Improve LangChain memory implementation

- Swap to RunnableWithMemory
- Add verbosity flag

src/reviewllama/llm.py

@@ -1,11 +1,13 @@
 from dataclasses import dataclass
 from typing import Any
 
-from langchain.memory import ConversationBufferMemory
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.schema import BaseMessage
+from langchain_community.chat_message_histories import ChatMessageHistory
+from langchain_core.chat_history import BaseChatMessageHistory
+from langchain_core.messages import BaseMessage
+from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
 from langchain_core.runnables import RunnableLambda
 from langchain_core.runnables.base import RunnableSerializable
+from langchain_core.runnables.history import RunnableWithMessageHistory
 from langchain_core.runnables.passthrough import RunnablePassthrough
 from langchain_core.vectorstores import VectorStoreRetriever
 from langchain_ollama import ChatOllama
@@ -17,20 +19,21 @@ from .configs import OllamaConfig
 
 @dataclass(frozen=True)
 class ChatClient:
-    chain: RunnableSerializable[dict[str, Any], BaseMessage]
-    memory: ConversationBufferMemory
+    chain: RunnableWithMessageHistory
+    message_history: dict[str, BaseChatMessageHistory]
 
-    def get_last_response_or_none(self):
+    def get_last_response_or_none(self, session_id: str = "default"):
         try:
-            return self.memory.chat_memory.messages[-1]
-        except IndexError:
+            messages = self.message_history[session_id].messages
+            return messages[-1] if messages else None
+        except (KeyError, IndexError):
             return None
 
 
 def create_chat_chain(
     config: OllamaConfig,
 ) -> RunnableSerializable[dict[str, Any], BaseMessage]:
-    """Create the chat chain for use by the code."""
+    """Create the base chat chain for use by the code."""
     llm = ChatOllama(
         model=config.chat_model,
         base_url=config.base_url,
@@ -40,28 +43,17 @@ def create_chat_chain(
     prompt = ChatPromptTemplate.from_messages(
         [
             ("system", config.system_prompt),
-            MessagesPlaceholder("chat_history"),
+            MessagesPlaceholder("history"),
             ("human", "Context\n{context}\n\nQuestion:\n{input}"),
         ]
     )
 
-    def get_chat_history(inputs: dict) -> list:
-        """Extract chat history from memory object."""
-        try:
-            return inputs["memory"].chat_memory.messages
-        except AttributeError:
-            return []
-
     def get_context(inputs: dict) -> str:
         """Extract the RAG context from the input object"""
-        try:
-            return inputs["context"]
-        except AttributeError:
-            return ""
+        return inputs.get("context", "")
 
     return (
         RunnablePassthrough.assign(
-            chat_history=RunnableLambda(get_chat_history),
             context=RunnableLambda(get_context),
         )
         | prompt
@@ -69,32 +61,48 @@ def create_chat_chain(
     )
 
 
-def create_memory():
-    return ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+def get_session_history(
+    session_id: str, message_history: dict[str, BaseChatMessageHistory]
+) -> BaseChatMessageHistory:
+    """Get or create chat message history for a session."""
+    if session_id not in message_history:
+        message_history[session_id] = ChatMessageHistory()
+    return message_history[session_id]
 
 
 def create_chat_client(config: OllamaConfig):
+    base_chain = create_chat_chain(config)
+    message_history = {}
+
+    chain_with_history = RunnableWithMessageHistory(
+        base_chain,
+        lambda session_id: get_session_history(session_id, message_history),
+        input_messages_key="input",
+        history_messages_key="history",
+    )
+
     return ChatClient(
-        chain=create_chat_chain(config),
-        memory=create_memory(),
+        chain=chain_with_history,
+        message_history=message_history,
     )
 
 
 def chat_with_client(
-    client: ChatClient, message: str, retriever: VectorStoreRetriever | None = None
-) -> ChatClient:
-
+    client: ChatClient,
+    message: str,
+    retriever: VectorStoreRetriever | None = None,
+    session_id: str = "default",
+    verbose: bool = False,
+) -> str:
+    """Chat with the client and return the response content."""
     if retriever:
         context = get_context_from_store(message, retriever)
     else:
         context = ""
 
     response = client.chain.invoke(
-        {"input": message, "memory": client.memory, "context": context}
+        {"input": message, "context": context},
+        config={"configurable": {"session_id": session_id}},
     )
 
-    memory = client.memory
-    memory.chat_memory.add_user_message(message)
-    memory.chat_memory.add_ai_message(response.content)
-
-    return ChatClient(chain=client.chain, memory=memory)
+    return response.content