Organize notebooks into logical groups #273

website/docs/Use-Cases/agent_chat.md

@@ -11,7 +11,7 @@ AutoGen abstracts and implements conversable agents
 designed to solve tasks through inter-agent conversations. Specifically, the agents in AutoGen have the following notable features:
 
 - Conversable: Agents in AutoGen are conversable, which means that any agent can send
-and receive messages from other agents to initiate or continue a conversation
+  and receive messages from other agents to initiate or continue a conversation
 
 - Customizable: Agents in AutoGen can be customized to integrate LLMs, humans, tools, or a combination of them.
 
@@ -20,7 +20,6 @@ The figure below shows the built-in agents in AutoGen.
 
 We have designed a generic `ConversableAgent` class for Agents that are capable of conversing with each other through the exchange of messages to jointly finish a task. An agent can communicate with other agents and perform actions. Different agents can differ in what actions they perform after receiving messages. Two representative subclasses are `AssistantAgent` and `UserProxyAgent`.
 
-
 - The `AssistantAgent` is designed to act as an AI assistant, using LLMs by default but not requiring human input or code execution. It could write Python code (in a Python coding block) for a user to execute when a message (typically a description of a task that needs to be solved) is received. Under the hood, the Python code is written by LLM (e.g., GPT-4). It can also receive the execution results and suggest corrections or bug fixes. Its behavior can be altered by passing a new system message. The LLM [inference](#enhanced-inference) configuration can be configured via `llm_config`.
 
 - The `UserProxyAgent` is conceptually a proxy agent for humans, soliciting human input as the agent's reply at each interaction turn by default and also having the capability to execute code and call functions. The `UserProxyAgent` triggers code execution automatically when it detects an executable code block in the received message and no human user input is provided. Code execution can be disabled by setting the `code_execution_config` parameter to False. LLM-based response is disabled by default. It can be enabled by setting `llm_config` to a dict corresponding to the [inference](/docs/Use-Cases/enhanced_inference) configuration. When `llm_config` is set as a dictionary, `UserProxyAgent` can generate replies using an LLM when code execution is not performed.
@@ -45,13 +44,15 @@ user_proxy = UserProxyAgent(name="user_proxy")
 ### A Basic Two-Agent Conversation Example
 
 Once the participating agents are constructed properly, one can start a multi-agent conversation session by an initialization step as shown in the following code:
+
 ```python
 # the assistant receives a message from the user, which contains the task description
 user_proxy.initiate_chat(
     assistant,
     message="""What date is today? Which big tech stock has the largest year-to-date gain this year? How much is the gain?""",
 )
 ```
+
 After the initialization step, the conversation could proceed automatically. Find a visual illustration of how the user_proxy and assistant collaboratively solve the above task autonmously below:
 ![Agent Chat Example](images/agent_example.png)
 
@@ -63,6 +64,7 @@ After the initialization step, the conversation could proceed automatically. Fin
 ### Supporting Diverse Conversation Patterns
 
 #### Conversations with different levels of autonomy, and human-involvement patterns
+
 On the one hand, one can achieve fully autonomous conversations after an initialization step. On the other hand, AutoGen can be used to implement human-in-the-loop problem-solving by configuring human involvement levels and patterns (e.g., setting the `human_input_mode` to `ALWAYS`), as human involvement is expected and/or desired in many applications.
 
 #### Static and dynamic conversations
@@ -72,34 +74,47 @@ By adopting the conversation-driven control with both programming language and n
 - 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 the next speaker will 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/agentchat_two_users.ipynb), where a student assistant would automatically resort to an expert using function calls.
+  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
 
-
 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/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)
-* [Automated Code Generation and Question Answering with Retrieval Augemented Agents](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb)
+1. **Code Generation, Execution, and Debugging**
+
+   - Automated Task Solving with Code Generation, Execution & Debugging - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)
+   - Auto Code Generation, Execution, Debugging and Human Feedback - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_human_feedback.ipynb)
+   - Automated Code Generation and Question Answering with Retrieval Augmented Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb)
+
+2. **Multi-Agent Collaboration (>3 Agents)**
+
+   - Automated Task Solving with GPT-4 + Multiple Human Users - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_two_users.ipynb)
+   - Automated Task Solving by Group Chat (with 3 group member agents and 1 manager agent) - [View Notebook](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) - [View Notebook](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) - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_research.ipynb)
+   - Automated Task Solving with Coding & Planning Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_planning.ipynb)
+
+3. **Applications**
+
+   - Automated Chess Game Playing & Chitchatting by GPT-4 Agents - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_chess.ipynb)
+   - Automated Continual Learning from New Data - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_stream.ipynb)
+
+4. **Tool Use**
 
+   - **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)
+   - Task Solving with Langchain Provided Tools as Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_function_call.ipynb)
+   - **RAG**: Group Chat with Retrieval Augmented Generation - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
+   - In-depth Guide to OpenAI Utility Functions - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_groupchat_RAG.ipynb)
 
+5. **Agent Teaching and Learning**
+   - Teach Agents New Skills & Reuse via Automated Chat - [View Notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb)
 
 ## For Further Reading
 
-*Interested in the research that leads to this package? Please check the following papers.*
+_Interested in the research that leads to this package? Please check the following papers._
 
-* [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.
+- [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).