Update app.py

app.py

@@ -90,17 +90,17 @@ llm = ChatPerplexity(temperature=0, pplx_api_key=GROQ_API_KEY, model="llama-3.1-
 
 # compressor = FlashrankRerank(model="ms-marco-MultiBERT-L-12", score_threshold=0.6)
 
-from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank
+# from langchain_community.document_compressors.rankllm_rerank import RankLLMRerank
 
-compressor = RankLLMRerank(top_n=6, model="zephyr")
+# compressor = RankLLMRerank(top_n=6, model="zephyr")
 
-compression_retriever = ContextualCompressionRetriever(
-    base_compressor=compressor, base_retriever=retriever
-)
+# compression_retriever = ContextualCompressionRetriever(
+#     base_compressor=compressor, base_retriever=retriever
+# )
 
-compression_retriever = ContextualCompressionRetriever(
-    base_compressor=compressor, base_retriever=retriever
-)
+# 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 \
@@ -115,7 +115,7 @@ 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.