After your data is indexed, you can start sending queries to Pinecone.

The Query operation searches the index using a query vector. It retrieves the IDs of the most similar records in the index, along with their similarity scores. This operation can optionally return the result’s vector values and metadata, too. You specify the number of vectors to retrieve each time you send a query. Matches are always ordered by similarity from most similar to least similar.

The similarity score for a vector represents its distance to the query vector, calculated according to the distance metric for the index. The significance of the score depends on the similarity metric. For example, for indexes using the euclidean distance metric, scores with lower values are more similar, while for indexes using the dotproduct metric, higher scores are more similar.

Sending a query

When you send a query, you provide vector values representing your query embedding. The top_k parameter indicates the number of results to return. For example, this example sends a query vector and retrieves three matching vectors:

import pinecone

pinecone.init(api_key="YOUR_API_KEY", environment="YOUR_ENVIRONMENT")
index = pinecone.Index('example-index')

  vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
# Returns:
# {
#     "matches": [
#         {
#             "id": "C",
#             "score": -1.76717265e-07,
#             "values": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
#         },
#         {
#             "id": "B",
#             "score": 0.080000028,
#             "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2],
#         },
#         {
#             "id": "D",
#             "score": 0.0800001323,
#             "values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4],
#         },
#     ],
#     "namespace": "",
# }

Depending on your data and your query, you may get fewer than top_k results. This happens when top_k is larger than the number of possible matching vectors for your query.

Querying by namespace

You can organize the records added to an index into partitions, or “namespaces,” to limit queries and other vector operations to only one such namespace at a time. For more information, see: Namespaces.

Using metadata filters in queries

You can add metadata to document embeddings within Pinecone, and then filter for those criteria when sending the query. Pinecone will search for similar vector embeddings only among those items that match the filter. For more information, see: Metadata Filtering.

    vector=[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1],
        "genre": {"$eq": "documentary"},
        "year": 2019

Querying vectors with sparse and dense values

When querying an index containing sparse and dense vectors, include a sparse_vector in your query parameters.


The following example shows how to query with a sparse-dense vector.

query_response = index.query(
    vector=[0.1, 0.2, 0.3, 0.4],
        'indices': [3],
        'values':  [0.8]

To learn more, see Querying sparse-dense vectors.


Avoid returning vector data and metadata when top_k is greater than 1000. This means queries with top_k over 1000 should not contain include_metadata=True or include_data=True. For more limitations, see: Limits.

Pinecone is eventually consistent, so queries may not reflect very recent upserts.