UPDATE MEMORY.PY WITH RESPOECT TO ALL THE CODE

my_memory_logic.py

@@ -1,48 +1,42 @@
 # 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.
+# Import your actual RAG chain (or pipeline) from pipeline.py
+# We'll assume `rag_chain` is exposed by pipeline.py
+from pipeline import rag_chain  
+
+# We'll import the session-based classes from langchain_core
+# If they're in different modules, adjust accordingly.
 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
+# 1) We'll keep an in-memory store mapping session_id -> ChatMessageHistory
 ###############################################################################
-store = {}  # { "abc123": ChatMessageHistory(...) }
+store = {}  # e.g., { "abc123": ChatMessageHistory(...) }
 
 def get_session_history(session_id: str) -> BaseChatMessageHistory:
     """
     Retrieve or create a ChatMessageHistory object for the given session_id.
+    This ensures each session_id has its own conversation history.
     """
     if session_id not in store:
         store[session_id] = ChatMessageHistory()
     return store[session_id]
 
 ###############################################################################
-# 2) Create the RunnableWithMessageHistory (conversational chain)
+# 2) Create the RunnableWithMessageHistory (session-based chain)
 ###############################################################################
-# If your snippet references `rag_chain`, combine it with get_session_history.
+# This wraps your `rag_chain` so it automatically reads/writes
+# conversation history from get_session_history for each session.
 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"
+    rag_chain,               # the main chain from pipeline.py
+    get_session_history,     # fetches or creates ChatMessageHistory by session_id
+    input_messages_key="input",       # key in the dict for user's new query
+    history_messages_key="chat_history",  # key for existing chat logs
+    output_messages_key="answer"      # key for final output
 )
 
 ###############################################################################
@@ -50,9 +44,11 @@ conversational_rag_chain = RunnableWithMessageHistory(
 ###############################################################################
 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'.
+    A helper that calls our `conversational_rag_chain` 
+    with a given session_id. Returns the final 'answer'.
     """
+    # We invoke the chain with the user query; 
+    # the chain automatically updates the session’s chat history.
     response = conversational_rag_chain.invoke(
         {"input": user_query},
         config={