pinecone-client

import os from pinecone import Pinecone, ServerlessSpec, CloudProvider, AwsRegion # Initialize the Pinecone client (using the new 'pinecone' package) api_key = os.environ.get('PINECONE_API_KEY', 'YOUR_API_KEY') # Ensure you have a Pinecone API Key set as an environment variable or replace 'YOUR_API_KEY' pc = Pinecone(api_key=api_key) index_name = 'my-first-index' dimension = 1536 # Example dimension, choose based on your embedding model # Create a serverless index (if it doesn't exist) if index_name not in pc.list_indexes().names(): pc.create_index( name=index_name, dimension=dimension, metric='cosine', # or 'euclidean', 'dotproduct' spec=ServerlessSpec( cloud=CloudProvider.AWS, region=AwsRegion.US_EAST_1 ) ) print(f"Index '{index_name}' created or already exists.") # Connect to the index index = pc.Index(name=index_name) # Example: Upserting data (replace with your actual vectors/text) vectors_to_upsert = [ {"id": "vec1", "values": [0.1]*dimension, "metadata": {"genre": "fiction"}}, {"id": "vec2", "values": [0.2]*dimension, "metadata": {"genre": "non-fiction"}} ] index.upsert(vectors=vectors_to_upsert) print(f"Upserted {len(vectors_to_upsert)} vectors to '{index_name}'.") # Example: Querying the index query_vector = [0.15]*dimension query_results = index.query(vector=query_vector, top_k=1, include_metadata=True) print("Query results:", query_results) # Example: Deleting the index (uncomment to run) # pc.delete_index(index_name) # print(f"Index '{index_name}' deleted.")