Update my_memory_logic.py

my_memory_logic.py

@@ -2,41 +2,28 @@
 
 import os
 
-# Import the "run_with_chain_context" function from pipeline.py
-# This function must accept a dict with { "input": ..., "chat_history": ... }
-# and return a dict with { "answer": ... }.
-from pipeline import run_with_chain_context
+# Import the PipelineRunnable from pipeline.py
+from pipeline import pipeline_runnable
 
-# For session-based chat history
 from langchain_core.chat_history import BaseChatMessageHistory
 from langchain_community.chat_message_histories import ChatMessageHistory
 from langchain_core.runnables.history import RunnableWithMessageHistory
 
 ###############################################################################
-# 1) In-Memory Store: session_id -> ChatMessageHistory
+# 1) In-memory store: session_id -> ChatMessageHistory
 ###############################################################################
-store = {}  # e.g., { "abc123": ChatMessageHistory(...) }
+store = {}  # e.g. { "abc123": ChatMessageHistory() }
 
 def get_session_history(session_id: str) -> BaseChatMessageHistory:
-    """
-    Retrieve (or create) a ChatMessageHistory for the given session_id.
-    This ensures each session_id has its own conversation transcripts.
-    """
     if session_id not in store:
         store[session_id] = ChatMessageHistory()
     return store[session_id]
 
 ###############################################################################
-# 2) Build a RunnableWithMessageHistory that wraps "run_with_chain_context"
+# 2) RunnableWithMessageHistory referencing pipeline_runnable
 ###############################################################################
-# "run_with_chain_context" must be a function returning a dict,
-# e.g. { "answer": "... final string ..." }
-# input_messages_key -> "input"
-# history_messages_key -> "chat_history"
-# output_messages_key -> "answer"
-
 conversational_rag_chain = RunnableWithMessageHistory(
-    run_with_chain_context,    # from pipeline.py
+    pipeline_runnable,   # The Runnable from pipeline.py
     get_session_history,
     input_messages_key="input",
     history_messages_key="chat_history",
@@ -44,12 +31,12 @@ conversational_rag_chain = RunnableWithMessageHistory(
 )
 
 ###############################################################################
-# 3) A convenience function that calls our chain with session-based memory
+# 3) Convenience function to run a query with session-based memory
 ###############################################################################
 def run_with_session_memory(user_query: str, session_id: str) -> str:
     """
-    Calls the 'conversational_rag_chain' with a given session_id and user_query.
-    This returns the final 'answer' from run_with_chain_context.
+    Calls our `conversational_rag_chain` with session_id,
+    returns the final 'answer' from pipeline_runnable.
     """
     response = conversational_rag_chain.invoke(
         {"input": user_query},