wooo another commit

main.py

@@ -116,7 +116,7 @@ def retrieve_docs(session_id: str):
     
 
 
-def run_query(message: str, intent: str, request: Request) -> str:
+def run_query(message: str, intent: str, session_id: str) -> str:
     """
     Routes the user message to the appropriate Haystack pipeline
     based on the intent selected in the UI.
@@ -128,7 +128,7 @@ def run_query(message: str, intent: str, request: Request) -> str:
     Returns:
         LLM reply as a string
     """
-    session_id = request.state.session_id
+    #session_id = request.state.session_id
     match intent:
         case "i3rab":
             # Strip any leading instruction words so the parser gets
@@ -170,7 +170,12 @@ def run_query(message: str, intent: str, request: Request) -> str:
         
         
         case "knowledge":
-            msg_history = retrieve_docs(session_id)
+
+            chat_docs = list(collection.find({
+                "session_id": session_id
+            }))
+            msg_history = json.dumps(docs, indent=2, default=str)
+            
             result = knowledge_pipeline.run({
                 "text_embedder": {"text": message},
                 "prompt_builder": {
@@ -219,7 +224,7 @@ async def chat_stream(req: ChatRequest, request: Request):
         # stays non-blocking for other concurrent requests
         loop = asyncio.get_event_loop()
         full_reply = await loop.run_in_executor(
-            None, run_query, req.message, req.intent, request
+            None, run_query, req.message, req.intent, request.state.session_id
         )
         # via mongodb api, insert the msgs and replies  into the docs
         # then in haystack_pipeline.py retrieve the docs and inject them into the prompt