Search with a vector
The query operation searches 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.
# pip install "pinecone[grpc]"
from pinecone.grpc import PineconeGRPC as Pinecone
pc = Pinecone(api_key="YOUR_API_KEY")
# To get the unique host for an index,
# see https://docs.pinecone.io/guides/manage-data/target-an-index
index = pc.Index(host="INDEX_HOST")
index.query(
namespace="example-namespace",
vector=[0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
filter={
"genre": {"$eq": "documentary"}
},
top_k=3,
include_values=True
)// npm install @pinecone-database/pinecone
import { Pinecone } from '@pinecone-database/pinecone'
const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' })
// To get the unique host for an index,
// see https://docs.pinecone.io/guides/manage-data/target-an-index
const index = pc.index("INDEX_NAME", "INDEX_HOST")
const queryResponse = await index.namespace('example-namespace').query({
vector: [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
filter: {
'genre': {'$eq': 'documentary'}
},
topK: 3,
includeValues: true
});import com.google.protobuf.Struct;
import com.google.protobuf.Value;
import io.pinecone.clients.Index;
import io.pinecone.configs.PineconeConfig;
import io.pinecone.configs.PineconeConnection;
import io.pinecone.unsigned_indices_model.QueryResponseWithUnsignedIndices;
import java.util.Arrays;
import java.util.List;
public class QueryExample {
public static void main(String[] args) {
PineconeConfig config = new PineconeConfig("YOUR_API_KEY");
// To get the unique host for an index,
// see https://docs.pinecone.io/guides/manage-data/target-an-index
config.setHost("INDEX_HOST");
PineconeConnection connection = new PineconeConnection(config);
Index index = new Index(connection, "INDEX_NAME");
List<Float> query = Arrays.asList(0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f);
Struct filter = Struct.newBuilder()
.putFields("genre", Value.newBuilder()
.setStructValue(Struct.newBuilder()
.putFields("$eq", Value.newBuilder()
.setStringValue("documentary")
.build()))
.build())
.build();
QueryResponseWithUnsignedIndices queryResponse = index.query(3, query, null, null, null, "example-namespace", filter, false, true);
System.out.println(queryResponse);
}
}package main
import (
"context"
"encoding/json"
"fmt"
"log"
"github.com/pinecone-io/go-pinecone/v2/pinecone"
"google.golang.org/protobuf/types/known/structpb"
)
func prettifyStruct(obj interface{}) string {
bytes, _ := json.MarshalIndent(obj, "", " ")
return string(bytes)
}
func main() {
ctx := context.Background()
pc, err := pinecone.NewClient(pinecone.NewClientParams{
ApiKey: "YOUR_API_KEY",
})
if err != nil {
log.Fatalf("Failed to create Client: %v", err)
}
// To get the unique host for an index,
// see https://docs.pinecone.io/guides/manage-data/target-an-index
idxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "INDEX_HOST", Namespace: "example-namespace"})
if err != nil {
log.Fatalf("Failed to create IndexConnection for Host: %v", err)
}
queryVector := []float32{0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3}
metadataMap := map[string]interface{}{
"genre": map[string]interface{}{
"$eq": "documentary",
},
}
metadataFilter, err := structpb.NewStruct(metadataMap)
if err != nil {
log.Fatalf("Failed to create metadata map: %v", err)
}
res, err := idxConnection.QueryByVectorValues(ctx, &pinecone.QueryByVectorValuesRequest{
Vector: queryVector,
TopK: 3,
MetadataFilter: metadataFilter,
IncludeValues: true,
})
if err != nil {
log.Fatalf("Error encountered when querying by vector: %v", err)
} else {
fmt.Printf(prettifyStruct(res))
}
}using Pinecone;
var pinecone = new PineconeClient("YOUR_API_KEY");
// To get the unique host for an index,
// see https://docs.pinecone.io/guides/manage-data/target-an-index
var index = pinecone.Index(host: "INDEX_HOST");
var queryResponse = await index.QueryAsync(new QueryRequest {
Vector = new[] { 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f, 0.3f },
Namespace = "example-namespace",
TopK = 3,
Filter = new Metadata
{
["genre"] =
new Metadata
{
["$eq"] = "documentary",
}
}
});
Console.WriteLine(queryResponse);PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"
curl "https://$INDEX_HOST/query" \
-H "Api-Key: $PINECONE_API_KEY" \
-H "Content-Type: application/json" \
-H "X-Pinecone-Api-Version: 2024-10" \
-d '{
"namespace": "example-namespace",
"vector": [0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3, 0.3],
"filter": {"genre": {"$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}
}POST /query
Request body
Section titled “Request body”namespace(body, string) — The namespace to query.topK(body, integer, required) — The number of results to return for each query.filter(body, object) — The filter to apply. You can use vector metadata to limit your search. See Understanding metadata.includeValues(body, boolean) — Indicates whether vector values are included in the response. For on-demand indexes, setting this totruemay increase latency, especially with highertopKvalues, because vector values are retrieved from object storage. Unless you need vector values, set this tofalsefor better performance.includeMetadata(body, boolean) — Indicates whether metadata is included in the response as well as the ids.queries(body, object[]) — DEPRECATED. Usevectororidinstead.vector(body, number[]) — The query vector. This should be the same length as the dimension of the index being queried. Each request can contain either theidorvectorparameter.sparseVector(body, object) — Vector sparse data. Represented as a list of indices and a list of corresponded values, which must be with the same length.id(body, string) — The unique ID of the vector to be used as a query vector. Each request can contain either thevectororidparameter.
Responses
Section titled “Responses”200— A successful response.400— Bad request. The request body included invalid request parameters.4XX— An unexpected error response.5XX— An unexpected error response.