Added pc

app.py

@@ -11,23 +11,24 @@ bi_encoder.max_seq_length = 256     # Truncate long documents to 256 tokens
 # Store the index as a variable
 INDEX_NAME = 'cl-search-idx'
 NAMESPACE = 'webpages'
-
+pc_api_key= '3f916d01-2a69-457d-85eb-966c5d1849a8'  #AWS
+pc = Pinecone(api_key=pc_api_key)
 index = pc.Index(name=INDEX_NAME)
 
-def query_from_pinecone(index, question_embedding, top_k=3):
+def query_from_pinecone(index,namespace, question_embedding, top_k=3):
     # get embedding from THE SAME embedder as the documents
 
     return index.query(
       vector=question_embedding,
       top_k=top_k,
-      namespace=NAMESPACE,
+      namespace=namespace,
       include_metadata=True   # gets the metadata (dates, text, etc)
     ).get('matches')
 
 
 QUESTION=st.text_area('Ask a question -e.g How to prepare for Verbal section for CAT?')  ##' How to prepare for Verbal section ?'
 question_embedding = bi_encoder.encode(QUESTION, convert_to_tensor=True)
-resp= query_from_pinecone(question_embedding.tolist(), 3)
+resp= query_from_pinecone(index,NAMESPACE, question_embedding.tolist(), 3)
 docresult= resp[0]['metadata']['text'] 
 #+ '\n*************\n'+  resp[1]['metadata']['text'] + '\n*************\n'+ resp[2]['metadata']['text']