Function query_pinecone_index

examples/openai/multi_tool_orchestration.py:111–122

(client, index, model, query_text)

#
# Create a natural language query, compute its embedding, and perform a similarity search on the Pinecone index. The returned results include metadata that provides context for generating answers.
def query_pinecone_index(client, index, model, query_text):
    # Generate an embedding for the query.
    query_embedding = client.embeddings.create(input=query_text, model=model).data[0].embedding

    # Query the index and return top 5 matches.
    res = index.query(vector=[query_embedding], top_k=5, include_metadata=True)
    print("Query Results:")
    for match in res["matches"]:
        print(
            f"{match['score']:.2f}: {match['metadata'].get('Question', 'N/A')} - {match['metadata'].get('Answer', 'N/A')}"
        )
    return res


# Example usage with a different query from the train/test set