Tool call support for LangchainLLM (#3542)

mem0/llms/langchain.py

@@ -5,6 +5,7 @@ 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`")
 
@@ -21,6 +22,35 @@ class LangchainLLM(LLMBase):
 
         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]],
@@ -34,32 +64,31 @@ class LangchainLLM(LLMBase):
         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. Not used in Langchain.
-            tool_choice (str, optional): Tool choice method. 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.
         """
-        try:
-            # Convert the messages to LangChain's tuple format
-            langchain_messages = []
-            for message in messages:
-                role = message["role"]
-                content = message["content"]
+        # 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 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")
+        if not langchain_messages:
+            raise ValueError("No valid messages found in the messages list")
 
-            ai_message = self.langchain_model.invoke(langchain_messages)
+        langchain_model = self.langchain_model
+        if tools:
+            langchain_model = langchain_model.bind_tools(tools=tools, tool_choice=tool_choice)
 
-            return ai_message.content
-
-        except Exception as e:
-            raise Exception(f"Error generating response using langchain model: {str(e)}")
+        response: AIMessage = langchain_model.invoke(langchain_messages)
+        return self._parse_response(response, tools)

tests/llms/test_langchain.py

@@ -68,6 +68,51 @@ def test_generate_response(mock_langchain_model):
     assert response == "This is a test response"
 
 
+def test_generate_response_with_tools(mock_langchain_model):
+    config = BaseLlmConfig(model=mock_langchain_model, temperature=0.7, max_tokens=100, api_key="test-api-key")
+    llm = LangchainLLM(config)
+
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": "Add a new memory: Today is a sunny day."},
+    ]
+    tools = [
+        {
+            "type": "function",
+            "function": {
+                "name": "add_memory",
+                "description": "Add a memory",
+                "parameters": {
+                    "type": "object",
+                    "properties": {"data": {"type": "string", "description": "Data to add to memory"}},
+                    "required": ["data"],
+                },
+            },
+        }
+    ]
+
+    mock_response = Mock()
+    mock_response.content = "I've added the memory for you."
+
+    mock_tool_call = Mock()
+    mock_tool_call.__getitem__ = Mock(
+        side_effect={"name": "add_memory", "args": {"data": "Today is a sunny day."}}.__getitem__
+    )
+
+    mock_response.tool_calls = [mock_tool_call]
+    mock_langchain_model.invoke.return_value = mock_response
+    mock_langchain_model.bind_tools.return_value = mock_langchain_model
+
+    response = llm.generate_response(messages, tools=tools)
+
+    mock_langchain_model.invoke.assert_called_once()
+
+    assert response["content"] == "I've added the memory for you."
+    assert len(response["tool_calls"]) == 1
+    assert response["tool_calls"][0]["name"] == "add_memory"
+    assert response["tool_calls"][0]["arguments"] == {"data": "Today is a sunny day."}
+
+
 def test_invalid_model():
     """Test that LangchainLLM raises an error with an invalid model."""
     config = BaseLlmConfig(model="not-a-valid-model-instance", temperature=0.7, max_tokens=100, api_key="test-api-key")