Update links on 0.2 website (#3734)

README.md

@@ -26,7 +26,7 @@ AutoGen is an open-source programming framework for building AI agents and facil
 > -_Maintainers (Sept 6th, 2024)_
 
 
-![AutoGen Overview](https://github.com/microsoft/autogen/blob/main/website/static/img/autogen_agentchat.png)
+![AutoGen Overview](https://github.com/microsoft/autogen/blob/0.2/website/static/img/autogen_agentchat.png)
 
 - AutoGen enables building next-gen LLM applications based on [multi-agent conversations](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat) with minimal effort. It simplifies the orchestration, automation, and optimization of a complex LLM workflow. It maximizes the performance of LLM models and overcomes their weaknesses.
 - It supports [diverse conversation patterns](https://microsoft.github.io/autogen/docs/Use-Cases/agent_chat#supporting-diverse-conversation-patterns) for complex workflows. With customizable and conversable agents, developers can use AutoGen to build a wide range of conversation patterns concerning conversation autonomy,
@@ -171,7 +171,7 @@ Features of this use case include:
 - **Customization**: AutoGen agents can be customized to meet the specific needs of an application. This includes the ability to choose the LLMs to use, the types of human input to allow, and the tools to employ.
 - **Human participation**: AutoGen seamlessly allows human participation. This means that humans can provide input and feedback to the agents as needed.
 
-For [example](https://github.com/microsoft/autogen/blob/main/test/twoagent.py),
+For [example](https://github.com/microsoft/autogen/blob/0.2/test/twoagent.py),
 
 ```python
 from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
@@ -194,9 +194,9 @@ python test/twoagent.py
 
 After the repo is cloned.
 The figure below shows an example conversation flow with AutoGen.
-![Agent Chat Example](https://github.com/microsoft/autogen/blob/main/website/static/img/chat_example.png)
+![Agent Chat Example](https://github.com/microsoft/autogen/blob/0.2/website/static/img/chat_example.png)
 
-Alternatively, the [sample code](https://github.com/microsoft/autogen/blob/main/samples/simple_chat.py) here allows a user to chat with an AutoGen agent in ChatGPT style.
+Alternatively, the [sample code](https://github.com/microsoft/autogen/blob/0.2/samples/simple_chat.py) here allows a user to chat with an AutoGen agent in ChatGPT style.
 Please find more [code examples](https://microsoft.github.io/autogen/docs/Examples#automated-multi-agent-chat) for this feature.
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
@@ -240,7 +240,7 @@ You can find detailed documentation about AutoGen [here](https://microsoft.githu
 
 In addition, you can find:
 
-- [Research](https://microsoft.github.io/autogen/docs/Research), [blogposts](https://microsoft.github.io/autogen/blog) around AutoGen, and [Transparency FAQs](https://github.com/microsoft/autogen/blob/main/TRANSPARENCY_FAQS.md)
+- [Research](https://microsoft.github.io/autogen/docs/Research), [blogposts](https://microsoft.github.io/autogen/blog) around AutoGen, and [Transparency FAQs](https://github.com/microsoft/autogen/blob/0.2/TRANSPARENCY_FAQS.md)
 
 - [Contributing guide](https://microsoft.github.io/autogen/docs/Contribute)
 

notebook/agentchat_MathChat.ipynb

@@ -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 framework allows tool use and human participation 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 conversational 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",
+    "MathChat is an experimental conversational 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/0.2/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",
     "````{=mdx}\n",
     ":::info Requirements\n",

notebook/agentchat_RetrieveChat.ipynb

@@ -10,7 +10,7 @@
     "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation 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 conversational system for retrieval-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 `AssistantAgent` 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, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
+    "RetrieveChat is a conversational system for retrieval-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 `AssistantAgent` 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/0.2/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
     "## Table of Contents\n",
     "We'll demonstrate six examples of using RetrieveChat for code generation and question answering:\n",

notebook/agentchat_RetrieveChat_mongodb.ipynb

@@ -10,7 +10,7 @@
     "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation 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 conversational system for retrieval-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 `AssistantAgent` 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, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
+    "RetrieveChat is a conversational system for retrieval-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 `AssistantAgent` 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/0.2/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
     "## Table of Contents\n",
     "We'll demonstrate six examples of using RetrieveChat for code generation and question answering:\n",

notebook/agentchat_RetrieveChat_pgvector.ipynb

@@ -10,7 +10,7 @@
     "AutoGen offers conversable agents powered by LLM, tool or human, which can be used to perform tasks collectively via automated chat. This framework allows tool use and human participation 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 conversational system for retrieval-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 `AssistantAgent` 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, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
+    "RetrieveChat is a conversational system for retrieval-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 `AssistantAgent` 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/0.2/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
     "## Table of Contents\n",
     "We'll demonstrate six examples of using RetrieveChat for code generation and question answering:\n",

notebook/agentchat_RetrieveChat_qdrant.ipynb

@@ -12,7 +12,7 @@
     "This notebook demonstrates the usage of Qdrant for RAG, based on [agentchat_RetrieveChat.ipynb](https://colab.research.google.com/github/microsoft/autogen/blob/main/notebook/agentchat_RetrieveChat.ipynb).\n",
     "\n",
     "\n",
-    "RetrieveChat is a conversational 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 `AssistantAgent` 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)).\n",
+    "RetrieveChat is a conversational 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 `AssistantAgent` 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/0.2/notebook/agentchat_auto_feedback_from_code_execution.ipynb)).\n",
     "\n",
     "We'll demonstrate usage of RetrieveChat with Qdrant for code generation and question answering w/ human feedback.\n",
     "\n",

notebook/agentchat_agentoptimizer.ipynb

@@ -53,7 +53,7 @@
       "source": [
         "# MathUserProxy with function_call\n",
         "\n",
-        "This agent is a customized MathUserProxy inherits from its [parent class](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/math_user_proxy_agent.py).\n",
+        "This agent is a customized MathUserProxy inherits from its [parent class](https://github.com/microsoft/autogen/blob/0.2/autogen/agentchat/contrib/math_user_proxy_agent.py).\n",
         "\n",
         "It supports using both function_call and python to solve math problems.\n"
       ]

notebook/agentchat_cost_token_tracking.ipynb

@@ -109,7 +109,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
    ]
   },
   {

notebook/agentchat_custom_model.ipynb

@@ -238,7 +238,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/notebook/oai_openai_utils.ipynb) for full code examples of the different methods."
    ]
   },
   {

notebook/agentchat_function_call.ipynb

@@ -104,7 +104,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
    ]
   },
   {

notebook/agentchat_function_call_currency_calculator.ipynb

@@ -104,7 +104,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
    ]
   },
   {

notebook/agentchat_human_feedback.ipynb

@@ -102,7 +102,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
    ]
   },
   {

notebook/agentchat_langchain.ipynb

@@ -139,7 +139,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
    ]
   },
   {

notebook/agentchat_oai_assistant_retrieval.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "## RAG OpenAI Assistants in AutoGen\n",
     "\n",
-    "This notebook shows an example of the [`GPTAssistantAgent`](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/gpt_assistant_agent.py#L16C43-L16C43) with retrieval augmented generation. `GPTAssistantAgent` is an experimental AutoGen agent class that leverages the [OpenAI Assistant API](https://platform.openai.com/docs/assistants/overview) for conversational capabilities,  working with\n",
+    "This notebook shows an example of the [`GPTAssistantAgent`](https://github.com/microsoft/autogen/blob/0.2/autogen/agentchat/contrib/gpt_assistant_agent.py#L16C43-L16C43) with retrieval augmented generation. `GPTAssistantAgent` is an experimental AutoGen agent class that leverages the [OpenAI Assistant API](https://platform.openai.com/docs/assistants/overview) for conversational capabilities,  working with\n",
     "`UserProxyAgent` in AutoGen."
    ]
   },

notebook/agentchat_oai_assistant_twoagents_basic.ipynb

@@ -6,7 +6,7 @@
    "source": [
     "## OpenAI Assistants in AutoGen\n",
     "\n",
-    "This notebook shows a very basic example of the [`GPTAssistantAgent`](https://github.com/microsoft/autogen/blob/main/autogen/agentchat/contrib/gpt_assistant_agent.py#L16C43-L16C43), which is an experimental AutoGen agent class that leverages the [OpenAI Assistant API](https://platform.openai.com/docs/assistants/overview) for conversational capabilities,  working with\n",
+    "This notebook shows a very basic example of the [`GPTAssistantAgent`](https://github.com/microsoft/autogen/blob/0.2/autogen/agentchat/contrib/gpt_assistant_agent.py#L16C43-L16C43), which is an experimental AutoGen agent class that leverages the [OpenAI Assistant API](https://platform.openai.com/docs/assistants/overview) for conversational capabilities,  working with\n",
     "`UserProxyAgent` in AutoGen."
    ]
   },

notebook/agentchat_planning.ipynb

@@ -105,7 +105,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/notebook/oai_openai_utils.ipynb) for full code examples of the different methods.\n",
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/notebook/oai_openai_utils.ipynb) for full code examples of the different methods.\n",
     "\n",
     "## Construct Agents\n",
     "\n",

notebook/agentchat_stream.ipynb

@@ -102,7 +102,7 @@
     "]\n",
     "```\n",
     "\n",
-    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/main/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
+    "You can set the value of config_list in any way you prefer. Please refer to this [notebook](https://github.com/microsoft/autogen/blob/0.2/website/docs/topics/llm_configuration.ipynb) for full code examples of the different methods."
    ]
   },
   {