first commit

llms/langchain.py

@@ -0,0 +1,94 @@
+from typing import Dict, List, Optional
+
+from mem0.configs.llms.base import BaseLlmConfig
+from mem0.llms.base import LLMBase
+
+try:
+    from langchain.chat_models.base import BaseChatModel
+    from langchain_core.messages import AIMessage
+except ImportError:
+    raise ImportError("langchain is not installed. Please install it using `pip install langchain`")
+
+
+class LangchainLLM(LLMBase):
+    def __init__(self, config: Optional[BaseLlmConfig] = None):
+        super().__init__(config)
+
+        if self.config.model is None:
+            raise ValueError("`model` parameter is required")
+
+        if not isinstance(self.config.model, BaseChatModel):
+            raise ValueError("`model` must be an instance of BaseChatModel")
+
+        self.langchain_model = self.config.model
+
+    def _parse_response(self, response: AIMessage, tools: Optional[List[Dict]]):
+        """
+        Process the response based on whether tools are used or not.
+
+        Args:
+            response: AI Message.
+            tools: The list of tools provided in the request.
+
+        Returns:
+            str or dict: The processed response.
+        """
+        if not tools:
+            return response.content
+
+        processed_response = {
+            "content": response.content,
+            "tool_calls": [],
+        }
+
+        for tool_call in response.tool_calls:
+            processed_response["tool_calls"].append(
+                {
+                    "name": tool_call["name"],
+                    "arguments": tool_call["args"],
+                }
+            )
+
+        return processed_response
+
+    def generate_response(
+        self,
+        messages: List[Dict[str, str]],
+        response_format=None,
+        tools: Optional[List[Dict]] = None,
+        tool_choice: str = "auto",
+    ):
+        """
+        Generate a response based on the given messages using langchain_community.
+
+        Args:
+            messages (list): List of message dicts containing 'role' and 'content'.
+            response_format (str or object, optional): Format of the response. Not used in Langchain.
+            tools (list, optional): List of tools that the model can call.
+            tool_choice (str, optional): Tool choice method.
+
+        Returns:
+            str: The generated response.
+        """
+        # Convert the messages to LangChain's tuple format
+        langchain_messages = []
+        for message in messages:
+            role = message["role"]
+            content = message["content"]
+
+            if role == "system":
+                langchain_messages.append(("system", content))
+            elif role == "user":
+                langchain_messages.append(("human", content))
+            elif role == "assistant":
+                langchain_messages.append(("ai", content))
+
+        if not langchain_messages:
+            raise ValueError("No valid messages found in the messages list")
+
+        langchain_model = self.langchain_model
+        if tools:
+            langchain_model = langchain_model.bind_tools(tools=tools, tool_choice=tool_choice)
+
+        response: AIMessage = langchain_model.invoke(langchain_messages)
+        return self._parse_response(response, tools)