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Improve doc in tutorial/conversation-patterns and customized_speaker_selection (#3006)
* update * update --------- Co-authored-by: Yiran Wu <32823396+kevin666aa@users.noreply.github.com>
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@ -75,7 +75,7 @@ We found that compared to basic prompting, which demonstrates the innate capabil
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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.
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The code for experiments can be found at this [repository](https://github.com/kevin666aa/FLAML/tree/gpt_math_solver/flaml/autogen/math).
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The code for experiments can be found at this [repository](https://github.com/yiranwu0/FLAML/tree/gpt_math_solver/flaml/autogen/math).
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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.
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## Future Directions
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@ -112,6 +112,6 @@ Checkout more examples [here](https://github.com/microsoft/autogen/tree/main/not
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`GPTAssistantAgent` was made possible through collaboration with
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[@IANTHEREAL](https://github.com/IANTHEREAL),
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[Jiale Liu](https://leoljl.github.io),
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[Yiran Wu](https://github.com/kevin666aa),
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[Yiran Wu](https://github.com/yiranwu0),
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[Qingyun Wu](https://qingyun-wu.github.io/),
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[Chi Wang](https://www.microsoft.com/en-us/research/people/chiw/), and many other AutoGen maintainers.
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@ -13,8 +13,8 @@ qingyunwu:
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yiranwu:
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name: Yiran Wu
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title: PhD student at Pennsylvania State University
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url: https://github.com/kevin666aa
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image_url: https://github.com/kevin666aa.png
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url: https://github.com/yiranwu0
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image_url: https://github.com/yiranwu0.png
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jialeliu:
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name: Jiale Liu
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@ -6,7 +6,34 @@
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"source": [
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"# Customize Speaker Selection\n",
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"\n",
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"In GroupChat, we can also customize the speaker selection by passing in a function to `speaker_selection_method`:\n",
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"```{=mdx}\n",
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"\n",
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"```\n",
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"\n",
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"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",
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"\n",
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"\n",
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"## An example research workflow\n",
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"We provide a simple example to build a StateFlow model for research with customized speaker selection.\n",
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"\n",
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"We first define the following agents:\n",
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"\n",
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"- Initializer: Start the workflow by sending a task.\n",
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"- Coder: Retrieve papers from the internet by writing code.\n",
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"- Executor: Execute the code.\n",
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"- Scientist: Read the papers and write a summary.\n",
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"\n",
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"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",
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"\n",
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"- *Init*: We use the initializer to start the workflow.\n",
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"- *Retrieve*: We will first call the coder to write code and then call the executor to execute the code.\n",
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"- *Research*: We will call the scientist to read the papers and write a summary.\n",
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"- *End*: We will end the workflow.\n",
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"\n",
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"## Create your speaker selection function\n",
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"\n",
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"Below is a skeleton of the speaker selection function. Fill in the function to define the speaker selection logic.\n",
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"\n",
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"```python\n",
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"def custom_speaker_selection_func(\n",
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" last_speaker: Agent, \n",
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@ -35,28 +62,7 @@
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")\n",
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"```\n",
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"The last speaker and the groupchat object are passed to the function. \n",
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"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",
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"\n",
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"Heres is a simple example to build workflow for research with customized speaker selection.\n",
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"\n",
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"\n",
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"```{=mdx}\n",
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"\n",
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"```\n",
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"\n",
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"We define the following agents:\n",
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"\n",
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"- Initializer: Start the workflow by sending a task.\n",
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"- Coder: Retrieve papers from the internet by writing code.\n",
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"- Executor: Execute the code.\n",
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"- Scientist: Read the papers and write a summary.\n",
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"\n",
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"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",
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"\n",
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"Init: We use the initializer to start the workflow.\n",
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"Retrieve: We will first call the coder to write code and then call the executor to execute the code.\n",
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"Research: We will call the scientist to read the papers and write a summary.\n",
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"End: We will end the workflow."
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"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`."
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]
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},
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{
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@ -12,7 +12,18 @@
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"In this chapter, we will first dig a little bit more into the two-agent \n",
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"chat pattern and chat result, \n",
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"then we will show you several conversation patterns that involve \n",
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"more than two agents.\n"
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"more than two agents.\n",
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"\n",
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"### An Overview\n",
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"\n",
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"1. **Two-agent chat**: the simplest form of conversation pattern where two agents chat with each other.\n",
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"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",
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"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",
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" - We support several strategies to select the next agent: `round_robin`, `random`, `manual` (human selection), and `auto` (Default, using an LLM to decide).\n",
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" - We provide a way to constrain the selection of the next speaker (See examples below).\n",
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" - 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",
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" 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",
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"4. **Nested Chat**: package a workflow into a single agent for reuse in a larger workflow."
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]
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},
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{
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