init

clqna.py

@@ -0,0 +1,35 @@
+import streamlit as st
+
+from transformers import pipeline
+from pinecone import Pinecone, ServerlessSpec
+from sentence_transformers import SentenceTransformer, util
+
+
+bi_encoder = SentenceTransformer('msmarco-distilbert-base-v4')
+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'
+
+index = pc.Index(name=INDEX_NAME)
+
+def query_from_pinecone(index, 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,
+      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)
+docresult= resp[0]['metadata']['text'] 
+#+ '\n*************\n'+  resp[1]['metadata']['text'] + '\n*************\n'+ resp[2]['metadata']['text']
+
+st.json(out)
+