Update app.py

app.py

@@ -162,18 +162,18 @@ async def run_hackrx(req: RunRequest):
     )
 
     # dense_retriever = vectorstore.as_retriever(search_type="mmr",search_kwargs={"k": 8})
-    dense_retriever = vectorstore.as_retriever(search_type="mmr",search_kwargs={"k": 12 ,"lambda_mult": 0.5})
+    dense_retriever = vectorstore.as_retriever(search_type="mmr",search_kwargs={"k": 11 ,"lambda_mult": 0.5} )
     
 
     # Create retrievers using the pre-loaded models from our ml_models dictionary
     keyword_retriever = BM25Retriever.from_documents(chunks)
-    keyword_retriever.k = 8
+    keyword_retriever.k = 7
     # dense_retriever = Chroma.from_documents(documents=chunks, embedding=ml_models["embedder"]).as_retriever()
-    ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.35, 0.65])
+    ensemble_retriever = EnsembleRetriever(retrievers=[keyword_retriever, dense_retriever], weights=[0.35, 0.65],search_kwargs={"k": 10})
     ### to make it faster we are now using our built reranker thats why commenting the code below
-    compression_retriever = ContextualCompressionRetriever(
-        base_retriever=ensemble_retriever, base_compressor=ml_models["reranker_compressor"]
-    )
+    # compression_retriever = ContextualCompressionRetriever(
+    #     base_retriever=ensemble_retriever, base_compressor=ml_models["reranker_compressor"]
+    # )
 
     
     # Define the RAG chain using pre-loaded components