PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"
curl "https://$INDEX_HOST/query" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-Pinecone-Api-Version: 2025-10" \
-d '{
"namespace": "example-namespace",
"vector": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
"filter": {"genre": {"$eq": "documentary"}},
"topK": 3,
"includeValues": 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}
}
Search with a vector
Search a namespace using a query vector. It retrieves the ids of the most similar items in a namespace, along with their similarity scores.
For guidance, examples, and limits, see Search.
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"
curl "https://$INDEX_HOST/query" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-Pinecone-Api-Version: 2025-10" \
-d '{
"namespace": "example-namespace",
"vector": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
"filter": {"genre": {"$eq": "documentary"}},
"topK": 3,
"includeValues": 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}
}
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"
curl "https://$INDEX_HOST/query" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-Pinecone-Api-Version: 2025-10" \
-d '{
"namespace": "example-namespace",
"vector": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
"filter": {"genre": {"$eq": "documentary"}},
"topK": 3,
"includeValues": 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
Headers
Required date-based version header
Body
The request for the query operation.
The number of results to return for each query.
1 <= x <= 1000010
The namespace to query.
"example-namespace"
The filter to apply. You can use vector metadata to limit your search. See Understanding metadata.
{
"genre": { "$in": ["comedy", "documentary", "drama"] },
"year": { "$eq": 2019 }
}
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.
true
Indicates whether metadata is included in the response as well as the ids.
true
DEPRECATED. Use vector or id instead.
Show child attributes
Show child attributes
The query vector. This should be the same length as the dimension of the index being queried. Each query request can contain only one of the parameters id or vector.
[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length.
Show child attributes
Show child attributes
The unique ID of the vector to be used as a query vector. Each request can contain either the vector or id parameter.
512"example-vector-1"
An optimization parameter for IVF dense indexes in dedicated read node indexes. It adjusts how much of the index is scanned to find vector candidates. Range: 0.5 – 4 (default). Keep the default (4.0) for the best search results. If query latency is too high, try lowering this value incrementally (minimum 0.5) to speed up the search at the cost of slightly lower accuracy. This parameter is only supported for dedicated (DRN) dense indexes.
0.5 <= x <= 42
An optimization parameter that controls the maximum number of candidate dense vectors to rerank. Reranking computes exact distances to improve recall but increases query latency. Range: top_k – 100000. Keep the default for a balance of recall and latency. Increase this value if recall is too low, or decrease it to reduce latency at the cost of accuracy. This parameter is only supported for dedicated (DRN) dense indexes.
1 <= x <= 1000001000
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.
DEPRECATED. The results of each query. The order is the same as QueryRequest.queries.
Show child attributes
Show child attributes
The matches for the vectors.
Show child attributes
Show child attributes
The namespace for the vectors.
Show child attributes
Show child attributes
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