File size: 2,717 Bytes
5b2b2db 9eaf8c4 0dad17d 9eaf8c4 0dad17d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 |
# my_memory_logic.py
import os
# We'll import the session-based classes from langchain_core if you have them installed:
# If not, you'll need to install the correct package versions or adapt to your environment.
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
# We'll assume you have a `rag_chain` from your pipeline code or can import it.
# For example:
# from pipeline import rag_chain
# For demonstration, let's just define a dummy "rag_chain" that returns "answer".
# In your real code, import your actual chain.
class DummyRagChain:
def invoke(self, inputs):
# returns a dictionary with "answer"
return {"answer": f"Dummy answer to '{inputs['input']}'."}
rag_chain = DummyRagChain()
###############################################################################
# 1) We'll keep an in-memory store of session_id -> ChatMessageHistory
###############################################################################
store = {} # { "abc123": ChatMessageHistory(...) }
def get_session_history(session_id: str) -> BaseChatMessageHistory:
"""
Retrieve or create a ChatMessageHistory object for the given session_id.
"""
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
###############################################################################
# 2) Create the RunnableWithMessageHistory (conversational chain)
###############################################################################
# If your snippet references `rag_chain`, combine it with get_session_history.
conversational_rag_chain = RunnableWithMessageHistory(
rag_chain, # your main chain (RAG or pipeline)
get_session_history, # function to fetch chat history for a session
input_messages_key="input",
history_messages_key="chat_history",
output_messages_key="answer"
)
###############################################################################
# 3) A convenience function to run a query with session-based memory
###############################################################################
def run_with_session_memory(user_query: str, session_id: str) -> str:
"""
A convenience wrapper that calls our `conversational_rag_chain`
with a specific session_id. This returns the final 'answer'.
"""
response = conversational_rag_chain.invoke(
{"input": user_query},
config={
"configurable": {
"session_id": session_id
}
}
)
return response["answer"]
|