Update routes.py

routes.py

@@ -1,5 +1,7 @@
 # routes.py
 
+import logging
+
 from fastapi import APIRouter, Body, HTTPException
 from models import (
     PageSpeedURLRequest,
@@ -15,6 +17,9 @@ from gemini_report import generate_report_with_gemini
 from chatbot import create_new_chat, get_chain_for_user, summarize_messages
 from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
 
+# Configure logger to integrate with Uvicorn’s logging
+logger = logging.getLogger("uvicorn.error")
+
 router = APIRouter()
 
 
@@ -25,10 +30,13 @@ async def ingest_user_report(request: ReportIngestRequest):
     """
     Ingests a user’s PageSpeed report text into the FAISS vectorstore.
     """
+    logger.info(f"[ingest-report] Received ingest request for user_id={request.user_id}")
     try:
         vectorstore, retriever = ingest_report(request.user_id, request.report_text)
+        logger.info(f"[ingest-report] Successfully ingested report for user_id={request.user_id}")
         return {"status": "success", "message": f"Report ingested for user {request.user_id}"}
     except Exception as e:
+        logger.error(f"[ingest-report] Error ingesting report for user_id={request.user_id}: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
 
@@ -37,8 +45,14 @@ async def generate_report(data: dict = Body(...)):
     """
     Generates a report using Gemini given arbitrary data in the request body.
     """
-    report = generate_report_with_gemini(data, GEMINI_API_KEY)
-    return {"report": report}
+    logger.info(f"[generate-report] Received data for Gemini report generation: keys={list(data.keys())}")
+    try:
+        report = generate_report_with_gemini(data, GEMINI_API_KEY)
+        logger.info(f"[generate-report] Successfully generated report")
+        return {"report": report}
+    except Exception as e:
+        logger.error(f"[generate-report] Error generating report: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
 
 
 @router.post("/fetch-pagespeed-data")
@@ -46,10 +60,13 @@ async def fetch_pagespeed_data(request: PageSpeedURLRequest):
     """
     Fetches PageSpeed Insights data for the provided target URL.
     """
+    logger.info(f"[fetch-pagespeed-data] Fetching PageSpeed for URL={request.target_url}")
     try:
         data = get_pagespeed_data(request.target_url, PAGESPEED_API_KEY)
+        logger.info(f"[fetch-pagespeed-data] Successfully fetched PageSpeed data for URL={request.target_url}")
         return {"status": "success", "data": data}
     except Exception as e:
+        logger.error(f"[fetch-pagespeed-data] Error fetching PageSpeed data for URL={request.target_url}: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
 
@@ -58,6 +75,7 @@ async def home():
     """
     Simple health check endpoint.
     """
+    logger.info("[home] Health check endpoint called")
     return {"API is UP and running"}
 
 
@@ -69,10 +87,13 @@ async def new_chat(request: NewChatRequest):
     Creates a new chat session for the given user_id and returns a chat_id.
     Expected JSON body: {"user_id": "<string>"}
     """
+    logger.info(f"[new-chat] Creating new chat session for user_id={request.user_id}")
     try:
         chat_id = create_new_chat(request.user_id)
+        logger.info(f"[new-chat] Created chat_id={chat_id} for user_id={request.user_id}")
         return {"status": "success", "chat_id": chat_id}
     except Exception as e:
+        logger.error(f"[new-chat] Error creating chat session for user_id={request.user_id}: {e}")
         raise HTTPException(status_code=500, detail=str(e))
 
 
@@ -87,16 +108,19 @@ async def chat_query(request: ChatQueryRequest):
         "query": "<string>"
       }
     """
+    logger.info(f"[chat] Received query for user_id={request.user_id}, chat_id={request.chat_id}")
     try:
         # Reconstruct the ConversationalRetrievalChain for this user & chat_id
         chain = get_chain_for_user(request.user_id, request.chat_id)
         chat_history: MongoDBChatMessageHistory = chain.memory  # type: ignore
 
         # Optionally summarize if the history is too long
-        summarize_messages(chat_history)
+        if summarize_messages(chat_history):
+            logger.info(f"[chat] Summarized chat history for chat_id={request.chat_id}")
 
         # Add the user’s message to history
         chat_history.add_user_message(request.query)
+        logger.info(f"[chat] Added user message to history: \"{request.query}\"")
 
         # Run the chain synchronously (non-streaming)
         result = chain({
@@ -105,15 +129,22 @@ async def chat_query(request: ChatQueryRequest):
         })
 
         answer = result.get("answer", "").strip()
+        logger.info(f"[chat] LLM returned answer=\"{answer}\" for user_id={request.user_id}")
 
         # Persist the AI’s message
         if answer:
             chat_history.add_ai_message(answer)
+            logger.info(f"[chat] Persisted AI message to history for chat_id={request.chat_id}")
 
-        return {"answer": answer }
+        return {
+            "status": "success",
+            "answer": answer,
+            "source_documents": result.get("source_documents", []),
+        }
 
     except ValueError as ve:
-        # Raised when no vectorstore is registered for this user_id
+        logger.warning(f"[chat] No vectorstore for user_id={request.user_id}: {ve}")
         raise HTTPException(status_code=404, detail=str(ve))
     except Exception as e:
+        logger.error(f"[chat] Error processing chat request for user_id={request.user_id}, chat_id={request.chat_id}: {e}")
         raise HTTPException(status_code=500, detail=str(e))