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cleanup

This commit is contained in:
Chi Wang 2023-09-19 02:26:57 +00:00
parent 1e4781acd2
commit ec85b4984d
23 changed files with 1043 additions and 72 deletions
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10
.github/workflows/openai.yml vendored
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@ -9,11 +9,11 @@ on:
paths:
- 'autogen/**'
- 'test/**'
- 'notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb'
- 'notebook/autogen_agentchat_function_call.ipynb'
- 'notebook/autogen_agentchat_MathChat.ipynb'
- 'notebook/autogen_openai_completion.ipynb'
- 'notebook/autogen_chatgpt_gpt4.ipynb'
- 'notebook/agentchat_auto_feedback_from_code_execution.ipynb'
- 'notebook/agentchat_function_call.ipynb'
- 'notebook/agentchat_MathChat.ipynb'
- 'notebook/oai_completion.ipynb'
- 'notebook/oai_chatgpt_gpt4.ipynb'
- '.github/workflows/openai.yml'
jobs:

4
notebook/autogen_agentchat_MathChat.ipynb → notebook/agentchat_MathChat.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_agentchat_MathChat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/agentchat_MathChat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
@ -17,7 +17,7 @@
"\n",
"AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation. Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
"\n",
"MathChat is an experimental convesational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://microsoft.github.io/autogen/blog/2023/06/28/MathChat).\n",
"MathChat is an experimental convesational framework for math problem solving. In this notebook, we demonstrate how to use MathChat to solve math problems. MathChat uses the `AssistantAgent` and `MathUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `MathUserProxyAgent` implements a different auto reply mechanism corresponding to the MathChat prompts. You can find more details in the paper [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337) or the [blogpost](https://microsoft.github.io/autogen/blog/2023/06/28/MathChat).\n",
"\n",
"## Requirements\n",
"\n",

5
notebook/autogen_agentchat_RetrieveChat.ipynb → notebook/agentchat_RetrieveChat.ipynb
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@ -1,10 +1,11 @@
{
"cells": [
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_RetrieveChat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
@ -18,7 +19,7 @@
"AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framwork allows tool use and human participance through multi-agent conversation.\n",
"Please find documentation about this feature [here](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat).\n",
"\n",
"RetrieveChat is a convesational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
"RetrieveChat is a convesational system for retrieve augmented code generation and question answering. In this notebook, we demonstrate how to utilize RetrieveChat to generate code and answer questions based on customized documentations that are not present in the LLM's training dataset. RetrieveChat uses the `RetrieveAssistantAgent` and `RetrieveUserProxyAgent`, which is similar to the usage of `AssistantAgent` and `UserProxyAgent` in other notebooks (e.g., [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveAssistantAgent` and `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
"\n",
"## Table of Contents\n",
"We'll demonstrates five examples of using RetrieveChat for code generation and question answering:\n",

2
notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb → notebook/agentchat_auto_feedback_from_code_execution.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_chess.ipynb → notebook/agentchat_chess.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_chess.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_function_call.ipynb → notebook/agentchat_function_call.ipynb
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@ -6,7 +6,7 @@
"id": "ae1f50ec",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_function_call.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_groupchat.ipynb → notebook/agentchat_groupchat.ipynb
View File

@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_groupchat_research.ipynb → notebook/agentchat_groupchat_research.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat_research.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/groupchat_research.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_groupchat_vis.ipynb → notebook/agentchat_groupchat_vis.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat_vis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_groupchat_vis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_human_feedback.ipynb → notebook/agentchat_human_feedback.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_human_feedback.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_planning.ipynb → notebook/agentchat_planning.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_planning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_stream.ipynb → notebook/agentchat_stream.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_stream.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

970
notebook/agentchat_teaching.ipynb Normal file
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File diff suppressed because one or more lines are too long

2
notebook/autogen_agentchat_two_users.ipynb → notebook/agentchat_two_users.ipynb
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@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/autogen_agentchat_two_users.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/FLAML/blob/main/notebook/agentchat_two_users.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_agentchat_web_info.ipynb → notebook/agentchat_web_info.ipynb
View File

@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_agentchat_web_info.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_chatgpt_gpt4.ipynb → notebook/oai_chatgpt_gpt4.ipynb
View File

@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_chatgpt_gpt4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

2
notebook/autogen_openai_completion.ipynb → notebook/oai_completion.ipynb
View File

@ -5,7 +5,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_openai_completion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
"<a href=\"https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_completion.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{

30
test/test_notebook.py
View File

@ -48,45 +48,45 @@ def run_notebook(input_nb, output_nb="executed_openai_notebook.ipynb", save=Fals
skip or not sys.version.startswith("3.10"),
reason="do not run if openai is not installed or py!=3.10",
)
def test_autogen_agentchat_auto_feedback_from_code(save=False):
run_notebook("autogen_agentchat_auto_feedback_from_code_execution.ipynb", save=save)
def test_agentchat_auto_feedback_from_code(save=False):
run_notebook("agentchat_auto_feedback_from_code_execution.ipynb", save=save)
@pytest.mark.skipif(
skip or not sys.version.startswith("3.10"),
reason="do not run if openai is not installed or py!=3.10",
)
def test_autogen_openai_completion(save=False):
run_notebook("autogen_openai_completion.ipynb", save=save)
def test_openai_completion(save=False):
run_notebook("openai_completion.ipynb", save=save)
@pytest.mark.skipif(
skip or not sys.version.startswith("3.10"),
reason="do not run if openai is not installed or py!=3.10",
)
def test_autogen_agentchat_function_call(save=False):
run_notebook("autogen_agentchat_function_call.ipynb", save=save)
def test_agentchat_function_call(save=False):
run_notebook("agentchat_function_call.ipynb", save=save)
@pytest.mark.skipif(
skip or not sys.version.startswith("3.10"),
reason="do not run if openai is not installed or py!=3.10",
)
def test_autogen_agentchat_MathChat(save=False):
run_notebook("autogen_agentchat_MathChat.ipynb", save=save)
def test_agentchat_MathChat(save=False):
run_notebook("agentchat_MathChat.ipynb", save=save)
@pytest.mark.skipif(
skip or not sys.version.startswith("3.11"),
reason="do not run if openai is not installed or py!=3.11",
)
def test_autogen_chatgpt_gpt4(save=False):
run_notebook("autogen_chatgpt_gpt4.ipynb", save=save)
def test_chatgpt_gpt4(save=False):
run_notebook("chatgpt_gpt4.ipynb", save=save)
if __name__ == "__main__":
test_autogen_agentchat_auto_feedback_from_code(save=True)
# test_autogen_chatgpt_gpt4(save=True)
# test_autogen_openai_completion(save=True)
# test_autogen_agentchat_MathChat(save=True)
# test_autogen_agentchat_function_call(save=True)
test_agentchat_auto_feedback_from_code(save=True)
# test_chatgpt_gpt4(save=True)
# test_openai_completion(save=True)
# test_agentchat_MathChat(save=True)
# test_agentchat_function_call(save=True)

2
website/blog/2023-06-28-MathChat/index.mdx
View File

@ -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).
We now provide an implementation of MathChat using the interactive agents in AutoGen. See this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_MathChat.ipynb) for example usage.
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

23
website/docs/Examples/AutoGen-AgentChat.md
View File

@ -4,14 +4,15 @@ AutoGen offers conversable agents powered by LLM, tool or human, which can be us
Please find documentation about this feature [here](/docs/Use-Cases/agent_chat).
Links to notebook examples:
* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)
* [Auto Code Generation, Execution, Debugging and Human Feedback](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_human_feedback.ipynb)
* [Solve Tasks Requiring Web Info](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_web_info.ipynb)
* [Use Provided Tools as Functions](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_function_call.ipynb)
* [Automated Task Solving with Coding & Planning Agents](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_planning.ipynb)
* [Automated Task Solving with GPT-4 + Multiple Human Users](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_two_users.ipynb)
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_chess.ipynb)
* [Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
* [Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat_vis.ipynb)
* [Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat_research.ipynb)
* [Automated Continual Learning from New Data](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_stream.ipynb)
* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)
* [Auto Code Generation, Execution, Debugging and Human Feedback](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
* [Solve Tasks Requiring Web Info](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb)
* [Use Provided Tools as Functions](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb)
* [Automated Task Solving with Coding & Planning Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
* [Automated Task Solving with GPT-4 + Multiple Human Users](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb)
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb)
* [Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
* [Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_vis.ipynb)
* [Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_research.ipynb)
* [Automated Continual Learning from New Data](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
* [Teach Agents New Skills & Reuse via Automated Chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teaching.ipynb)

4
website/docs/Examples/AutoGen-OpenAI.md
View File

@ -4,5 +4,5 @@ AutoGen also offers a cost-effective hyperparameter optimization technique [EcoO
Please find documentation about this feature [here](/docs/Use-Cases/enhanced_inference).
Links to notebook examples:
* [Optimize for Code Generation](https://github.com/microsoft/autogen/blob/main/notebook/autogen_openai_completion.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_openai_completion.ipynb)
* [Optimize for Math](https://github.com/microsoft/autogen/blob/main/notebook/autogen_chatgpt_gpt4.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/autogen_chatgpt_gpt4.ipynb)
* [Optimize for Code Generation](https://github.com/microsoft/autogen/blob/main/notebook/oai_completion.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_completion.ipynb)
* [Optimize for Math](https://github.com/microsoft/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb) | [Open in colab](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb)

34
website/docs/Use-Cases/agent_chat.md
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@ -69,29 +69,29 @@ On the one hand, one can achieve fully autonomous conversations after an initial
By adopting the conversation-driven control with both programming language and natural language, AutoGen inherently allows dynamic conversation. Dynamic conversation allows the agent topology to change depending on the actual flow of conversation under different input problem instances, while the flow of a static conversation always follows a pre-defined topology. The dynamic conversation pattern is useful in complex applications where the patterns of interaction cannot be predetermined in advance. AutoGen provides two general approaches to achieving dynamic conversation:
- Registered auto-reply. With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. A working system demonstrating this type of dynamic conversation can be found in this code example, demonstrating a [dynamic group chat](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat.ipynb). In the system, we register an auto-reply function in the group chat manager, which lets LLM decide who will the next speaker be in a group chat setting.
- Registered auto-reply. With the pluggable auto-reply function, one can choose to invoke conversations with other agents depending on the content of the current message and context. A working system demonstrating this type of dynamic conversation can be found in this code example, demonstrating a [dynamic group chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb). In the system, we register an auto-reply function in the group chat manager, which lets LLM decide who will the next speaker be in a group chat setting.
- LLM-based function call. In this approach, LLM decides whether or not to call a particular function depending on the conversation status in each inference call.
By messaging additional agents in the called functions, the LLM can drive dynamic multi-agent conversation. A working system showcasing this type of dynamic conversation can be found in the [multi-user math problem solving scenario](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_two_users.ipynb), where a student assistant would automatically resort to an expert using function calls.
- LLM-based function call. In this approach, LLM decides whether or not to call a particular function depending on the conversation status in each inference call.
By messaging additional agents in the called functions, the LLM can drive dynamic multi-agent conversation. A working system showcasing this type of dynamic conversation can be found in the [multi-user math problem solving scenario](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb), where a student assistant would automatically resort to an expert using function calls.
### Diverse Applications Implemented with AutoGen
### Diverse Applications Implemented with AutoGen
The figure below shows six examples of applications built using AutoGen.
![Applications](images/app.png)
* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_auto_feedback_from_code_execution.ipynb)
* [Auto Code Generation, Execution, Debugging and Human Feedback](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_human_feedback.ipynb)
* [Solve Tasks Requiring Web Info](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_web_info.ipynb)
* [Use Provided Tools as Functions](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_function_call.ipynb)
* [Automated Task Solving with Coding & Planning Agents](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_planning.ipynb)
* [Automated Task Solving with GPT-4 + Multiple Human Users](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_two_users.ipynb)
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_chess.ipynb)
* [Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat.ipynb)
* [Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat_vis.ipynb)
* [Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_groupchat_research.ipynb)
* [Automated Continual Learning from New Data](https://github.com/microsoft/autogen/blob/main/notebook/autogen_agentchat_stream.ipynb)
* [Automated Task Solving with Code Generation, Execution & Debugging](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)
* [Auto Code Generation, Execution, Debugging and Human Feedback](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
* [Solve Tasks Requiring Web Info](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_web_info.ipynb)
* [Use Provided Tools as Functions](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb)
* [Automated Task Solving with Coding & Planning Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
* [Automated Task Solving with GPT-4 + Multiple Human Users](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb)
* [Automated Chess Game Playing & Chitchatting by GPT-4 Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb)
* [Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat.ipynb)
* [Automated Data Visualization by Group Chat (with 3 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_vis.ipynb)
* [Automated Complex Task Solving by Group Chat (with 6 group member agents and 1 manager agent)](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_research.ipynb)
* [Automated Continual Learning from New Data](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
* [Teach Agents New Skills & Reuse via Automated Chat](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_teaching.ipynb)
@ -101,4 +101,4 @@ The figure below shows six examples of applications built using AutoGen.
* [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework](https://arxiv.org/abs/2308.08155). Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Shaokun Zhang, Erkang Zhu, Beibin Li, Li Jiang, Xiaoyun Zhang and Chi Wang. ArXiv 2023.
* [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).
* [An Empirical Study on Challenging Math Problem Solving with GPT-4](https://arxiv.org/abs/2306.01337). Yiran Wu, Feiran Jia, Shaokun Zhang, Hangyu Li, Erkang Zhu, Yue Wang, Yin Tat Lee, Richard Peng, Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2306.01337 (2023).

7
website/docs/Use-Cases/enhanced_inference.md
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@ -6,8 +6,8 @@ There are a number of benefits of using `autogen` to perform inference: performa
## Tune Inference Parameters
*Links to notebook examples:*
* [Optimize for Code Generation](https://github.com/microsoft/autogen/blob/main/notebook/autogen_openai_completion.ipynb)
* [Optimize for Math](https://github.com/microsoft/autogen/blob/main/notebook/autogen_chatgpt_gpt4.ipynb)
* [Optimize for Code Generation](https://github.com/microsoft/autogen/blob/main/notebook/oai_completion.ipynb)
* [Optimize for Math](https://github.com/microsoft/autogen/blob/main/notebook/oai_chatgpt_gpt4.ipynb)
### Choices to optimize
@ -158,7 +158,7 @@ response = autogen.Completion.create(
It will try querying Azure OpenAI gpt-4, OpenAI gpt-3.5-turbo, and a locally hosted llama-7B one by one, ignoring AuthenticationError, RateLimitError and Timeout,
until a valid result is returned. This can speed up the development process where the rate limit is a bottleneck. An error will be raised if the last choice fails. So make sure the last choice in the list has the best availability.
For convenience, we provide a number of utility functions to load config lists, such as `config_list_from_json`.
For convenience, we provide a number of utility functions to load config lists, such as [`config_list_from_json`](/docs/reference/oai/openai_utils#config_list_from_json).
### Logic error
@ -364,4 +364,3 @@ Set `compact=False` in `start_logging()` to switch.
```
It can be seen that the individual API call history contains redundant information of the conversation. For a long conversation the degree of redundancy is high.
The compact history is more efficient and the individual API call history contains more details.