dev/null
+++ b/notebook/agentchat_transform_messages.ipynb
@@ -0,0 +1,502 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b7549a27-bc4a-4609-bb25-cc7d95cf8c23",
+   "metadata": {},
+   "source": [
+    "# Preprocessing Chat History with `TransformMessages`\n",
+    "\n",
+    "## Introduction\n",
+    "This notebook illustrates how to use `TransforMessages` give any `ConversableAgent` the ability to handle long contexts, sensitive data, and more.\n",
+    "\n",
+    "````{=mdx}\n",
+    ":::info Requirements\n",
+    "Install `pyautogen`:\n",
+    "```bash\n",
+    "pip install pyautogen\n",
+    "```\n",
+    "\n",
+    "For more information, please refer to the [installation guide](/docs/installation/).\n",
+    ":::\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "47773f79-c0fd-4993-bc6e-3d1a57690118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pprint\n",
+    "import copy\n",
+    "import re\n",
+    "\n",
+    "import autogen\n",
+    "from autogen.agentchat.contrib.capabilities import transform_messages, transforms\n",
+    "from typing import Dict, List"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "9f09246b-a7d0-4238-b62c-1e72c7d815b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_list = autogen.config_list_from_json(\n",
+    "    env_or_file=\"OAI_CONFIG_LIST\",\n",
+    ")\n",
+    "# Define your llm config\n",
+    "llm_config = {\"config_list\": config_list}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ea68962a-048d-42e9-9fca-cd944c56184d",
+   "metadata": {},
+   "source": [
+    "````{=mdx}\n",
+    ":::tip\n",
+    "Learn more about configuring LLMs for agents [here](/docs/topics/llm_configuration).\n",
+    ":::\n",
+    "````"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "84d0e5ad-8b35-4b30-847e-4723e9c76f7c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Define your agent; the user proxy and an assistant\n",
+    "assistant = autogen.AssistantAgent(\n",
+    "    \"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    ")\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    \"user_proxy\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    is_termination_msg=lambda x: \"TERMINATE\" in x.get(\"content\", \"\"),\n",
+    "    max_consecutive_auto_reply=10,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "180aa953-45be-469a-a94f-0ed0b4ef5ddf",
+   "metadata": {},
+   "source": [
+    "## Handling Long Contexts\n",
+    "\n",
+    "Imagine a scenario where the LLM generates an extensive amount of text, surpassing the token limit imposed by your API provider. To address this issue, you can leverage `TransformMessages` along with its constituent transformations, `MessageHistoryLimiter` and `MessageTokenLimiter`.\n",
+    "\n",
+    "- `MessageHistoryLimiter`: You can restrict the total number of messages considered as context history. This transform is particularly useful when you want to limit the conversational context to a specific number of recent messages, ensuring efficient processing and response generation.\n",
+    "- `MessageTokenLimiter`: Enables you to cap the total number of tokens, either on a per-message basis or across the entire context history (or both). This transformation is invaluable when you need to adhere to strict token limits imposed by your API provider, preventing unnecessary costs or errors caused by exceeding the allowed token count."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "34b943a2-ec58-41bc-a449-d9118c4bbdea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Limit the message history to the 3 most recent messages\n",
+    "max_msg_transfrom = transforms.MessageHistoryLimiter(max_messages=3)\n",
+    "\n",
+    "# Limit the token limit per message to 10 tokens\n",
+    "token_limit_transform = transforms.MessageTokenLimiter(max_tokens_per_message=3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "679c1026-4e1b-4c07-85cc-86594cc0b87b",
+   "metadata": {},
+   "source": [
+    "## Example 1: Limiting number of messages\n",
+    "Let's take a look at how these transformations will effect the messages. Below we see that by applying the `MessageHistoryLimiter`, we can see that we limited the context history to the 3 most recent messages."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "61a2ead4-5f8b-4108-b1f0-3b51b41e2231",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[{'content': 'how', 'role': 'user'},\n",
+      " {'content': [{'text': 'are you doing?', 'type': 'text'}], 'role': 'assistant'},\n",
+      " {'content': 'very very very very very very long string', 'role': 'user'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "messages = [\n",
+    "    {\"role\": \"user\", \"content\": \"hello\"},\n",
+    "    {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"there\"}]},\n",
+    "    {\"role\": \"user\", \"content\": \"how\"},\n",
+    "    {\"role\": \"assistant\", \"content\": [{\"type\": \"text\", \"text\": \"are you doing?\"}]},\n",
+    "    {\"role\": \"user\", \"content\": \"very very very very very very long string\"},\n",
+    "]\n",
+    "\n",
+    "processed_messages = max_msg_transfrom.apply_transform(copy.deepcopy(messages))\n",
+    "pprint.pprint(processed_messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "610739af-b812-404e-82d2-b3ed796b8b6c",
+   "metadata": {},
+   "source": [
+    "## Example 2: Limiting number of tokens\n",
+    "\n",
+    "Now let's test limiting the number of tokens in messages. We can see that we can limit the number of tokens to 3, which is equivalent to 3 words in this instance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "739dd260-fa95-4e5d-ae84-9cb7f40de975",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33mTruncated 6 tokens. Tokens reduced from 15 to 9\u001b[0m\n",
+      "[{'content': 'hello', 'role': 'user'},\n",
+      " {'content': [{'text': 'there', 'type': 'text'}], 'role': 'assistant'},\n",
+      " {'content': 'how', 'role': 'user'},\n",
+      " {'content': [{'text': 'are you doing', 'type': 'text'}], 'role': 'assistant'},\n",
+      " {'content': 'very very very', 'role': 'user'}]\n"
+     ]
+    }
+   ],
+   "source": [
+    "processed_messages = token_limit_transform.apply_transform(copy.deepcopy(messages))\n",
+    "\n",
+    "pprint.pprint(processed_messages)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "35fa2844-bd83-42ac-8275-959f093b7bc7",
+   "metadata": {},
+   "source": [
+    "## Example 3: Combining transformations\n",
+    "\n",
+    "Let's test these transforms with agents (the upcoming test is replicated from the agentchat_capability_long_context_handling notebook). We will see that the agent without the capability to handle long context will result in an error, while the agent with that capability will have no issues."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "80e53623-2830-41b7-8ae2-bf3668071657",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\n",
+      "plot and save a graph of x^2 from -10 to 10\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "Encountered an error with the base assistant\n",
+      "Error code: 429 - {'error': {'message': 'Request too large for gpt-3.5-turbo in organization org-U58JZBsXUVAJPlx2MtPYmdx1 on tokens per min (TPM): Limit 60000, Requested 1252546. The input or output tokens must be reduced in order to run successfully. Visit https://platform.openai.com/account/rate-limits to learn more.', 'type': 'tokens', 'param': None, 'code': 'rate_limit_exceeded'}}\n",
+      "\n",
+      "\n",
+      "\n",
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\n",
+      "plot and save a graph of x^2 from -10 to 10\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mTruncated 3804 tokens. Tokens reduced from 4019 to 215\u001b[0m\n",
+      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
+      "\n",
+      "To plot the graph of \\( x^2 \\) from -10 to 10 and save it, we can use Python with the matplotlib library. Here is the code to achieve this:\n",
+      "\n",
+      "```python\n",
+      "# filename: plot_graph.py\n",
+      "import matplotlib.pyplot as plt\n",
+      "import numpy as np\n",
+      "\n",
+      "x = np.linspace(-10, 10, 100)\n",
+      "y = x**2\n",
+      "\n",
+      "plt.plot(x, y)\n",
+      "plt.xlabel('x')\n",
+      "plt.ylabel('x^2')\n",
+      "plt.title('Graph of x^2')\n",
+      "plt.grid(True)\n",
+      "plt.savefig('x_squared_graph.png')\n",
+      "plt.show()\n",
+      "```\n",
+      "\n",
+      "After executing this code, you should see the graph of \\( x^2 \\) displayed and saved as `x_squared_graph.png`.\n",
+      "\n",
+      "Please make sure you have matplotlib installed. If not, you can install it using pip:\n",
+      "\n",
+      "```sh\n",
+      "pip install matplotlib\n",
+      "```\n",
+      "\n",
+      "Go ahead and execute the Python script provided above to plot and save the graph of \\( x^2 \\). Let me know if you encounter any issues.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is sh)...\u001b[0m\n",
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: \n",
+      "Figure(640x480)\n",
+      "\n",
+      "Requirement already satisfied: matplotlib in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (3.8.2)\n",
+      "Requirement already satisfied: contourpy>=1.0.1 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (1.2.0)\n",
+      "Requirement already satisfied: cycler>=0.10 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (0.12.1)\n",
+      "Requirement already satisfied: fonttools>=4.22.0 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (4.48.1)\n",
+      "Requirement already satisfied: kiwisolver>=1.3.1 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (1.4.5)\n",
+      "Requirement already satisfied: numpy<2,>=1.21 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (1.26.4)\n",
+      "Requirement already satisfied: packaging>=20.0 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (23.2)\n",
+      "Requirement already satisfied: pillow>=8 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (10.2.0)\n",
+      "Requirement already satisfied: pyparsing>=2.3.1 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (3.1.1)\n",
+      "Requirement already satisfied: python-dateutil>=2.7 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from matplotlib) (2.8.2)\n",
+      "Requirement already satisfied: six>=1.5 in /home/wael/workspaces/autogen/.venv/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33mTruncated 3435 tokens. Tokens reduced from 3700 to 265\u001b[0m\n",
+      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
+      "\n",
+      "The graph has been successfully created and saved. You can find the graph as a file named \"x_squared_plot.png\" in the directory where you ran the script. You can open and view this file to see the plotted graph of \\(x^2\\) from -10 to 10.\n",
+      "\n",
+      "TERMINATE\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "llm_config = {\n",
+    "    \"config_list\": [{\"model\": \"gpt-3.5-turbo\", \"api_key\": os.environ.get(\"OPENAI_API_KEY\")}],\n",
+    "}\n",
+    "\n",
+    "assistant_base = autogen.AssistantAgent(\n",
+    "    \"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    ")\n",
+    "\n",
+    "assistant_with_context_handling = autogen.AssistantAgent(\n",
+    "    \"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    ")\n",
+    "# suppose this capability is not available\n",
+    "context_handling = transform_messages.TransformMessages(\n",
+    "    transforms=[\n",
+    "        transforms.MessageHistoryLimiter(max_messages=10),\n",
+    "        transforms.MessageTokenLimiter(max_tokens=1000, max_tokens_per_message=50),\n",
+    "    ]\n",
+    ")\n",
+    "\n",
+    "context_handling.add_to_agent(assistant_with_context_handling)\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    \"user_proxy\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    is_termination_msg=lambda x: \"TERMINATE\" in x.get(\"content\", \"\"),\n",
+    "    code_execution_config={\n",
+    "        \"work_dir\": \"coding\",\n",
+    "        \"use_docker\": False,\n",
+    "    },\n",
+    "    max_consecutive_auto_reply=2,\n",
+    ")\n",
+    "\n",
+    "# suppose the chat history is large\n",
+    "# Create a very long chat history that is bound to cause a crash\n",
+    "# for gpt 3.5\n",
+    "for i in range(1000):\n",
+    "    # define a fake, very long messages\n",
+    "    assitant_msg = {\"role\": \"assistant\", \"content\": \"test \" * 1000}\n",
+    "    user_msg = {\"role\": \"user\", \"content\": \"\"}\n",
+    "\n",
+    "    assistant_base.send(assitant_msg, user_proxy, request_reply=False, silent=True)\n",
+    "    assistant_with_context_handling.send(assitant_msg, user_proxy, request_reply=False, silent=True)\n",
+    "    user_proxy.send(user_msg, assistant_base, request_reply=False, silent=True)\n",
+    "    user_proxy.send(user_msg, assistant_with_context_handling, request_reply=False, silent=True)\n",
+    "\n",
+    "try:\n",
+    "    user_proxy.initiate_chat(assistant_base, message=\"plot and save a graph of x^2 from -10 to 10\", clear_history=False)\n",
+    "except Exception as e:\n",
+    "    print(\"Encountered an error with the base assistant\")\n",
+    "    print(e)\n",
+    "    print(\"\\n\\n\")\n",
+    "\n",
+    "try:\n",
+    "    user_proxy.initiate_chat(\n",
+    "        assistant_with_context_handling, message=\"plot and save a graph of x^2 from -10 to 10\", clear_history=False\n",
+    "    )\n",
+    "except Exception as e:\n",
+    "    print(e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e380678-a923-43cb-91b1-f9c9e8deede2",
+   "metadata": {},
+   "source": [
+    "## Handling Sensitive Data\n",
+    "\n",
+    "You can use the `MessageTransform` protocol to create custom message transformations that redact sensitive data from the chat history. This is particularly useful when you want to ensure that sensitive information, such as API keys, passwords, or personal data, is not exposed in the chat history or logs.\n",
+    "\n",
+    "Now, we will create a custom message transform to detect any OpenAI API key and redact it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "74429344-3c0a-4057-aba3-27358fbf059c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# The transform must adhere to transform_messages.MessageTransform protocol.\n",
+    "class MessageRedact:\n",
+    "    def __init__(self):\n",
+    "        self._openai_key_pattern = r\"sk-([a-zA-Z0-9]{48})\"\n",
+    "        self._replacement_string = \"REDACTED\"\n",
+    "\n",
+    "    def apply_transform(self, messages: List[Dict]) -> List[Dict]:\n",
+    "        temp_messages = copy.deepcopy(messages)\n",
+    "\n",
+    "        for message in temp_messages:\n",
+    "            if isinstance(message[\"content\"], str):\n",
+    "                message[\"content\"] = re.sub(self._openai_key_pattern, self._replacement_string, message[\"content\"])\n",
+    "            elif isinstance(message[\"content\"], list):\n",
+    "                for item in message[\"content\"]:\n",
+    "                    if item[\"type\"] == \"text\":\n",
+    "                        item[\"text\"] = re.sub(self._openai_key_pattern, self._replacement_string, item[\"text\"])\n",
+    "        return temp_messages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "8a79c0b4-5ff8-49c5-b8a6-c54ca4c7cca2",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\n",
+      "What are the two API keys that I just provided\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[33massistant\u001b[0m (to user_proxy):\n",
+      "\n",
+      "To retrieve the two API keys you provided, I will display them individually in the output. \n",
+      "\n",
+      "Here is the first API key:\n",
+      "```python\n",
+      "# Display the first API key\n",
+      "print(\"API key 1 =\", \"REDACTED\")\n",
+      "```\n",
+      "\n",
+      "Here is the second API key:\n",
+      "```python\n",
+      "# Display the second API key\n",
+      "print(\"API key 2 =\", \"REDACTED\")\n",
+      "```\n",
+      "\n",
+      "Please run the code snippets to see the API keys. After that, I will mark this task as complete.\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
+      "\u001b[31m\n",
+      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
+      "\u001b[33muser_proxy\u001b[0m (to assistant):\n",
+      "\n",
+      "exitcode: 0 (execution succeeded)\n",
+      "Code output: \n",
+      "API key 1 = REDACTED\n",
+      "\n",
+      "API key 2 = REDACTED\n",
+      "\n",
+      "\n",
+      "--------------------------------------------------------------------------------\n"
+     ]
+    }
+   ],
+   "source": [
+    "assistant_with_redact = autogen.AssistantAgent(\n",
+    "    \"assistant\",\n",
+    "    llm_config=llm_config,\n",
+    "    max_consecutive_auto_reply=1,\n",
+    ")\n",
+    "# suppose this capability is not available\n",
+    "redact_handling = transform_messages.TransformMessages(transforms=[MessageRedact()])\n",
+    "\n",
+    "redact_handling.add_to_agent(assistant_with_redact)\n",
+    "\n",
+    "user_proxy = autogen.UserProxyAgent(\n",
+    "    \"user_proxy\",\n",
+    "    human_input_mode=\"NEVER\",\n",
+    "    max_consecutive_auto_reply=1,\n",
+    ")\n",
+    "\n",
+    "messages = [\n",
+    "    {\"content\": \"api key 1 = sk-7nwt00xv6fuegfu3gnwmhrgxvuc1cyrhxcq1quur9zvf05fy\"},  # Don't worry, randomly generated\n",
+    "    {\"content\": [{\"type\": \"text\", \"text\": \"API key 2 = sk-9wi0gf1j2rz6utaqd3ww3o6c1h1n28wviypk7bd81wlj95an\"}]},\n",
+    "]\n",
+    "\n",
+    "for message in messages:\n",
+    "    user_proxy.send(message, assistant_with_redact, request_reply=False, silent=True)\n",
+    "\n",
+    "result = user_proxy.initiate_chat(\n",
+    "    assistant_with_redact, message=\"What are the two API keys that I just provided\", clear_history=False\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "front_matter": {
+   "description": "Preprocessing chat history with `TransformMessages`",
+   "tags": [
+    "long context handling",
+    "capability"
+   ]
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/test/agentchat/contrib/capabilities/test_transform_messages.py b/test/agentchat/contrib/capabilities/test_transform_messages.py
new file mode 100644
index 00000000000..c5b7c1dcf2d
--- /dev/null
+++ b/test/agentchat/contrib/capabilities/test_transform_messages.py
@@ -0,0 +1,154 @@
+import copy
+import os
+import sys
+import tempfile
+from typing import Any, Dict, List, Union
+
+import pytest
+
+import autogen
+from autogen import token_count_utils
+from autogen.agentchat.contrib.capabilities.transform_messages import TransformMessages
+from autogen.agentchat.contrib.capabilities.transforms import MessageHistoryLimiter, MessageTokenLimiter
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "../../.."))
+from conftest import skip_openai  # noqa: E402
+
+sys.path.append(os.path.join(os.path.dirname(__file__), "..", ".."))
+from test_assistant_agent import KEY_LOC, OAI_CONFIG_LIST  # noqa: E402
+
+
+def _count_tokens(content: Union[str, List[Dict[str, Any]]]) -> int:
+    token_count = 0
+    if isinstance(content, str):
+        token_count = token_count_utils.count_token(content)
+    elif isinstance(content, list):
+        for item in content:
+            token_count += _count_tokens(item.get("text", ""))
+    return token_count
+
+
+def test_limit_token_transform():
+    """
+    Test the TokenLimitTransform capability.
+    """
+
+    messages = [
+        {"role": "user", "content": "short string"},
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": "very very very very very very very very long string"}],
+        },
+    ]
+
+    # check if token limit per message is not exceeded.
+    max_tokens_per_message = 5
+    token_limit_transform = MessageTokenLimiter(max_tokens_per_message=max_tokens_per_message)
+    transformed_messages = token_limit_transform.apply_transform(copy.deepcopy(messages))
+
+    for message in transformed_messages:
+        assert _count_tokens(message["content"]) <= max_tokens_per_message
+
+    # check if total token limit is not exceeded.
+    max_tokens = 10
+    token_limit_transform = MessageTokenLimiter(max_tokens=max_tokens)
+    transformed_messages = token_limit_transform.apply_transform(copy.deepcopy(messages))
+
+    token_count = 0
+    for message in transformed_messages:
+        token_count += _count_tokens(message["content"])
+
+    assert token_count <= max_tokens
+    assert len(transformed_messages) <= len(messages)
+
+    # check if token limit per message works nicely with total token limit.
+    token_limit_transform = MessageTokenLimiter(max_tokens=max_tokens, max_tokens_per_message=max_tokens_per_message)
+
+    transformed_messages = token_limit_transform.apply_transform(copy.deepcopy(messages))
+
+    token_count = 0
+    for message in transformed_messages:
+        token_count_local = _count_tokens(message["content"])
+        token_count += token_count_local
+        assert token_count_local <= max_tokens_per_message
+
+    assert token_count <= max_tokens
+    assert len(transformed_messages) <= len(messages)
+
+
+def test_max_message_history_length_transform():
+    """
+    Test the MessageHistoryLimiter capability to limit the number of messages.
+    """
+    messages = [
+        {"role": "user", "content": "hello"},
+        {"role": "assistant", "content": [{"type": "text", "text": "there"}]},
+        {"role": "user", "content": "how"},
+        {"role": "assistant", "content": [{"type": "text", "text": "are you doing?"}]},
+    ]
+
+    max_messages = 2
+    messages_limiter = MessageHistoryLimiter(max_messages=max_messages)
+    transformed_messages = messages_limiter.apply_transform(copy.deepcopy(messages))
+
+    assert len(transformed_messages) == max_messages
+    assert transformed_messages == messages[max_messages:]
+
+
+@pytest.mark.skipif(skip_openai, reason="Requested to skip openai test.")
+def test_transform_messages_capability():
+    """Test the TransformMessages capability to handle long contexts.
+
+    This test is a replica of test_transform_chat_history_with_agents in test_context_handling.py
+    """
+    with tempfile.TemporaryDirectory() as temp_dir:
+        config_list = autogen.config_list_from_json(
+            OAI_CONFIG_LIST,
+            KEY_LOC,
+            filter_dict={
+                "model": "gpt-3.5-turbo",
+            },
+        )
+
+        assistant = autogen.AssistantAgent(
+            "assistant", llm_config={"config_list": config_list}, max_consecutive_auto_reply=1
+        )
+
+        context_handling = TransformMessages(
+            transforms=[
+                MessageHistoryLimiter(max_messages=10),
+                MessageTokenLimiter(max_tokens=10, max_tokens_per_message=5),
+            ]
+        )
+        context_handling.add_to_agent(assistant)
+        user = autogen.UserProxyAgent(
+            "user",
+            code_execution_config={"work_dir": temp_dir},
+            human_input_mode="NEVER",
+            is_termination_msg=lambda x: "TERMINATE" in x.get("content", ""),
+            max_consecutive_auto_reply=1,
+        )
+
+        # Create a very long chat history that is bound to cause a crash
+        # for gpt 3.5
+        for i in range(1000):
+            assitant_msg = {"role": "assistant", "content": "test " * 1000}
+            user_msg = {"role": "user", "content": ""}
+
+            assistant.send(assitant_msg, user, request_reply=False)
+            user.send(user_msg, assistant, request_reply=False)
+
+        try:
+            user.initiate_chat(
+                assistant,
+                message="Plot a chart of nvidia and tesla stock prices for the last 5 years",
+                clear_history=False,
+            )
+        except Exception as e:
+            assert False, f"Chat initiation failed with error {str(e)}"
+
+
+if __name__ == "__main__":
+    test_limit_token_transform()
+    test_max_message_history_length_transform()
+    test_transform_messages_capability()

From 1a09005fcdff73896f3fcd67f19340931e9843fc Mon Sep 17 00:00:00 2001
From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com>
Date: Thu, 28 Mar 2024 13:52:26 -0400
Subject: [PATCH 22/27] Bump express from 4.18.2 to 4.19.2 in /website (#2157)

Bumps [express](https://github.com/expressjs/express) from 4.18.2 to 4.19.2.
- [Release notes](https://github.com/expressjs/express/releases)
- [Changelog](https://github.com/expressjs/express/blob/master/History.md)
- [Commits](https://github.com/expressjs/express/compare/4.18.2...4.19.2)

---
updated-dependencies:
- dependency-name: express
  dependency-type: indirect
...

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
---
 website/yarn.lock | 40 ++++++++++++++++++++--------------------
 1 file changed, 20 insertions(+), 20 deletions(-)

diff --git a/website/yarn.lock b/website/yarn.lock
index cc98c129ee9..df98d5d5bd0 100644
--- a/website/yarn.lock
+++ b/website/yarn.lock
@@ -3434,13 +3434,13 @@ binary-extensions@^2.0.0:
   resolved "https://registry.yarnpkg.com/binary-extensions/-/binary-extensions-2.2.0.tgz#75f502eeaf9ffde42fc98829645be4ea76bd9e2d"
   integrity sha512-jDctJ/IVQbZoJykoeHbhXpOlNBqGNcwXJKJog42E5HDPUwQTSdjCHdihjj0DlnheQ7blbT6dHOafNAiS8ooQKA==
 
-body-parser@1.20.1:
-  version "1.20.1"
-  resolved "https://registry.yarnpkg.com/body-parser/-/body-parser-1.20.1.tgz#b1812a8912c195cd371a3ee5e66faa2338a5c668"
-  integrity sha512-jWi7abTbYwajOytWCQc37VulmWiRae5RyTpaCyDcS5/lMdtwSz5lOpDE67srw/HYe35f1z3fDQw+3txg7gNtWw==
+body-parser@1.20.2:
+  version "1.20.2"
+  resolved "https://registry.yarnpkg.com/body-parser/-/body-parser-1.20.2.tgz#6feb0e21c4724d06de7ff38da36dad4f57a747fd"
+  integrity sha512-ml9pReCu3M61kGlqoTm2umSXTlRTuGTx0bfYj+uIUKKYycG5NtSbeetV3faSU6R7ajOPw0g/J1PvK4qNy7s5bA==
   dependencies:
     bytes "3.1.2"
-    content-type "~1.0.4"
+    content-type "~1.0.5"
     debug "2.6.9"
     depd "2.0.0"
     destroy "1.2.0"
@@ -3448,7 +3448,7 @@ body-parser@1.20.1:
     iconv-lite "0.4.24"
     on-finished "2.4.1"
     qs "6.11.0"
-    raw-body "2.5.1"
+    raw-body "2.5.2"
     type-is "~1.6.18"
     unpipe "1.0.0"
 
@@ -3926,7 +3926,7 @@ content-disposition@0.5.4:
   dependencies:
     safe-buffer "5.2.1"
 
-content-type@~1.0.4:
+content-type@~1.0.4, content-type@~1.0.5:
   version "1.0.5"
   resolved "https://registry.yarnpkg.com/content-type/-/content-type-1.0.5.tgz#8b773162656d1d1086784c8f23a54ce6d73d7918"
   integrity sha512-nTjqfcBFEipKdXCv4YDQWCfmcLZKm81ldF0pAopTvyrFGVbcR6P/VAAd5G7N+0tTr8QqiU0tFadD6FK4NtJwOA==
@@ -3941,10 +3941,10 @@ cookie-signature@1.0.6:
   resolved "https://registry.yarnpkg.com/cookie-signature/-/cookie-signature-1.0.6.tgz#e303a882b342cc3ee8ca513a79999734dab3ae2c"
   integrity sha512-QADzlaHc8icV8I7vbaJXJwod9HWYp8uCqf1xa4OfNu1T7JVxQIrUgOWtHdNDtPiywmFbiS12VjotIXLrKM3orQ==
 
-cookie@0.5.0:
-  version "0.5.0"
-  resolved "https://registry.yarnpkg.com/cookie/-/cookie-0.5.0.tgz#d1f5d71adec6558c58f389987c366aa47e994f8b"
-  integrity sha512-YZ3GUyn/o8gfKJlnlX7g7xq4gyO6OSuhGPKaaGssGB2qgDUS0gPgtTvoyZLTt9Ab6dC4hfc9dV5arkvc/OCmrw==
+cookie@0.6.0:
+  version "0.6.0"
+  resolved "https://registry.yarnpkg.com/cookie/-/cookie-0.6.0.tgz#2798b04b071b0ecbff0dbb62a505a8efa4e19051"
+  integrity sha512-U71cyTamuh1CRNCfpGY6to28lxvNwPG4Guz/EVjgf3Jmzv0vlDp1atT9eS5dDjMYHucpHbWns6Lwf3BKz6svdw==
 
 copy-text-to-clipboard@^3.2.0:
   version "3.2.0"
@@ -4813,16 +4813,16 @@ execa@^5.0.0:
     strip-final-newline "^2.0.0"
 
 express@^4.17.3:
-  version "4.18.2"
-  resolved "https://registry.yarnpkg.com/express/-/express-4.18.2.tgz#3fabe08296e930c796c19e3c516979386ba9fd59"
-  integrity sha512-5/PsL6iGPdfQ/lKM1UuielYgv3BUoJfz1aUwU9vHZ+J7gyvwdQXFEBIEIaxeGf0GIcreATNyBExtalisDbuMqQ==
+  version "4.19.2"
+  resolved "https://registry.yarnpkg.com/express/-/express-4.19.2.tgz#e25437827a3aa7f2a827bc8171bbbb664a356465"
+  integrity sha512-5T6nhjsT+EOMzuck8JjBHARTHfMht0POzlA60WV2pMD3gyXw2LZnZ+ueGdNxG+0calOJcWKbpFcuzLZ91YWq9Q==
   dependencies:
     accepts "~1.3.8"
     array-flatten "1.1.1"
-    body-parser "1.20.1"
+    body-parser "1.20.2"
     content-disposition "0.5.4"
     content-type "~1.0.4"
-    cookie "0.5.0"
+    cookie "0.6.0"
     cookie-signature "1.0.6"
     debug "2.6.9"
     depd "2.0.0"
@@ -8301,10 +8301,10 @@ range-parser@^1.2.1, range-parser@~1.2.1:
   resolved "https://registry.yarnpkg.com/range-parser/-/range-parser-1.2.1.tgz#3cf37023d199e1c24d1a55b84800c2f3e6468031"
   integrity sha512-Hrgsx+orqoygnmhFbKaHE6c296J+HTAQXoxEF6gNupROmmGJRoyzfG3ccAveqCBrwr/2yxQ5BVd/GTl5agOwSg==
 
-raw-body@2.5.1:
-  version "2.5.1"
-  resolved "https://registry.yarnpkg.com/raw-body/-/raw-body-2.5.1.tgz#fe1b1628b181b700215e5fd42389f98b71392857"
-  integrity sha512-qqJBtEyVgS0ZmPGdCFPWJ3FreoqvG4MVQln/kCgF7Olq95IbOp0/BWyMwbdtn4VTvkM8Y7khCQ2Xgk/tcrCXig==
+raw-body@2.5.2:
+  version "2.5.2"
+  resolved "https://registry.yarnpkg.com/raw-body/-/raw-body-2.5.2.tgz#99febd83b90e08975087e8f1f9419a149366b68a"
+  integrity sha512-8zGqypfENjCIqGhgXToC8aB2r7YrBX+AQAfIPs/Mlk+BtPTztOvTS01NRW/3Eh60J+a48lt8qsCzirQ6loCVfA==
   dependencies:
     bytes "3.1.2"
     http-errors "2.0.0"

From 9b15aa6b6eee0448fca69f4f73042f0a36a4d22f Mon Sep 17 00:00:00 2001
From: Eric Zhu <ekzhu@users.noreply.github.com>
Date: Thu, 28 Mar 2024 16:26:22 -0700
Subject: [PATCH 23/27] add clarity analytics (#2201)

---
 website/docusaurus.config.js | 11 ++++++++++-
 website/package.json         |  1 +
 website/yarn.lock            | 15 ++++++++++++++-
 3 files changed, 25 insertions(+), 2 deletions(-)

diff --git a/website/docusaurus.config.js b/website/docusaurus.config.js
index d3fc424803a..0f2da9d6df2 100644
--- a/website/docusaurus.config.js
+++ b/website/docusaurus.config.js
@@ -137,6 +137,10 @@ module.exports = {
       textColor: '#091E42',
       isCloseable: true,
     },
+    /* Clarity Config */
+    clarity: {
+      ID: "lnxpe6skj1", // The Tracking ID provided by Clarity
+    }
   },
   presets: [
     [
@@ -223,6 +227,11 @@ module.exports = {
           }
         ],
       },
-    ]
+    ],
+    [
+      'docusaurus-plugin-clarity',
+      {
+      }
+    ],
   ],
 };
diff --git a/website/package.json b/website/package.json
index 5a882e32881..3eab1f28c77 100644
--- a/website/package.json
+++ b/website/package.json
@@ -33,6 +33,7 @@
     "@svgr/webpack": "^5.5.0",
     "antd": "^5.11.5",
     "clsx": "^1.1.1",
+    "docusaurus-plugin-clarity": "^2.1.0",
     "file-loader": "^6.2.0",
     "hast-util-is-element": "1.1.0",
     "joi": "17.6.0",
diff --git a/website/yarn.lock b/website/yarn.lock
index df98d5d5bd0..642bde204e4 100644
--- a/website/yarn.lock
+++ b/website/yarn.lock
@@ -1822,7 +1822,7 @@
     "@docusaurus/theme-search-algolia" "3.1.1"
     "@docusaurus/types" "3.1.1"
 
-"@docusaurus/react-loadable@5.5.2", "react-loadable@npm:@docusaurus/react-loadable@5.5.2":
+"@docusaurus/react-loadable@5.5.2":
   version "5.5.2"
   resolved "https://registry.yarnpkg.com/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz#81aae0db81ecafbdaee3651f12804580868fa6ce"
   integrity sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ==
@@ -4397,6 +4397,11 @@ dns-packet@^5.2.2:
   dependencies:
     "@leichtgewicht/ip-codec" "^2.0.1"
 
+docusaurus-plugin-clarity@^2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/docusaurus-plugin-clarity/-/docusaurus-plugin-clarity-2.1.0.tgz#f9e30b51f15b0f973f4f7d7d66d94be33a4dc937"
+  integrity sha512-8BHjDC+69m3yQTqBHJ1xFhijHqbCjJJs19bEVySX8DpnGWlPBDbGiujEqEAyIba0sSjYaJNfg/4n61k1NOm0lw==
+
 dom-converter@^0.2.0:
   version "0.2.0"
   resolved "https://registry.yarnpkg.com/dom-converter/-/dom-converter-0.2.0.tgz#6721a9daee2e293682955b6afe416771627bb768"
@@ -8760,6 +8765,14 @@ react-loadable-ssr-addon-v5-slorber@^1.0.1:
   dependencies:
     "@babel/runtime" "^7.10.3"
 
+"react-loadable@npm:@docusaurus/react-loadable@5.5.2":
+  version "5.5.2"
+  resolved "https://registry.yarnpkg.com/@docusaurus/react-loadable/-/react-loadable-5.5.2.tgz#81aae0db81ecafbdaee3651f12804580868fa6ce"
+  integrity sha512-A3dYjdBGuy0IGT+wyLIGIKLRE+sAk1iNk0f1HjNDysO7u8lhL4N3VEm+FAubmJbAztn94F7MxBTPmnixbiyFdQ==
+  dependencies:
+    "@types/react" "*"
+    prop-types "^15.6.2"
+
 react-router-config@^5.1.1:
   version "5.1.1"
   resolved "https://registry.yarnpkg.com/react-router-config/-/react-router-config-5.1.1.tgz#0f4263d1a80c6b2dc7b9c1902c9526478194a988"

From 6626f37659b8cf3051eeee34b6dbee2e514dbc0e Mon Sep 17 00:00:00 2001
From: Harsha S <sharsha315@gmail.com>
Date: Thu, 28 Mar 2024 11:54:38 +0000
Subject: [PATCH 24/27] Docstring formatting fix: Standardize docstrings to
 adhere to Google style guide, ensuring consistency and clarity. and also
 fixed the broken link for autogen/agentchat/chat.py

---
 autogen/agentchat/chat.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index 0fc321b4379..7125b150f0f 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -146,8 +146,8 @@ def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
     Args:
         chat_queue (List[Dict]): a list of dictionaries containing the information about the chats.
 
-        Each dictionary should contain the input arguments for [`ConversableAgent.initiate_chat`]
-        (/docs/reference/agentchat/conversable_agent#initiate_chat).
+        Each dictionary should contain the input arguments for
+        [`ConversableAgent.initiate_chat`](/docs/reference/agentchat/conversable_agent#initiate_chat).
         For example:
             - `"sender"` - the sender agent.
             - `"recipient"` - the recipient agent.

From 1bf17035116955ef00b3e9d7ee14939f5dd47e00 Mon Sep 17 00:00:00 2001
From: Harsha S <sharsha315@gmail.com>
Date: Fri, 29 Mar 2024 08:42:40 +0000
Subject: [PATCH 25/27] Docstring fix: Reformattted docstrings to adhere to
 Google style guide, nsuring consistency and clarity. For
 agentchat/contrib/retrieve_user_proxy_agent.py file

---
 .../contrib/retrieve_user_proxy_agent.py      | 46 +++++++++----------
 1 file changed, 23 insertions(+), 23 deletions(-)

diff --git a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
index fb217ad776a..1d029a5192c 100644
--- a/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
+++ b/autogen/agentchat/contrib/retrieve_user_proxy_agent.py
@@ -98,17 +98,17 @@ def __init__(
 
                 To use default config, set to None. Otherwise, set to a dictionary with the
                 following keys:
-                - task (Optional, str): the task of the retrieve chat. Possible values are
+                - `task` (Optional, str) - the task of the retrieve chat. Possible values are
                     "code", "qa" and "default". System prompt will be different for different tasks.
                      The default value is `default`, which supports both code and qa.
-                - client (Optional, chromadb.Client): the chromadb client. If key not provided, a
+                - `client` (Optional, chromadb.Client) - the chromadb client. If key not provided, a
                      default client `chromadb.Client()` will be used. If you want to use other
                      vector db, extend this class and override the `retrieve_docs` function.
-                - docs_path (Optional, Union[str, List[str]]): the path to the docs directory. It
+                - `docs_path` (Optional, Union[str, List[str]]) - the path to the docs directory. It
                      can also be the path to a single file, the url to a single file or a list
                      of directories, files and urls. Default is None, which works only if the
                      collection is already created.
-                - extra_docs (Optional, bool): when true, allows adding documents with unique IDs
+                - `extra_docs` (Optional, bool) - when true, allows adding documents with unique IDs
                     without overwriting existing ones; when false, it replaces existing documents
                     using default IDs, risking collection overwrite., when set to true it enables
                     the system to assign unique IDs starting from "length+i" for new document
@@ -117,58 +117,58 @@ def __init__(
                     By default, "extra_docs" is set to false, starting document IDs from zero.
                     This poses a risk as new documents might overwrite existing ones, potentially
                     causing unintended loss or alteration of data in the collection.
-                - collection_name (Optional, str): the name of the collection.
+                - `collection_name` (Optional, str) - the name of the collection.
                     If key not provided, a default name `autogen-docs` will be used.
-                - model (Optional, str): the model to use for the retrieve chat.
+                - `model` (Optional, str) - the model to use for the retrieve chat.
                     If key not provided, a default model `gpt-4` will be used.
-                - chunk_token_size (Optional, int): the chunk token size for the retrieve chat.
+                - `chunk_token_size` (Optional, int) - the chunk token size for the retrieve chat.
                     If key not provided, a default size `max_tokens * 0.4` will be used.
-                - context_max_tokens (Optional, int): the context max token size for the
+                - `context_max_tokens` (Optional, int) - the context max token size for the
                     retrieve chat.
                     If key not provided, a default size `max_tokens * 0.8` will be used.
-                - chunk_mode (Optional, str): the chunk mode for the retrieve chat. Possible values
+                - `chunk_mode` (Optional, str) - the chunk mode for the retrieve chat. Possible values
                     are "multi_lines" and "one_line". If key not provided, a default mode
                     `multi_lines` will be used.
-                - must_break_at_empty_line (Optional, bool): chunk will only break at empty line
+                - `must_break_at_empty_line` (Optional, bool) - chunk will only break at empty line
                     if True. Default is True.
                     If chunk_mode is "one_line", this parameter will be ignored.
-                - embedding_model (Optional, str): the embedding model to use for the retrieve chat.
+                - `embedding_model` (Optional, str) - the embedding model to use for the retrieve chat.
                     If key not provided, a default model `all-MiniLM-L6-v2` will be used. All available
                     models can be found at `https://www.sbert.net/docs/pretrained_models.html`.
                     The default model is a fast model. If you want to use a high performance model,
                     `all-mpnet-base-v2` is recommended.
-                - embedding_function (Optional, Callable): the embedding function for creating the
+                - `embedding_function` (Optional, Callable) - the embedding function for creating the
                     vector db. Default is None, SentenceTransformer with the given `embedding_model`
                     will be used. If you want to use OpenAI, Cohere, HuggingFace or other embedding
                     functions, you can pass it here,
                     follow the examples in `https://docs.trychroma.com/embeddings`.
-                - customized_prompt (Optional, str): the customized prompt for the retrieve chat.
+                - `customized_prompt` (Optional, str) - the customized prompt for the retrieve chat.
                     Default is None.
-                - customized_answer_prefix (Optional, str): the customized answer prefix for the
+                - `customized_answer_prefix` (Optional, str) - the customized answer prefix for the
                     retrieve chat. Default is "".
                     If not "" and the customized_answer_prefix is not in the answer,
                     `Update Context` will be triggered.
-                - update_context (Optional, bool): if False, will not apply `Update Context` for
+                - `update_context` (Optional, bool) - if False, will not apply `Update Context` for
                     interactive retrieval. Default is True.
-                - get_or_create (Optional, bool): if True, will create/return a collection for the
+                - `get_or_create` (Optional, bool) - if True, will create/return a collection for the
                     retrieve chat. This is the same as that used in chromadb.
                     Default is False. Will raise ValueError if the collection already exists and
                     get_or_create is False. Will be set to True if docs_path is None.
-                - custom_token_count_function (Optional, Callable): a custom function to count the
+                - `custom_token_count_function` (Optional, Callable) - a custom function to count the
                     number of tokens in a string.
                     The function should take (text:str, model:str) as input and return the
                     token_count(int). the retrieve_config["model"] will be passed in the function.
                     Default is autogen.token_count_utils.count_token that uses tiktoken, which may
                     not be accurate for non-OpenAI models.
-                - custom_text_split_function (Optional, Callable): a custom function to split a
+                - `custom_text_split_function` (Optional, Callable) - a custom function to split a
                     string into a list of strings.
                     Default is None, will use the default function in
                     `autogen.retrieve_utils.split_text_to_chunks`.
-                - custom_text_types (Optional, List[str]): a list of file types to be processed.
+                - `custom_text_types` (Optional, List[str]) - a list of file types to be processed.
                     Default is `autogen.retrieve_utils.TEXT_FORMATS`.
                     This only applies to files under the directories in `docs_path`. Explicitly
                     included files and urls will be chunked regardless of their types.
-                - recursive (Optional, bool): whether to search documents recursively in the
+                - `recursive` (Optional, bool) - whether to search documents recursively in the
                     docs_path. Default is True.
 
             `**kwargs` (dict): other kwargs in [UserProxyAgent](../user_proxy_agent#__init__).
@@ -459,9 +459,9 @@ def message_generator(sender, recipient, context):
             sender (Agent): the sender agent. It should be the instance of RetrieveUserProxyAgent.
             recipient (Agent): the recipient agent. Usually it's the assistant agent.
             context (dict): the context for the message generation. It should contain the following keys:
-                - problem (str): the problem to be solved.
-                - n_results (int): the number of results to be retrieved. Default is 20.
-                - search_string (str): only docs that contain an exact match of this string will be retrieved. Default is "".
+                - `problem` (str) - the problem to be solved.
+                - `n_results` (int) - the number of results to be retrieved. Default is 20.
+                - `search_string` (str) - only docs that contain an exact match of this string will be retrieved. Default is "".
         Returns:
             str: the generated message ready to be sent to the recipient agent.
         """

From 83a12b803232f16a836f6bdc51a13f723b599215 Mon Sep 17 00:00:00 2001
From: Harsha S <sharsha315@gmail.com>
Date: Fri, 29 Mar 2024 13:28:26 +0000
Subject: [PATCH 26/27] Fixed Pre-Commit Error, Trailing spaces on
 agentchat/chat.py

---
 autogen/agentchat/chat.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index 7125b150f0f..3d51942bd6b 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -144,7 +144,7 @@ def __post_carryover_processing(chat_info: Dict[str, Any]) -> None:
 def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
     """Initiate a list of chats.
     Args:
-        chat_queue (List[Dict]): a list of dictionaries containing the information about the chats.
+        chat_queue (List[Dict]): A list of dictionaries containing the information about the chats.
 
         Each dictionary should contain the input arguments for
         [`ConversableAgent.initiate_chat`](/docs/reference/agentchat/conversable_agent#initiate_chat).

From 1776a57ae3bb178e999dd67bf7d7dc37fdb50f9b Mon Sep 17 00:00:00 2001
From: Harsha S <sharsha315@gmail.com>
Date: Fri, 29 Mar 2024 13:55:17 +0000
Subject: [PATCH 27/27] Fixed Pre-Commit Error, Trailing spaces on
 agentchat/chat.py

---
 autogen/agentchat/chat.py | 6 ++----
 1 file changed, 2 insertions(+), 4 deletions(-)

diff --git a/autogen/agentchat/chat.py b/autogen/agentchat/chat.py
index a8f709df40d..347f58bfc3b 100644
--- a/autogen/agentchat/chat.py
+++ b/autogen/agentchat/chat.py
@@ -167,10 +167,8 @@ def initiate_chats(chat_queue: List[Dict[str, Any]]) -> List[ChatResult]:
                initial message.
             - `**context` - additional context information to be passed to the chat.
             - `"carryover"` - It can be used to specify the carryover information to be passed
-                to this chat.
-                If provided, we will combine this carryover with the "message" content when
-                generating the initial chat message in `generate_init_message`.
-        
+               to this chat. If provided, we will combine this carryover with the "message" content when
+               generating the initial chat message in `generate_init_message`.
     Returns:
         (list): a list of ChatResult objects corresponding to the finished chats in the chat_queue.
     """