Update app.py

app.py

@@ -78,7 +78,7 @@ retriever = PineconeHybridSearchRetriever(
     embeddings=embed_model, 
     sparse_encoder=bm25, 
     index=pinecone_index, 
-    top_k=20, 
+    top_k=10, 
     alpha=0.5,
 )
 
@@ -99,10 +99,10 @@ llm = ChatPerplexity(temperature=0, pplx_api_key=GROQ_API_KEY, model="llama-3.1-
 #     base_compressor=compressor, base_retriever=retriever
 # )
 
-compressor = FlashrankRerank(top_n=10)
-compression_retriever = ContextualCompressionRetriever(
-    base_compressor=compressor, base_retriever=retriever
-)
+# compressor = FlashrankRerank(top_n=10)
+# compression_retriever = ContextualCompressionRetriever(
+#     base_compressor=compressor, base_retriever=retriever
+# )
 
 # Contextualization prompt and retriever
 contextualize_q_system_prompt = """Given a chat history and the latest user question \
@@ -117,7 +117,7 @@ contextualize_q_prompt = ChatPromptTemplate.from_messages(
         ("human", "{input}")
     ]
 )
-history_aware_retriever = create_history_aware_retriever(llm, compression_retriever, contextualize_q_prompt)
+history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
 
 # QA system prompt and chain
 qa_system_prompt = """ You are a highly skilled information retrieval assistant. Use the following context to answer questions effectively.