Merge branch 'main' into agent_function_print

.coveragerc

@@ -3,3 +3,4 @@ branch = True
 source = autogen
 omit =
   *test*
+  *samples*

.github/workflows/build.yml

@@ -42,10 +42,6 @@ jobs:
           python -c "import autogen"
           pip install -e. pytest mock
           pip uninstall -y openai
-      - name: Install unstructured if not windows
-        if: matrix.os != 'windows-2019'
-        run: |
-          pip install "unstructured[all-docs]"
       - name: Test with pytest
         if: matrix.python-version != '3.10'
         run: |

.github/workflows/contrib-tests.yml

@@ -34,10 +34,14 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel
           pip install pytest
-      - name: Install qdrant_client when python-version is 3.10
-        if: matrix.python-version == '3.10' || matrix.python-version == '3.8'
+      - name: Install qdrant_client when python-version is 3.8 and 3.10
+        if: matrix.python-version == '3.8' || matrix.python-version == '3.10'
         run: |
           pip install qdrant_client[fastembed]
+      - name: Install unstructured when python-version is 3.9 and 3.11 and not windows
+        if: (matrix.python-version == '3.9' || matrix.python-version == '3.11') && matrix.os != 'windows-2019'
+        run: |
+          pip install unstructured[all-docs]
       - name: Install packages and dependencies for RetrieveChat
         run: |
           pip install -e .[retrievechat]

.gitignore

@@ -167,3 +167,6 @@ wolfram.txt
 
 # DB on disk for TeachableAgent
 tmp/
+
+# Storage for the AgentEval output
+test/test_files/agenteval-in-out/out/

OAI_CONFIG_LIST_sample

@@ -5,14 +5,14 @@
         "api_key": "<your OpenAI API key here>"
     },
     {
-        "model": "gpt-4",
+        "model": "<your Azure OpenAI deployment name>",
         "api_key": "<your Azure OpenAI API key here>",
         "base_url": "<your Azure OpenAI API base here>",
         "api_type": "azure",
         "api_version": "2023-07-01-preview"
     },
     {
-        "model": "gpt-3.5-turbo",
+        "model": "<your Azure OpenAI deployment name>",
         "api_key": "<your Azure OpenAI API key here>",
         "base_url": "<your Azure OpenAI API base here>",
         "api_type": "azure",

autogen/agentchat/contrib/qdrant_retrieve_user_proxy_agent.py

@@ -1,7 +1,7 @@
 from typing import Callable, Dict, List, Optional
 
 from autogen.agentchat.contrib.retrieve_user_proxy_agent import RetrieveUserProxyAgent
-from autogen.retrieve_utils import get_files_from_dir, split_files_to_chunks
+from autogen.retrieve_utils import get_files_from_dir, split_files_to_chunks, TEXT_FORMATS
 import logging
 
 logger = logging.getLogger(__name__)
@@ -45,8 +45,8 @@ def __init__(
                     prompt will be different for different tasks. The default value is `default`, which supports both code and qa.
                 - client (Optional, qdrant_client.QdrantClient(":memory:")): A QdrantClient instance. If not provided, an in-memory instance will be assigned. Not recommended for production.
                     will be used. If you want to use other vector db, extend this class and override the `retrieve_docs` function.
-                - docs_path (Optional, str): the path to the docs directory. It can also be the path to a single file,
-                    or the url to a single file. Default is None, which works only if the collection is already created.
+                - 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.
                 - 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.
@@ -66,11 +66,14 @@ def __init__(
                 - 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.
-                - custom_token_count_function(Optional, Callable): a custom function to count the number of tokens in a string.
+                - custom_token_count_function (Optional, Callable): a custom function to count the number of tokens in a string.
                     The function should take a string as input and return three integers (token_count, tokens_per_message, tokens_per_name).
                     Default is None, tiktoken will be used and 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.
+                - 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`. Explictly 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.
                 - parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores.
                 - on_disk (Optional, bool): Whether to store the collection on disk. Default is False.
                 - quantization_config: Quantization configuration. If None, quantization will be disabled.
@@ -111,6 +114,8 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
                 must_break_at_empty_line=self._must_break_at_empty_line,
                 embedding_model=self._embedding_model,
                 custom_text_split_function=self.custom_text_split_function,
+                custom_text_types=self._custom_text_types,
+                recursive=self._recursive,
                 parallel=self._parallel,
                 on_disk=self._on_disk,
                 quantization_config=self._quantization_config,
@@ -139,15 +144,17 @@ def create_qdrant_from_dir(
     must_break_at_empty_line: bool = True,
     embedding_model: str = "BAAI/bge-small-en-v1.5",
     custom_text_split_function: Callable = None,
+    custom_text_types: List[str] = TEXT_FORMATS,
+    recursive: bool = True,
     parallel: int = 0,
     on_disk: bool = False,
     quantization_config: Optional[models.QuantizationConfig] = None,
     hnsw_config: Optional[models.HnswConfigDiff] = None,
     payload_indexing: bool = False,
     qdrant_client_options: Optional[Dict] = {},
 ):
-    """Create a Qdrant collection from all the files in a given directory, the directory can also be a single file or a url to
-        a single file.
+    """Create a Qdrant collection from all the files in a given directory, the directory can also be a single file or a
+      url to a single file.
 
     Args:
         dir_path (str): the path to the directory, file or url.
@@ -156,24 +163,35 @@ def create_qdrant_from_dir(
         collection_name (Optional, str): the name of the collection. Default is "all-my-documents".
         chunk_mode (Optional, str): the chunk mode. Default is "multi_lines".
         must_break_at_empty_line (Optional, bool): Whether to break at empty line. Default is True.
-        embedding_model (Optional, str): the embedding model to use. Default is "BAAI/bge-small-en-v1.5". The list of all the available models can be at https://qdrant.github.io/fastembed/examples/Supported_Models/.
+        embedding_model (Optional, str): the embedding model to use. Default is "BAAI/bge-small-en-v1.5".
+            The list of all the available models can be at https://qdrant.github.io/fastembed/examples/Supported_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 TEXT_FORMATS.
+        recursive (Optional, bool): whether to search documents recursively in the dir_path. Default is True.
         parallel (Optional, int): How many parallel workers to use for embedding. Defaults to the number of CPU cores
         on_disk (Optional, bool): Whether to store the collection on disk. Default is False.
-        quantization_config: Quantization configuration. If None, quantization will be disabled. Ref: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
-        hnsw_config: HNSW configuration. If None, default configuration will be used. Ref: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
+        quantization_config: Quantization configuration. If None, quantization will be disabled.
+            Ref: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
+        hnsw_config: HNSW configuration. If None, default configuration will be used.
+            Ref: https://qdrant.github.io/qdrant/redoc/index.html#tag/collections/operation/create_collection
         payload_indexing: Whether to create a payload index for the document field. Default is False.
-        qdrant_client_options: (Optional, dict): the options for instantiating the qdrant client. Reference: https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58.
+        qdrant_client_options: (Optional, dict): the options for instantiating the qdrant client.
+            Ref: https://github.com/qdrant/qdrant-client/blob/master/qdrant_client/qdrant_client.py#L36-L58.
     """
     if client is None:
         client = QdrantClient(**qdrant_client_options)
         client.set_model(embedding_model)
 
     if custom_text_split_function is not None:
         chunks = split_files_to_chunks(
-            get_files_from_dir(dir_path), custom_text_split_function=custom_text_split_function
+            get_files_from_dir(dir_path, custom_text_types, recursive),
+            custom_text_split_function=custom_text_split_function,
         )
     else:
-        chunks = split_files_to_chunks(get_files_from_dir(dir_path), max_tokens, chunk_mode, must_break_at_empty_line)
+        chunks = split_files_to_chunks(
+            get_files_from_dir(dir_path, custom_text_types, recursive), max_tokens, chunk_mode, must_break_at_empty_line
+        )
     logger.info(f"Found {len(chunks)} chunks.")
 
     # Check if collection by same name exists, if not, create it with custom options

autogen/agentchat/contrib/retrieve_user_proxy_agent.py

@@ -6,7 +6,7 @@
     raise ImportError("Please install dependencies first. `pip install pyautogen[retrievechat]`")
 from autogen.agentchat.agent import Agent
 from autogen.agentchat import UserProxyAgent
-from autogen.retrieve_utils import create_vector_db_from_dir, query_vector_db
+from autogen.retrieve_utils import create_vector_db_from_dir, query_vector_db, TEXT_FORMATS
 from autogen.token_count_utils import count_token
 from autogen.code_utils import extract_code
 
@@ -97,8 +97,8 @@ def __init__(
                     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, str): the path to the docs directory. It can also be the path to a single file,
-                    or the url to a single file. Default is None, which works only if the collection is already created.
+                - 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.
                 - 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.
@@ -124,11 +124,14 @@ def __init__(
                 - 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.
+                - 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.
+                - 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`. Explictly 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 of overriding retrieve_docs:
@@ -181,6 +184,8 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
         self._get_or_create = self._retrieve_config.get("get_or_create", False) if self._docs_path is not None else True
         self.custom_token_count_function = self._retrieve_config.get("custom_token_count_function", count_token)
         self.custom_text_split_function = self._retrieve_config.get("custom_text_split_function", None)
+        self._custom_text_types = self._retrieve_config.get("custom_text_types", TEXT_FORMATS)
+        self._recursive = self._retrieve_config.get("recursive", True)
         self._context_max_tokens = self._max_tokens * 0.8
         self._collection = True if self._docs_path is None else False  # whether the collection is created
         self._ipython = get_ipython()
@@ -189,6 +194,7 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
         self._intermediate_answers = set()  # the intermediate answers
         self._doc_contents = []  # the contents of the current used doc
         self._doc_ids = []  # the ids of the current used doc
+        self._search_string = ""  # the search string used in the current query
         # update the termination message function
         self._is_termination_msg = (
             self._is_termination_msg_retrievechat if is_termination_msg is None else is_termination_msg
@@ -282,6 +288,8 @@ def _generate_message(self, doc_contents, task="default"):
     def _check_update_context(self, message):
         if isinstance(message, dict):
             message = message.get("content", "")
+        elif not isinstance(message, str):
+            message = ""
         update_context_case1 = "UPDATE CONTEXT" in message[-20:].upper() or "UPDATE CONTEXT" in message[:20].upper()
         update_context_case2 = self.customized_answer_prefix and self.customized_answer_prefix not in message.upper()
         return update_context_case1, update_context_case2
@@ -320,7 +328,9 @@ def _generate_retrieve_user_reply(
                 if not doc_contents:
                     for _tmp_retrieve_count in range(1, 5):
                         self._reset(intermediate=True)
-                        self.retrieve_docs(self.problem, self.n_results * (2 * _tmp_retrieve_count + 1))
+                        self.retrieve_docs(
+                            self.problem, self.n_results * (2 * _tmp_retrieve_count + 1), self._search_string
+                        )
                         doc_contents = self._get_context(self._results)
                         if doc_contents:
                             break
@@ -329,7 +339,9 @@ def _generate_retrieve_user_reply(
                 # docs in the retrieved doc results to the context.
                 for _tmp_retrieve_count in range(5):
                     self._reset(intermediate=True)
-                    self.retrieve_docs(_intermediate_info[0], self.n_results * (2 * _tmp_retrieve_count + 1))
+                    self.retrieve_docs(
+                        _intermediate_info[0], self.n_results * (2 * _tmp_retrieve_count + 1), self._search_string
+                    )
                     self._get_context(self._results)
                     doc_contents = "\n".join(self._doc_contents)  # + "\n" + "\n".join(self._intermediate_answers)
                     if doc_contents:
@@ -371,6 +383,8 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
                 get_or_create=self._get_or_create,
                 embedding_function=self._embedding_function,
                 custom_text_split_function=self.custom_text_split_function,
+                custom_text_types=self._custom_text_types,
+                recursive=self._recursive,
             )
             self._collection = True
             self._get_or_create = True
@@ -384,6 +398,7 @@ def retrieve_docs(self, problem: str, n_results: int = 20, search_string: str =
             embedding_model=self._embedding_model,
             embedding_function=self._embedding_function,
         )
+        self._search_string = search_string
         self._results = results
         print("doc_ids: ", results["ids"])
 

autogen/agentchat/conversable_agent.py

@@ -1201,7 +1201,7 @@ def generate_init_message(self, **context) -> Union[str, Dict]:
         """Generate the initial message for the agent.
 
         Override this function to customize the initial message based on user's request.
-        If not overriden, "message" needs to be provided in the context.
+        If not overridden, "message" needs to be provided in the context.
         """
         return context["message"]