Update app.py

app.py

@@ -157,14 +157,14 @@ async def run_hackrx(req: RunRequest):
         embedding=ml_models["embedder"]
     )
 
-    dense_retriever = vectorstore.as_retriever(search_kwargs={"k": 5})
+    dense_retriever = vectorstore.as_retriever(search_kwargs={"k": 4})
     
 
     # Create retrievers using the pre-loaded models from our ml_models dictionary
     keyword_retriever = BM25Retriever.from_documents(chunks)
-    keyword_retriever.k = 4
+    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=[0.2, 0.8])
+    ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.4, 0.6])
     
     compression_retriever = ContextualCompressionRetriever(
         base_retriever=ensemble_retriever, base_compressor=ml_models["reranker_compressor"]