Link updates for AG2

.devcontainer/README.md

@@ -26,7 +26,7 @@ These configurations can be used with Codespaces and locally.
 - **Usage**: Recommended for developers who are contributing to the AutoGen project.
 - **Building the Image**: Run `docker build -f dev/Dockerfile -t ag2_dev_img .`.
 - **Using with Codespaces**: `Code > Codespaces > Click on ...> New with options > Choose "dev" as devcontainer configuration`. This image may require a Codespace with at least 64GB of disk space.
-- **Before using**: We highly encourage all potential contributors to read the [AutoGen Contributing](https://ag2ai.github.io/autogen/docs/Contribute) page prior to submitting any pull requests.
+- **Before using**: We highly encourage all potential contributors to read the [AutoGen Contributing](https://ag2ai.github.io/ag2/docs/Contribute) page prior to submitting any pull requests.
 
 
 ## Customizing Dockerfiles

.github/PULL_REQUEST_TEMPLATE.md

@@ -1,4 +1,4 @@
-<!-- Thank you for your contribution! Please review https://ag2ai.github.io/autogen/docs/Contribute before opening a pull request. -->
+<!-- Thank you for your contribution! Please review https://ag2ai.github.io/ag2/docs/Contribute before opening a pull request. -->
 
 <!-- Please add a reviewer to the assignee section when you create a PR. If you don't have the access to it, we will shortly find a reviewer and assign them to your PR. -->
 
@@ -12,6 +12,6 @@
 
 ## Checks
 
-- [ ] I've included any doc changes needed for https://ag2ai.github.io/autogen/. See https://ag2ai.github.io/autogen/docs/Contribute#documentation to build and test documentation locally.
+- [ ] I've included any doc changes needed for https://ag2ai.github.io/autogen/. See https://ag2ai.github.io/ag2/docs/Contribute#documentation to build and test documentation locally.
 - [ ] I've added tests (if relevant) corresponding to the changes introduced in this PR.
 - [ ] I've made sure all auto checks have passed.

MAINTAINERS.md

@@ -27,7 +27,7 @@
 | Evan David *    | [evandavid1](https://github.com/evandavid1)                | -                      | gpt assistant, group chat, rag, autobuild |
 
 ## I would like to join this list. How can I help the project?
-> We're always looking for new contributors to join our team and help improve the project. For more information, please refer to our [CONTRIBUTING](https://ag2ai.github.io/autogen/docs/contributor-guide/contributing) guide.
+> We're always looking for new contributors to join our team and help improve the project. For more information, please refer to our [CONTRIBUTING](https://ag2ai.github.io/ag2/docs/contributor-guide/contributing) guide.
 
 
 ## Are you missing from this list?

README.md

@@ -195,7 +195,7 @@ The figure below shows an example conversation flow with AG2.
 ![Agent Chat Example](./website/static/img/chat_example.png)
 
 
-Alternatively, the [sample code](https://github.com/ag2ai/build-with-autogen/blob/main/samples/simple_chat.py) here allows a user to chat with an AG2 agent in ChatGPT style.
+Alternatively, the [sample code](https://github.com/ag2ai/build-with-ag2/blob/main/samples/simple_chat.py) here allows a user to chat with an AG2 agent in ChatGPT style.
 Please find more [code examples](https://ag2ai.github.io/ag2/docs/Examples#automated-multi-agent-chat) for this feature.
 
 <p align="right" style="font-size: 14px; color: #555; margin-top: 20px;">
@@ -254,12 +254,12 @@ In addition, you can find:
 ## CookBook
 
 Explore detailed implementations with sample code and applications to help you get started with AG2.
-[Cookbook](https://github.com/ag2ai/build-with-autogen)
+[Cookbook](https://github.com/ag2ai/build-with-ag2)
 
 
 ## Related Papers
 
-[AG2](https://arxiv.org/abs/2308.08155)
+[AutoGen](https://arxiv.org/abs/2308.08155)
 
 ```
 @inproceedings{wu2023autogen,

autogen/agentchat/conversable_agent.py

@@ -164,7 +164,7 @@ def __init__(
             except TypeError as e:
                 raise TypeError(
                     "Please implement __deepcopy__ method for each value class in llm_config to support deepcopy."
-                    " Refer to the docs for more details: https://ag2ai.github.io/autogen/docs/topics/llm_configuration#adding-http-client-in-llm_config-for-proxy"
+                    " Refer to the docs for more details: https://ag2ai.github.io/ag2/docs/topics/llm_configuration#adding-http-client-in-llm_config-for-proxy"
                 ) from e
 
         self._validate_llm_config(llm_config)

autogen/oai/completion.py

@@ -742,7 +742,7 @@ def create(
                 E.g., `prompt="Complete the following sentence: {prefix}, context={"prefix": "Today I feel"}`.
                 The actual prompt will be:
                 "Complete the following sentence: Today I feel".
-                More examples can be found at [templating](https://ag2ai.github.io/autogen/docs/Use-Cases/enhanced_inference#templating).
+                More examples can be found at [templating](https://ag2ai.github.io/ag2/docs/Use-Cases/enhanced_inference#templating).
             use_cache (bool, Optional): Whether to use cached responses.
             config_list (List, Optional): List of configurations for the completion to try.
                 The first one that does not raise an error will be used.
@@ -804,7 +804,7 @@ def yes_or_no_filter(context, config, response):
         logger.warning(
             "Completion.create is deprecated in autogen, pyautogen v0.2 and openai>=1. "
             "The new openai requires initiating a client for inference. "
-            "Please refer to https://ag2ai.github.io/autogen/docs/Use-Cases/enhanced_inference#api-unification"
+            "Please refer to https://ag2ai.github.io/ag2/docs/Use-Cases/enhanced_inference#api-unification"
         )
         if ERROR:
             raise ERROR

autogen/oai/gemini.py

@@ -171,7 +171,7 @@ def create(self, params: Dict) -> ChatCompletion:
             raise ValueError(
                 "Please provide a model name for the Gemini Client. "
                 "You can configure it in the OAI Config List file. "
-                "See this [LLM configuration tutorial](https://ag2ai.github.io/autogen/docs/topics/llm_configuration/) for more details."
+                "See this [LLM configuration tutorial](https://ag2ai.github.io/ag2/docs/topics/llm_configuration/) for more details."
             )
 
         params.get("api_type", "google")  # not used

notebook/agentchat_MathChat.ipynb

@@ -7,7 +7,7 @@
    "source": [
     "# Auto Generated Agent Chat: Using MathChat to Solve Math Problems\n",
     "\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://ag2ai.github.io/autogen/docs/Use-Cases/agent_chat).\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://ag2ai.github.io/ag2/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/ag2ai/ag2/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://ag2ai.github.io/autogen/blog/2023/06/28/MathChat).\n",
     "\n",
@@ -31,7 +31,7 @@
    "source": [
     "## Set your API Endpoint\n",
     "\n",
-    "The [`config_list_from_json`](https://ag2ai.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
+    "The [`config_list_from_json`](https://ag2ai.github.io/ag2/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
    ]
   },
   {

notebook/agentchat_RetrieveChat.ipynb

@@ -8,7 +8,7 @@
     "# Using RetrieveChat for Retrieve Augmented Code Generation and Question Answering\n",
     "\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.\n",
-    "Please find documentation about this feature [here](https://ag2ai.github.io/autogen/docs/Use-Cases/agent_chat).\n",
+    "Please find documentation about this feature [here](https://ag2ai.github.io/ag2/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/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
@@ -43,7 +43,7 @@
    "source": [
     "## Set your API Endpoint\n",
     "\n",
-    "The [`config_list_from_json`](https://ag2ai.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
+    "The [`config_list_from_json`](https://ag2ai.github.io/ag2/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
    ]
   },
   {
@@ -141,8 +141,8 @@
     ")\n",
     "\n",
     "# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
-    "# Refer to https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
-    "# and https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/vectordb/chromadb\n",
+    "# Refer to https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
+    "# and https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/vectordb/chromadb\n",
     "# for more information on the RetrieveUserProxyAgent and ChromaVectorDB\n",
     "ragproxyagent = RetrieveUserProxyAgent(\n",
     "    name=\"ragproxyagent\",\n",

notebook/agentchat_RetrieveChat_mongodb.ipynb

@@ -8,7 +8,7 @@
     "# Using RetrieveChat Powered by MongoDB Atlas for Retrieve Augmented Code Generation and Question Answering\n",
     "\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.\n",
-    "Please find documentation about this feature [here](https://ag2ai.github.io/autogen/docs/Use-Cases/agent_chat).\n",
+    "Please find documentation about this feature [here](https://ag2ai.github.io/ag2/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/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
@@ -123,8 +123,8 @@
     ")\n",
     "\n",
     "# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
-    "# Refer to https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
-    "# and https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/vectordb/mongodb\n",
+    "# Refer to https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
+    "# and https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/vectordb/mongodb\n",
     "# for more information on the RetrieveUserProxyAgent and MongoDBAtlasVectorDB\n",
     "ragproxyagent = RetrieveUserProxyAgent(\n",
     "    name=\"ragproxyagent\",\n",

notebook/agentchat_RetrieveChat_pgvector.ipynb

@@ -8,7 +8,7 @@
     "# Using RetrieveChat Powered by PGVector for Retrieve Augmented Code Generation and Question Answering\n",
     "\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.\n",
-    "Please find documentation about this feature [here](https://ag2ai.github.io/autogen/docs/Use-Cases/agent_chat).\n",
+    "Please find documentation about this feature [here](https://ag2ai.github.io/ag2/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/ag2ai/ag2/blob/main/notebook/agentchat_auto_feedback_from_code_execution.ipynb)). Essentially, `RetrieveUserProxyAgent` implement a different auto-reply mechanism corresponding to the RetrieveChat prompts.\n",
     "\n",
@@ -67,7 +67,7 @@
    "source": [
     "## Set your API Endpoint\n",
     "\n",
-    "The [`config_list_from_json`](https://ag2ai.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
+    "The [`config_list_from_json`](https://ag2ai.github.io/ag2/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
    ]
   },
   {
@@ -175,8 +175,8 @@
     "sentence_transformer_ef = SentenceTransformer(\"all-distilroberta-v1\").encode\n",
     "\n",
     "# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
-    "# Refer to https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
-    "# and https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/vectordb/pgvectordb\n",
+    "# Refer to https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
+    "# and https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/vectordb/pgvectordb\n",
     "# for more information on the RetrieveUserProxyAgent and PGVectorDB\n",
     "ragproxyagent = RetrieveUserProxyAgent(\n",
     "    name=\"ragproxyagent\",\n",

notebook/agentchat_RetrieveChat_qdrant.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "## Set your API Endpoint\n",
     "\n",
-    "The [`config_list_from_json`](https://ag2ai.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
+    "The [`config_list_from_json`](https://ag2ai.github.io/ag2/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
    ]
   },
   {
@@ -167,8 +167,8 @@
     "client = QdrantClient(\":memory:\")\n",
     "\n",
     "# 2. create the RetrieveUserProxyAgent instance named \"ragproxyagent\"\n",
-    "# Refer to https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
-    "# and https://ag2ai.github.io/autogen/docs/reference/agentchat/contrib/vectordb/qdrant\n",
+    "# Refer to https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/retrieve_user_proxy_agent\n",
+    "# and https://ag2ai.github.io/ag2/docs/reference/agentchat/contrib/vectordb/qdrant\n",
     "# for more information on the RetrieveUserProxyAgent and QdrantVectorDB\n",
     "ragproxyagent = RetrieveUserProxyAgent(\n",
     "    name=\"ragproxyagent\",\n",

notebook/agentchat_agentoptimizer.ipynb

@@ -7,7 +7,7 @@
         "# AgentOptimizer: An Agentic Way to Train Your LLM Agent\n",
         "\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.\n",
-        "Please find documentation about this feature [here](https://ag2ai.github.io/autogen/docs/Use-Cases/agent_chat).\n",
+        "Please find documentation about this feature [here](https://ag2ai.github.io/ag2/docs/Use-Cases/agent_chat).\n",
         "\n",
         "In traditional ML pipeline, we train a model by updating its parameter according to the loss on the training set, while in the era of LLM agents, how should we train an agent? Here, we take an initial step towards the agent training. Inspired by the [function calling](https://platform.openai.com/docs/guides/function-calling) capabilities provided by OpenAI, we draw an analogy between model parameters and agent functions/skills, and update agent’s functions/skills based on its historical performance on the training set. As an agentic way of training an agent, our approach help enhance the agents’ abilities without requiring access to the LLMs parameters.\n",
         "\n",

notebook/agentchat_cost_token_tracking.ipynb

@@ -53,7 +53,7 @@
    "source": [
     "## Set your API Endpoint\n",
     "\n",
-    "The [`config_list_from_json`](https://ag2ai.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
+    "The [`config_list_from_json`](https://ag2ai.github.io/ag2/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n"
    ]
   },
   {

notebook/agentchat_custom_model.ipynb

@@ -210,7 +210,7 @@
    "source": [
     "## Set your API Endpoint\n",
     "\n",
-    "The [`config_list_from_json`](https://ag2ai.github.io/autogen/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n",
+    "The [`config_list_from_json`](https://ag2ai.github.io/ag2/docs/reference/oai/openai_utils#config_list_from_json) function loads a list of configurations from an environment variable or a json file.\n",
     "\n",
     "It first looks for an environment variable of a specified name (\"OAI_CONFIG_LIST\" in this example), which needs to be a valid json string. If that variable is not found, it looks for a json file with the same name. It filters the configs by models (you can filter by other keys as well).\n",
     "\n",