Support using LangChain Chat Models with AutoGen v0.2

ads/llm/autogen/client_v02.py

@@ -0,0 +1,282 @@
+# coding: utf-8
+# Copyright (c) 2016, 2024, Oracle and/or its affiliates.  All rights reserved.
+# This software is dual-licensed to you under the Universal Permissive License (UPL) 1.0 as shown at https://oss.oracle.com/licenses/upl or Apache License 2.0 as shown at http://www.apache.org/licenses/LICENSE-2.0. You may choose either license.
+
+"""This module contains the custom LLM client for AutoGen v0.2 to use LangChain chat models.
+https://microsoft.github.io/autogen/0.2/blog/2024/01/26/Custom-Models/
+
+To use the custom client:
+1. Prepare the LLM config, including the parameters for initializing the LangChain client.
+2. Register the custom LLM
+
+The LLM config should config the following keys:
+* model_client_cls: Required by AutoGen to identify the custom client. It should be "LangChainModelClient"
+* langchain_cls: LangChain class including the full import path.
+* model: Name of the model to be used by AutoGen
+* client_params: A dictionary containing the parameters to initialize the LangChain chat model.
+
+Although the `LangChainModelClient` is designed to be generic and can potentially support any LangChain chat model,
+the invocation depends on the server API spec and it may not be compatible with some implementations.
+
+Following is an example config for OCI Generative AI service:
+{
+    "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": 2048},
+        # Update the authentication method as needed
+        "auth_type": "SECURITY_TOKEN",
+        "auth_profile": "DEFAULT",
+        # You may need to specify `service_endpoint` if the service is in a different region.
+    },
+}
+
+Following is an example config for OCI Data Science Model Deployment:
+{
+    "model_client_cls": "LangChainModelClient",
+    "langchain_cls": "ads.llm.ChatOCIModelDeploymentVLLM",
+    "model": "odsc-llm",
+    "endpoint": "https://MODEL_DEPLOYMENT_URL/predict",
+    "model_kwargs": {"temperature": 0.1, "max_tokens": 2048},
+    # 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.mistral(),
+    },
+}
+
+Note that if `client_params` is not specified in the config, all arguments from the config except
+`model_client_cls` and `langchain_cls`, and `function_call_params`, will be used to initialize
+the LangChain chat model.
+
+The `function_call_params` will only be used for function/tool calling when tools are specified.
+
+To register the custom client:
+
+from ads.llm.autogen.client_v02 import LangChainModelClient, register_custom_client
+register_custom_client(LangChainModelClient)
+
+Once registered with ADS, the custom LLM class will be auto-registered for all new agents.
+There is no need to call `register_model_client()` on each agent.
+
+References:
+https://microsoft.github.io/autogen/0.2/docs/notebooks/agentchat_huggingface_langchain/
+https://github.com/microsoft/autogen/blob/0.2/notebook/agentchat_custom_model.ipynb
+
+"""
+import copy
+import importlib
+import json
+import logging
+from typing import Any, Dict, List, Union
+from types import SimpleNamespace
+
+from autogen import ModelClient
+from autogen.oai.client import OpenAIWrapper, PlaceHolderClient
+from langchain_core.messages import AIMessage
+
+
+logger = logging.getLogger(__name__)
+
+# custom_clients is a dictionary mapping the name of the class to the actual class
+custom_clients = {}
+
+# There is a bug in GroupChat when using custom client:
+# https://github.com/microsoft/autogen/issues/2956
+# Here we will be patching the OpenAIWrapper to fix the issue.
+# With this patch, you only need to register the client once with ADS.
+# For example:
+#
+# from ads.llm.autogen.client_v02 import LangChainModelClient, register_custom_client
+# register_custom_client(LangChainModelClient)
+#
+# This patch will auto-register the custom LLM to all new agents.
+# So there is no need to call `register_model_client()` on each agent.
+OpenAIWrapper._original_register_default_client = OpenAIWrapper._register_default_client
+
+
+def _new_register_default_client(
+    self: OpenAIWrapper, config: Dict[str, Any], openai_config: Dict[str, Any]
+) -> None:
+    """This is a patched version of the _register_default_client() method
+    to automatically register custom client for agents.
+    """
+    model_client_cls_name = config.get("model_client_cls")
+    if model_client_cls_name in custom_clients:
+        self._clients.append(PlaceHolderClient(config))
+        self.register_model_client(custom_clients[model_client_cls_name])
+    else:
+        self._original_register_default_client(
+            config=config, openai_config=openai_config
+        )
+
+
+# Patch the _register_default_client() method
+OpenAIWrapper._register_default_client = _new_register_default_client
+
+
+def register_custom_client(client_class):
+    """Registers custom client for AutoGen."""
+    if client_class.__name__ not in custom_clients:
+        custom_clients[client_class.__name__] = client_class
+
+
+def _convert_to_langchain_tool(tool):
+    """Converts the OpenAI tool spec to LangChain tool spec."""
+    if tool["type"] == "function":
+        tool = tool["function"]
+        required = tool["parameters"].get("required", [])
+        properties = copy.deepcopy(tool["parameters"]["properties"])
+        for key in properties.keys():
+            val = properties[key]
+            val["default"] = key in required
+        return {
+            "title": tool["name"],
+            "description": tool["description"],
+            "properties": properties,
+        }
+    raise NotImplementedError(f"Type {tool['type']} is not supported.")
+
+
+def _convert_to_openai_tool_call(tool_call):
+    """Converts the LangChain tool call in AI message to OpenAI tool call."""
+    return {
+        "id": tool_call.get("id"),
+        "function": {
+            "name": tool_call.get("name"),
+            "arguments": (
+                ""
+                if tool_call.get("args") is None
+                else json.dumps(tool_call.get("args"))
+            ),
+        },
+        "type": "function",
+    }
+
+
+class Message(AIMessage):
+    """Represents message returned from the LLM."""
+
+    @classmethod
+    def from_message(cls, message: AIMessage):
+        """Converts from LangChain AIMessage."""
+        message = copy.deepcopy(message)
+        message.__class__ = cls
+        message.tool_calls = [
+            _convert_to_openai_tool_call(tool) for tool in message.tool_calls
+        ]
+        return message
+
+    @property
+    def function_call(self):
+        """Function calls."""
+        return self.tool_calls
+
+
+class LangChainModelClient(ModelClient):
+    """Represents a model client wrapping a LangChain chat model."""
+
+    def __init__(self, config: dict, **kwargs) -> None:
+        super().__init__()
+        logger.info("LangChain model client config: %s", str(config))
+        # Make a copy of the config since we are popping some keys
+        config = copy.deepcopy(config)
+        # model_client_cls will always be LangChainModelClient
+        self.client_class = config.pop("model_client_cls")
+
+        # model_name is used in constructing the response.
+        self.model_name = config.get("model", "")
+
+        # If the config specified function_call_params,
+        # Pop the params and use them only for tool calling.
+        self.function_call_params = config.pop("function_call_params", {})
+
+        # If the config specified invoke_params,
+        # Pop the params and use them only for invoking.
+        self.invoke_params = config.pop("invoke_params", {})
+
+        # Import the LangChain class
+        if "langchain_cls" not in config:
+            raise ValueError("Missing langchain_cls in LangChain Model Client config.")
+        module_cls = config.pop("langchain_cls")
+        module_name, cls_name = str(module_cls).rsplit(".", 1)
+        langchain_module = importlib.import_module(module_name)
+        langchain_cls = getattr(langchain_module, cls_name)
+
+        # If the config specified client_params,
+        # Only use the client_params to initialize the LangChain model.
+        # Otherwise, use the config
+        self.client_params = config.get("client_params", config)
+
+        # Initialize the LangChain client
+        self.model = langchain_cls(**self.client_params)
+
+    def create(self, params) -> ModelClient.ModelClientResponseProtocol:
+        """Creates a LLM completion for a given config.
+
+        Parameters
+        ----------
+        params : dict
+            OpenAI API compatible parameters, including all the keys from llm_config.
+
+        Returns
+        -------
+        ModelClientResponseProtocol
+            Response from LLM
+
+        """
+        streaming = params.get("stream", False)
+        # TODO: num_of_responses
+        num_of_responses = params.get("n", 1)
+        messages = params.pop("messages", [])
+
+        invoke_params = copy.deepcopy(self.invoke_params)
+
+        tools = params.get("tools")
+        if tools:
+            model = self.model.bind_tools(
+                [_convert_to_langchain_tool(tool) for tool in tools]
+            )
+            # invoke_params["tools"] = tools
+            invoke_params.update(self.function_call_params)
+        else:
+            model = self.model
+
+        response = SimpleNamespace()
+        response.choices = []
+        response.model = self.model_name
+
+        if streaming and messages:
+            # If streaming is enabled and has messages, then iterate over the chunks of the response.
+            raise NotImplementedError()
+        else:
+            # If streaming is not enabled, send a regular chat completion request
+            ai_message = model.invoke(messages, **invoke_params)
+            choice = SimpleNamespace()
+            choice.message = Message.from_message(ai_message)
+            response.choices.append(choice)
+        return response
+
+    def message_retrieval(
+        self, response: ModelClient.ModelClientResponseProtocol
+    ) -> Union[List[str], List[ModelClient.ModelClientResponseProtocol.Choice.Message]]:
+        """
+        Retrieve and return a list of strings or a list of Choice.Message from the response.
+
+        NOTE: if a list of Choice.Message is returned, it currently needs to contain the fields of OpenAI's ChatCompletion Message object,
+        since that is expected for function or tool calling in the rest of the codebase at the moment, unless a custom agent is being used.
+        """
+        return [choice.message for choice in response.choices]
+
+    def cost(self, response: ModelClient.ModelClientResponseProtocol) -> float:
+        response.cost = 0
+        return 0
+
+    @staticmethod
+    def get_usage(response: ModelClient.ModelClientResponseProtocol) -> Dict:
+        """Return usage summary of the response using RESPONSE_USAGE_KEYS."""
+        return {}

docs/source/user_guide/large_language_model/autogen_integration.rst

@@ -0,0 +1,106 @@
+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:: bash
+
+    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