Update app.py

app.py

@@ -67,7 +67,7 @@ def initialize_pinecone(index_name: str):
 
 # Initialize Pinecone index and BM25 encoder
 pinecone_index = initialize_pinecone("updated-uae-gov")
-bm25 = BM25Encoder().load("./updated-uae-gov.json")
+bm25 = BM25Encoder().load("./new-updated-uae-gov.json")
 
 ##################################################
 ##################################################
@@ -86,12 +86,12 @@ retriever = PineconeHybridSearchRetriever(
 llm = ChatPerplexity(temperature=0, pplx_api_key=GROQ_API_KEY, model="llama-3.1-sonar-large-128k-chat", max_tokens=512, max_retries=2)
 
 # Initialize Reranker
-# model = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-base")
-# compressor = CrossEncoderReranker(model=model, top_n=10)
+model = HuggingFaceCrossEncoder(model_name="BAAI/bge-reranker-base")
+compressor = CrossEncoderReranker(model=model, top_n=10)
 
-# 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 \
@@ -106,11 +106,11 @@ contextualize_q_prompt = ChatPromptTemplate.from_messages(
         ("human", "{input}")
     ]
 )
-history_aware_retriever = create_history_aware_retriever(llm, retriever, contextualize_q_prompt)
+history_aware_retriever = create_history_aware_retriever(llm, compression_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. 
-If you don't know the answer, simply state that you don't know.
+If you don't know the answer, state that you don't know.
 
 YOUR ANSWER SHOULD BE IN '{language}' LANGUAGE.