Add LangChain client for AutoGen.

Author: qiuosier

ads/llm/autogen/__init__.py

@@ -0,0 +1,96 @@
+# 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 LangChain LLM client for AutoGen
+# 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 logging
+from typing import Dict, List, Union
+from types import SimpleNamespace
+
+
+from autogen import ModelClient
+from langchain_core.messages import AIMessage
+
+logger = logging.getLogger(__name__)
+
+
+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
+        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))
+        self.client_class = config.pop("model_client_cls")
+        # Parameters for the model
+        self.model_name = config.get("model")
+        # 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)
+        # Initialize the LangChain client
+        self.model = langchain_cls(**config)
+
+    def create(self, params) -> ModelClient.ModelClientResponseProtocol:
+        streaming = params.get("stream", False)
+        num_of_responses = params.get("n", 1)
+        messages = params.get("messages", [])
+
+        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 = self.model.invoke(messages)
+            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.content 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 {}