Add docs.

Author: qiuosier

docs/source/user_guide/large_language_model/autogen_integration.rst

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+AutoGen Integration
+*******************
+
+ADS provides custom LLM clients for `AutoGen <https://microsoft.github.io/autogen/0.2/>`_. This custom client allows you to use LangChain chat models for AutoGen.
+
+.. admonition:: Requirements
+  :class: note
+
+  The LangChain integration requires ``python>=3.9``, ``langchain-community>=0.3`` and ``langchain-openai``.
+
+  .. code-block::
+
+    pip install "langchain-community>0.3" langchain-openai
+
+
+Custom Client Registration
+==========================
+
+AutoGen requires custom clients to be registered with each agent after the agent is created. To simplify the process, ADS provides a global ``register_model_client()`` method to register the client globally. Once registered with ADS, all new agents created subsequently will have the custom client registered automatically.
+
+The following code shows how you can import the custom client and register it with AutoGen. 
+
+.. code-block:: python3
+
+    from ads.llm.autogen.client_v02 import LangChainModelClient, register_custom_client
+
+    # Register the custom LLM globally
+    register_custom_client(LangChainModelClient)
+
+If you don't want the custom client to be registered for all agents. You may skip the above code and still use the ``register_model_client()`` method from each agent.
+
+
+LLM Config
+==========
+
+The LLM config for the ``LangChainModelClient`` should have the following keys:
+
+* ``model_client_cls``, the name of the client class, which should always be ``LangChainModelClient``.
+* ``langchain_cls``, the LangChain chat model class with the full path.
+* ``model``, the model name for AutoGen to identify the model.
+* ``client_params``, the parameters for initializing the LangChain client.
+
+The following keys are optional:
+* ``invoke_params``, the parameters for invoking the chat model.
+* ``function_call_params``, the parameters for invoking the chat model with functions/tools.
+
+Data Science Model Deployment
+-----------------------------
+
+Following is an example LLM config for LLM deployed with AI Quick Action on OCI Data Science Model Deployment:
+
+.. code-block:: python3
+
+    import ads
+    from ads.llm.chat_template import ChatTemplates
+
+    # You may use ADS to config the authentication globally
+    ads.set_auth("security_token", profile="DEFAULT")
+
+    {
+        "model_client_cls": "LangChainModelClient",
+        "langchain_cls": "ads.llm.ChatOCIModelDeploymentVLLM",
+        # Note that you may use a different model name for the `model` in `client_params`.
+        "model": "Mistral-7B",
+        # client_params will be used to initialize the LangChain ChatOCIModelDeploymentVLLM class.
+        "client_params": {
+            "model": "odsc-llm"
+            "endpoint": "<ODSC_ENDPOINT>",
+            "model_kwargs": {
+                "temperature": 0,
+                "max_tokens": 500
+            },
+        }
+        # function_call_params will only be added to the API call when function/tools are added.
+        "function_call_params": {
+            "tool_choice": "auto",
+            "chat_template": ChatTemplates.hermes()
+        }
+    }
+
+
+OCI Generative AI
+-----------------
+
+Following is an example LLM config for the OCI Generative AI service:
+
+.. code-block:: python3
+
+    {
+        "model_client_cls": "LangChainModelClient",
+        "langchain_cls": "langchain_community.chat_models.oci_generative_ai.ChatOCIGenAI",
+        "model": "cohere.command-r-plus",
+        # client_params will be used to initialize the LangChain ChatOCIGenAI class.
+        "client_params": {
+            "model_id": "cohere.command-r-plus",
+            "compartment_id": COMPARTMENT_OCID,
+            "model_kwargs": {"temperature": 0, "max_tokens": 4000},
+            "service_endpoint": "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
+            "auth_type": "SECURITY_TOKEN",
+            "auth_profile": "DEFAULT",
+        },
+    }
+

docs/source/user_guide/large_language_model/index.rst

@@ -39,6 +39,7 @@ ADS is designed to work with LangChain, enabling developers to incorporate vario
 
     training_llm
     langchain_models
+    autogen_integration
     deploy_langchain_application
     retrieval
     guardrails