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POST
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query
# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("docs-example")

index.query(
    namespace="example-namespace",
    vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    filter={
        "genre": {"$eq": "documentary"}
    },
    top_k=3,
    include_values=True
)

{
  "matches":[
    {
      "id": "vec3",
      "score": 0,
      "values": [0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3]
    },
    {
      "id": "vec2",
      "score": 0.0800000429,
      "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]
    },
    {
      "id": "vec4",
      "score": 0.0799999237,
      "values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
    }
  ],
  "namespace": "example-namespace",
  "usage": {"read_units": 6}
}

# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")
index = pc.Index("docs-example")

index.query(
    namespace="example-namespace",
    vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
    filter={
        "genre": {"$eq": "documentary"}
    },
    top_k=3,
    include_values=True
)

{
  "matches":[
    {
      "id": "vec3",
      "score": 0,
      "values": [0.3,0.3,0.3,0.3,0.3,0.3,0.3,0.3]
    },
    {
      "id": "vec2",
      "score": 0.0800000429,
      "values": [0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2]
    },
    {
      "id": "vec4",
      "score": 0.0799999237,
      "values": [0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4]
    }
  ],
  "namespace": "example-namespace",
  "usage": {"read_units": 6}
}

Authorizations

​
Api-Key
string
header
required

An API Key is required to call Pinecone APIs. Get yours from the console.

Body

application/json

The request for the query operation.

​
topK
integer<int64>
required

The number of results to return for each query.

Required range: 1 <= x <= 10000
Example:

10

​
namespace
string

The namespace to query.

Example:

"example-namespace"

​
filter
object

The filter to apply. You can use vector metadata to limit your search. See Understanding metadata.

Example:
{
  "genre": { "$in": ["comedy", "documentary", "drama"] },
  "year": { "$eq": 2019 }
}
​
includeValues
boolean
default:false

Indicates whether vector values are included in the response. For on-demand indexes, setting this to true may increase latency, especially with higher topK values, because vector values are retrieved from object storage. Unless you need vector values, set this to false for better performance.

Example:

true

​
includeMetadata
boolean
default:false

Indicates whether metadata is included in the response as well as the ids.

Example:

true

​
queries
object[]
deprecated

DEPRECATED. Use vector or id instead.

​
vector
number<float>[]

The query vector. This should be the same length as the dimension of the index being queried. Each request can contain either the id or vector parameter.

Example:
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
​
sparseVector
object

Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length.

​
id
string

The unique ID of the vector to be used as a query vector. Each request can contain either the vector or id parameter.

Maximum string length: 512
Example:

"example-vector-1"

Response

A successful response.

The response for the query operation. These are the matches found for a particular query vector. The matches are ordered from most similar to least similar.

​
results
The query results for a single `QueryVector` · object[]
deprecated

DEPRECATED. The results of each query. The order is the same as QueryRequest.queries.

​
matches
object[]

The matches for the vectors.

​
namespace
string

The namespace for the vectors.

​
usage
object