Update app.py

app.py

@@ -340,7 +340,7 @@ vectorstore,search_type="similarity",k=10,score_threshold=None
 base_retriever_HF = Vectorstore_backed_retriever(vector_store_HF,"similarity",k=10)
 
 
-
+'''
 def create_compression_retriever(embeddings, base_retriever, chunk_size=500, k=16, similarity_threshold=None):
     """Build a ContextualCompressionRetriever.
     We wrap the the base_retriever (a vectorstore-backed retriever) into a ContextualCompressionRetriever.
@@ -382,7 +382,7 @@ def create_compression_retriever(embeddings, base_retriever, chunk_size=500, k=1
     )
     print("compression retriever done")
     return compression_retriever
-
+'''
 
 
 '''
@@ -718,8 +718,8 @@ chain = ConversationalRetrievalChain.from_llm(
         LLM_provider="Google",api_key=google_api_key,temperature=0.1,
         model_name="gemini-pro"),
     memory=create_memory("gemini-pro"),
-    retriever = compression_retriever,
-    #retriever = base_retriever_HF,  #base_retriever_HF
+
+    retriever = base_retriever_HF,  #base_retriever_HF
     llm=instantiate_LLM(
         LLM_provider="Google",api_key=google_api_key,temperature=0.7,
         model_name="gemini-pro"),
@@ -808,8 +808,8 @@ Standalone question: {question}""")
             model_name="gemini-pro"),
 
         memory=memory,
-        retriever = compression_retriever,
-        #retriever = base_retriever_HF,  #changed this
+        #retriever = compression_retriever,
+        retriever = base_retriever_HF,  #changed this
         #retriever = retriever,
         #llm=llm,    #changed this
         llm=instantiate_LLM(