Update app.py

app.py

@@ -143,22 +143,15 @@ def parse_llm_response(content: str) -> str:
 # --- 5. Main API Endpoint ---
 @app.post("/api/v1/hackrx/run", response_model=RunResponse, dependencies=[Depends(verify_api_key)])
 async def run_hackrx(req: RunRequest):
-    # chunks = load_and_chunk(str(req.documents))
-    # if not chunks:
-    #     return JSONResponse({"error": "No documents could be processed."}, status_code=400)
+    chunks = load_and_chunk(str(req.documents))
+    if not chunks:
+        return JSONResponse({"error": "No documents could be processed."}, status_code=400)
 
         
     ####code for parallel####################################################################################
-    all_chunks = []
-    for doc_url in req.documents:
-        chunks = load_and_chunk(doc_url)
-        all_chunks.extend(chunks)
-
-    if not all_chunks:
-        return JSONResponse({"error": "No documents could be processed."}, status_code=400)
 
     vectorstore = await FAISS.afrom_documents(
-        documents=all_chunks, 
+        documents=chunks, 
         embedding=ml_models["embedder"]
     )
 
@@ -166,7 +159,7 @@ async def run_hackrx(req: RunRequest):
     
 
     # Create retrievers using the pre-loaded models from our ml_models dictionary
-    keyword_retriever = BM25Retriever.from_documents(all_chunks)
+    keyword_retriever = BM25Retriever.from_documents(chunks)
     keyword_retriever.k = 3
     # dense_retriever = Chroma.from_documents(documents=chunks, embedding=ml_models["embedder"]).as_retriever()
     ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.4, 0.65])