Consulta un índice público para obtener los vecinos más cercanos

def vector_search_find_neighbors(
    project: str,
    location: str,
    index_endpoint_name: str,
    deployed_index_id: str,
    queries: List[List[float]],
    num_neighbors: int,
) -> None:
    """Query the vector search index.

    Args:
        project (str): Required. Project ID
        location (str): Required. The region name
        index_endpoint_name (str): Required. Index endpoint to run the query
        against.
        deployed_index_id (str): Required. The ID of the DeployedIndex to run
        the queries against.
        queries (List[List[float]]): Required. A list of queries. Each query is
        a list of floats, representing a single embedding.
        num_neighbors (int): Required. The number of neighbors to return.
    """
    # Initialize the Vertex AI client
    aiplatform.init(project=project, location=location)

    # Create the index endpoint instance from an existing endpoint.
    my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint(
        index_endpoint_name=index_endpoint_name
    )

    # Query the index endpoint for the nearest neighbors.
    resp = my_index_endpoint.find_neighbors(
        deployed_index_id=deployed_index_id,
        queries=queries,
        num_neighbors=num_neighbors,
    )
    print(resp)

    # Query hybrid datapoints, sparse-only datapoints, and dense-only datapoints.
    hybrid_queries = [
        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
            dense_embedding=[1, 2, 3],
            sparse_embedding_dimensions=[10, 20, 30],
            sparse_embedding_values=[1.0, 1.0, 1.0],
            rrf_ranking_alpha=0.5,
        ),
        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
            dense_embedding=[1, 2, 3],
            sparse_embedding_dimensions=[10, 20, 30],
            sparse_embedding_values=[0.1, 0.2, 0.3],
        ),
        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
            sparse_embedding_dimensions=[10, 20, 30],
            sparse_embedding_values=[0.1, 0.2, 0.3],
        ),
        aiplatform.matching_engine.matching_engine_index_endpoint.HybridQuery(
            dense_embedding=[1, 2, 3]
        ),
    ]

    hybrid_resp = my_index_endpoint.find_neighbors(
            deployed_index_id=deployed_index_id,
            queries=hybrid_queries,
            num_neighbors=num_neighbors,)
    print(hybrid_resp)