Merge branch 'main' into pull/3000

autogen/agentchat/contrib/vectordb/mongodb.py

@@ -59,6 +59,10 @@ def __init__(
         sentences = ["The weather is lovely today in paradise."]
         embeddings = self.embedding_function(sentences)
         self.dimensions = len(embeddings[0])
+        # index lookup
+        self.database_name = database_name
+        self.index_name = index_name
+        self.similarity = similarity
 
     def list_collections(self):
         """
@@ -363,7 +367,6 @@ def retrieve_docs(
         collection_name: str = None,
         n_results: int = 10,
         distance_threshold: float = -1,
-        index_name: str = "default",
         **kwargs,
     ) -> QueryResults:
         """

autogen/version.py

@@ -1 +1 @@
-__version__ = "0.2.30"
+__version__ = "0.2.31"

test/agentchat/contrib/vectordb/test_mongodb.py

@@ -116,7 +116,6 @@ def create_collection(collection_name: str,
     collection = db.create_collection(collection_name, overwrite=False, get_or_create=True)
     assert collection.name == collection_name
 
-
 def test_get_collection(db):
     collection_name = MONGODB_COLLECTION
 

website/blog/2023-06-28-MathChat/index.mdx

@@ -75,7 +75,7 @@ We found that compared to basic prompting, which demonstrates the innate capabil
 
 For categories like Algebra and Prealgebra, PoT and PS showed little improvement, and in some instances, even led to a decrease in accuracy. However, MathChat was able to enhance total accuracy by around 6% compared to PoT and PS, showing competitive performance across all categories. Remarkably, MathChat improved accuracy in the Algebra category by about 15% over other methods. Note that categories like Intermediate Algebra and Precalculus remained challenging for all methods, with only about 20% of problems solved accurately.
 
-The code for experiments can be found at this [repository](https://github.com/kevin666aa/FLAML/tree/gpt_math_solver/flaml/autogen/math).
+The code for experiments can be found at this [repository](https://github.com/yiranwu0/FLAML/tree/gpt_math_solver/flaml/autogen/math).
 We now provide an implementation of MathChat using the interactive agents in AutoGen. See this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_MathChat.ipynb) for example usage.
 
 ## Future Directions

website/blog/2023-11-13-OAI-assistants/index.mdx

@@ -112,6 +112,6 @@ Checkout more examples [here](https://github.com/microsoft/autogen/tree/main/not
 `GPTAssistantAgent` was made possible through collaboration with
 [@IANTHEREAL](https://github.com/IANTHEREAL),
 [Jiale Liu](https://leoljl.github.io),
-[Yiran Wu](https://github.com/kevin666aa),
+[Yiran Wu](https://github.com/yiranwu0),
 [Qingyun Wu](https://qingyun-wu.github.io/),
 [Chi Wang](https://www.microsoft.com/en-us/research/people/chiw/), and many other AutoGen maintainers.

website/blog/authors.yml

@@ -13,8 +13,8 @@ qingyunwu:
 yiranwu:
   name: Yiran Wu
   title: PhD student at Pennsylvania State University
-  url: https://github.com/kevin666aa
-  image_url: https://github.com/kevin666aa.png
+  url: https://github.com/yiranwu0
+  image_url: https://github.com/yiranwu0.png
 
 jialeliu:
   name: Jiale Liu

website/docs/topics/groupchat/customized_speaker_selection.ipynb

@@ -6,7 +6,34 @@
    "source": [
     "# Customize Speaker Selection\n",
     "\n",
-    "In GroupChat, we can also customize the speaker selection by passing in a function to `speaker_selection_method`:\n",
+    "```{=mdx}\n",
+    "![group_chat](../../../blog/2024-02-29-StateFlow/img/sf_example_1.png)\n",
+    "```\n",
+    "\n",
+    "In GroupChat, we can customize the speaker selection by passing a function to the `GroupChat` object. With this function, you can build a more **deterministic** agent workflow. We recommend following a **StateFlow** pattern when crafting this function. Please refer to the [StateFlow blog](/blog/2024/02/29/StateFlow) for more details.\n",
+    "\n",
+    "\n",
+    "## An example research workflow\n",
+    "We provide a simple example to build a StateFlow model for research with customized speaker selection.\n",
+    "\n",
+    "We first define the following agents:\n",
+    "\n",
+    "- Initializer: Start the workflow by sending a task.\n",
+    "- Coder: Retrieve papers from the internet by writing code.\n",
+    "- Executor: Execute the code.\n",
+    "- Scientist: Read the papers and write a summary.\n",
+    "\n",
+    "In the figure above, we define a simple workflow for research with 4 states: *Init*, *Retrieve*, *Research*, and *End*. Within each state, we will call different agents to perform the tasks.\n",
+    "\n",
+    "- *Init*: We use the initializer to start the workflow.\n",
+    "- *Retrieve*: We will first call the coder to write code and then call the executor to execute the code.\n",
+    "- *Research*: We will call the scientist to read the papers and write a summary.\n",
+    "- *End*: We will end the workflow.\n",
+    "\n",
+    "## Create your speaker selection function\n",
+    "\n",
+    "Below is a skeleton of the speaker selection function. Fill in the function to define the speaker selection logic.\n",
+    "\n",
     "```python\n",
     "def custom_speaker_selection_func(\n",
     "    last_speaker: Agent, \n",
@@ -35,28 +62,7 @@
     ")\n",
     "```\n",
     "The last speaker and the groupchat object are passed to the function. \n",
-    "Commonly used variables from groupchat are `groupchat.messages` and `groupchat.agents`, which is the message history and the agents in the group chat respectively. You can access other attributes of the groupchat, such as `groupchat.allowed_speaker_transitions_dict` for pre-defined `allowed_speaker_transitions_dict`.\n",
-    "\n",
-    "Heres is a simple example to build workflow for research with customized speaker selection.\n",
-    "\n",
-    "\n",
-    "```{=mdx}\n",
-    "![group_chat](../../../blog/2024-02-29-StateFlow/img/sf_example_1.png)\n",
-    "```\n",
-    "\n",
-    "We define the following agents:\n",
-    "\n",
-    "- Initializer: Start the workflow by sending a task.\n",
-    "- Coder: Retrieve papers from the internet by writing code.\n",
-    "- Executor: Execute the code.\n",
-    "- Scientist: Read the papers and write a summary.\n",
-    "\n",
-    "In the Figure, we define a simple workflow for research with 4 states: Init, Retrieve, Research and End. Within each state, we will call different agents to perform the tasks.\n",
-    "\n",
-    "Init: We use the initializer to start the workflow.\n",
-    "Retrieve: We will first call the coder to write code and then call the executor to execute the code.\n",
-    "Research: We will call the scientist to read the papers and write a summary.\n",
-    "End: We will end the workflow."
+    "Commonly used variables from groupchat are `groupchat.messages` and `groupchat.agents`, which is the message history and the agents in the group chat respectively. You can access other attributes of the groupchat, such as `groupchat.allowed_speaker_transitions_dict` for pre-defined `allowed_speaker_transitions_dict`."
    ]
   },
   {

website/docs/tutorial/conversation-patterns.ipynb

@@ -12,7 +12,18 @@
     "In this chapter, we will first dig a little bit more into the two-agent \n",
     "chat pattern and chat result, \n",
     "then we will show you several conversation patterns that involve \n",
-    "more than two agents.\n"
+    "more than two agents.\n",
+    "\n",
+    "### An Overview\n",
+    "\n",
+    "1. **Two-agent chat**: the simplest form of conversation pattern where two agents chat with each other.\n",
+    "2. **Sequential chat**: a sequence of chats between two agents, chained together by a carryover mechanism, which brings the summary of the previous chat to the context of the next chat.\n",
+    "3. **Group Chat**: a single chat involving more than two agents. An important question in group chat is: What agent should be next to speak? To support different scenarios, we provide different ways to organize agents in a group chat:\n",
+    "    - We support several strategies to select the next agent: `round_robin`, `random`, `manual` (human selection), and `auto` (Default, using an LLM to decide).\n",
+    "    - We provide a way to constrain the selection of the next speaker (See examples below).\n",
+    "    - We allow you to pass in a function to customize the selection of the next speaker. With this feature, you can build a **StateFlow** model which allows a deterministic workflow among your agents.\n",
+    " Please refer to this [guide](/docs/topics/groupchat/customized_speaker_selection) and this [blog post](/blog/2024/02/29/StateFlow) on StateFlow for more details.\n",
+    "4. **Nested Chat**: package a workflow into a single agent for reuse in a larger workflow."
    ]
   },
   {