google-cloud-vectorsearch

import os from google.cloud import vectorsearch # Set your GCP Project ID and Location project_id = os.environ.get("GCP_PROJECT_ID", "your-gcp-project-id") location = os.environ.get("GCP_REGION", "us-central1") # e.g., 'us-central1' # Set your Vector Search Index Endpoint ID and Deployed Index ID # These must point to an existing, deployed index endpoint in your project. index_endpoint_id = os.environ.get("VECTORSEARCH_INDEX_ENDPOINT_ID", "your-index-endpoint-id") deployed_index_id = os.environ.get("VECTORSEARCH_DEPLOYED_INDEX_ID", "your-deployed-index-id") # Initialize the client try: client = vectorsearch.VectorSearchServiceClient() index_endpoint_name = client.index_endpoint_path( project=project_id, location=location, index_endpoint=index_endpoint_id ) # Define a query vector (replace with your actual embedding) # The dimension must match the index's dimension. Example for 1536 dimensions: query_vector = [0.1] * 1536 # Placeholder: adjust dimensions as needed num_neighbors = 5 # Perform a vector search (match) print(f"Searching for {num_neighbors} neighbors in index endpoint: {index_endpoint_name}...") response = client.match( index_endpoint=index_endpoint_name, deployed_index_id=deployed_index_id, queries=[ vectorsearch.MatchQuery( vector=query_vector, # Optional: Add filters if your index supports them # restrict_filters=[vectorsearch.RestrictFilter(namespace="color", allow_tokens=["red"])], # numeric_filters=[vectorsearch.NumericFilter(value_int=vectorsearch.IntFilter(value=10), field_name="price")], ) ], # Optional: Set to True to retrieve full datapoint metadata # return_full_datapoint=True ) if response.results: print(f"Found {len(response.results[0].matches)} matches for the first query:") for match in response.results[0].matches: print(f" ID: {match.id}, Distance: {match.distance:.4f}") if match.datapoint: print(f" Datapoint ID: {match.datapoint.datapoint_id}") # Access other datapoint fields like match.datapoint.metadata, match.datapoint.restricts, etc. else: print("No results found. Check your query, index, and endpoint configuration.") except Exception as e: print(f"An error occurred: {e}") print("Please ensure you have authenticated to GCP (e.g., `gcloud auth application-default login`),") print("your project, location, index endpoint, and deployed index IDs are correct, and the index is deployed.")