Vector Database, Large Language Models, Llm, Semantic Search, Vector Search.
= [text for text in questions[ctr:ctr_end]] embeddings = model.encode(questions[ctr:ctr_end]).tolist() metadatas = question_ids[ctr:ctr_end] coll.upsert(documents=documents, ids=IDs, embeddings=embeddings, metadatas=metadatas) Now our collection has not only the questions but the question_id as a