Update vectors in a legacy vector index

New projects should use the Documents API. To move an existing index, see Move to the Documents API.

This page shows you how to update records in a namespace of a legacy vector index with the legacy Vectors API. To replace whole records, upsert them instead. For a document index, see Update data.

Update by ID

To update the vector and/or metadata of a single record, use the Update a record operation with the following parameters:

namespace: The namespace containing the record to update. To use the default namespace, set the namespace to "__default__".
id: The ID of the record to update.
One or both of the following:
- Updated values for the vector. Specify one of the following:
  - values: For dense vectors. Must have the same length as the existing vector.
  - sparse_values: For sparse vectors.
- setMetadata: The metadata to add or change. When updating metadata, only the specified metadata fields are modified, and if a specified metadata field doesn’t exist, it’s added.

If a non-existent record ID is specified, no records are affected and a 200 OK status is returned.

In this example, assume you are updating the dense vector values and one metadata value of the following record in the example-namespace namespace:

(
    namespace="example-namespace",
    id="id-3", 
    values=[4.0, 2.0], 
    setMetadata={"type": "doc", "genre": "drama"}
)

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/indexes/target-an-index
index = pc.index(host="INDEX_HOST", grpc=True)

index.update(
	namespace="example-namespace",
	id="id-3", 
	values=[5.0, 3.0], 
	set_metadata={"genre": "comedy"}
)

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/indexes/target-an-index
const index = pc.index("INDEX_NAME", "INDEX_HOST")

await index.namespace('example-namespace').update({
  id: 'id-3',
  values: [5.0, 3.0],
  metadata: {
    genre: "comedy",
  },
});

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.proto.UpdateResponse;

import java.util.Arrays;
import java.util.List;

public class UpdateExample {
    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/indexes/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);
        Index index = new Index(config, connection, "INDEX_NAME");
        List<Float> values = Arrays.asList(5.0f, 3.0f);
		Struct metaData = Struct.newBuilder()
			.putFields("genre",
					Value.newBuilder().setStringValue("comedy").build())
			.build();
        UpdateResponse updateResponse = index.update("id-3", values, metaData, "example-namespace", null, null);
        System.out.println(updateResponse);
    }
}

package main

import (
    "context"
    "log"

    "github.com/pinecone-io/go-pinecone/v7/pinecone"
    "google.golang.org/protobuf/types/known/structpb"
)

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/indexes/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)
  	}

    id := "id-3"

    metadataMap := map[string]interface{}{
        "genre": "comedy",
    }

    metadataFilter, err := structpb.NewStruct(metadataMap)
    if err != nil {
        log.Fatalf("Failed to create metadata map: %v", err)
    }

    err = idxConnection.UpdateVector(ctx, &pinecone.UpdateVectorRequest{
        Id:       id,
        Values: []float32{5.0, 3.0},
        Metadata: metadataFilter,
    })
    if err != nil {
        log.Fatalf("Failed to update vector with ID %v: %v", id, err)
    }
}

# To get the unique host for an index,
# see https://docs.pinecone.io/guides/indexes/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

# Update both values and metadata
curl "https://$INDEX_HOST/vectors/update" \
  -H "Api-Key: $PINECONE_API_KEY" \
  -H 'Content-Type: application/json' \
  -H "X-Pinecone-Api-Version: 2026-07" \
  -d '{
        "id": "id-3",
        "values": [5.0, 3.0],
        "setMetadata": {"genre": "comedy"},
        "namespace": "example-namespace"
      }'

After the update, the dense vector values and the genre metadata value are changed, but the type metadata value is unchanged:

(
    id="id-3", 
    values=[5.0, 3.0], 
    metadata={"type": "doc", "genre": "comedy"}
)

Update by metadata

To add or change metadata across multiple records in a namespace, use the update operation with the following parameters:

namespace: The namespace containing the records to update. To use the default namespace, set this to "__default__".
filter: A metadata filter expression to match the records to update.
setMetadata: The metadata to add or change. When updating metadata, only the specified metadata fields are modified. If a specified metadata field doesn’t exist, it is added.
dry_run: Optional. If true, the number of records that match the filter expression is returned, but the records aren’t updated.
Each request updates a maximum of 100,000 records. Use "dry_run": true to check if you need to run the request multiple times. See the example below for details.

For example, let’s say you have records that represent chunks of a single document with metadata that keeps track of chunk and document details, and you want to store the author’s name with each chunk of the document:

{
    "id": "document1#chunk1", 
    "values": [0.0236663818359375, -0.032989501953125, ..., -0.01041412353515625, 0.0086669921875], 
    "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases",
        "chunk_number": 1,
        "chunk_text": "First chunk of the document content...",
        "document_url": "https://example.com/docs/document1"
    }
},
{
    "id": "document1#chunk2", 
    "values": [-0.0412445068359375, 0.028839111328125, ..., 0.01953125, -0.0174560546875],
    "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases", 
        "chunk_number": 2,
        "chunk_text": "Second chunk of the document content...",
        "document_url": "https://example.com/docs/document1"
    }
},
...

The following code updates all matching records with the new author metadata field:

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/indexes/target-an-index
index = pc.index(host="INDEX_HOST", grpc=True)

# Use dry_run to check how many records match the filter
dry_run_response = index.update(
    namespace="example-namespace",
    filter={"document_title": {"$eq": "Introduction to Vector Databases"}},
    set_metadata={"author": "Del Klein"},
    dry_run=True
)
print(dry_run_response.matched_records)

# Perform the update
response = index.update(
    namespace="example-namespace",
    filter={"document_title": {"$eq": "Introduction to Vector Databases"}},
    set_metadata={"author": "Del Klein"}
)

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/indexes/target-an-index
const index = pc.index("INDEX_NAME", "INDEX_HOST")

await index.namespace('example-namespace').update({
  filter: { document_title: { $eq: 'Introduction to Vector Databases' } },
  metadata: { author: 'Del Klein' },
});

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.proto.UpdateResponse;

public class UpdateByMetadataExample {
    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/indexes/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);
        Index index = new Index(config, connection, "INDEX_NAME");

        Struct filter = Struct.newBuilder()
            .putFields("document_title", Value.newBuilder()
                .setStructValue(Struct.newBuilder()
                    .putFields("$eq", Value.newBuilder()
                        .setStringValue("Introduction to Vector Databases")
                        .build())
                    .build())
                .build())
            .build();

        Struct metadata = Struct.newBuilder()
            .putFields("author", Value.newBuilder()
                .setStringValue("Del Klein")
                .build())
            .build();

        // Dry run to check how many records match
        UpdateResponse dryRunResponse = index.updateByMetadata(
            filter, metadata, "example-namespace", true);
        System.out.println("Matched records: " + dryRunResponse.getMatchedRecords());

        // Perform the update
        UpdateResponse response = index.updateByMetadata(
            filter, metadata, "example-namespace");
        System.out.println(response);
    }
}

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v7/pinecone"
    "google.golang.org/protobuf/types/known/structpb"
)

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/indexes/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)
    }

    filter, err := structpb.NewStruct(map[string]interface{}{
        "document_title": map[string]interface{}{
            "$eq": "Introduction to Vector Databases",
        },
    })
    if err != nil {
        log.Fatalf("Failed to create filter: %v", err)
    }

    metadata, err := structpb.NewStruct(map[string]interface{}{
        "author": "Del Klein",
    })
    if err != nil {
        log.Fatalf("Failed to create metadata: %v", err)
    }

    // Dry run to check how many records match
    dryRun := true
    dryRunRes, err := idxConnection.UpdateVectorsByMetadata(ctx, &pinecone.UpdateVectorsByMetadataRequest{
        Filter:   filter,
        Metadata: metadata,
        DryRun:   &dryRun,
    })
    if err != nil {
        log.Fatalf("Failed to dry run update: %v", err)
    }
    fmt.Printf("Matched records: %d\n", dryRunRes.MatchedRecords)

    // Perform the update
    res, err := idxConnection.UpdateVectorsByMetadata(ctx, &pinecone.UpdateVectorsByMetadataRequest{
        Filter:   filter,
        Metadata: metadata,
    })
    if err != nil {
        log.Fatalf("Failed to update vectors by metadata: %v", err)
    }
    fmt.Printf("Updated records: %d\n", res.MatchedRecords)
}

# To get the unique host for an index,
# see https://docs.pinecone.io/guides/indexes/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/vectors/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H 'Content-Type: application/json' \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "namespace": "example-namespace",
            "filter": {
                "document_title": {"$eq": "Introduction to Vector Databases"}
            },
            "setMetadata": {
                "author": "Del Klein"
            } 
        }'

Handling large updates

If you need to update most of the records in a large namespace, contact Support for help creating an export to enable a faster and more cost-effective approach.

Each request updates a maximum of 100,000 records. For larger datasets, use dry_run to check the count and repeat the request as needed:

To check how many records match the filter expression, send a request with dry_run set to true:

curl

# To get the unique host for an index,
# see https://docs.pinecone.io/guides/indexes/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/vectors/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H 'Content-Type: application/json' \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "dry_run": true,
            "namespace": "example-namespace",
            "filter": {
                "document_title": {"$eq": "Introduction to Vector Databases"}
            },
            "setMetadata": {
                "author": "Del Klein"
            } 
        }'

The response contains the number of records that match the filter expression:

{
    "matchedRecords": 150000
}

Since this number exceeds the 100,000 record limit, you’ll need to run the update request multiple times.

Initiate the first update by sending the request without the dry_run parameter:

curl

curl "https://$INDEX_HOST/vectors/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H 'Content-Type: application/json' \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "namespace": "example-namespace",
            "filter": {
                "document_title": {"$eq": "Introduction to Vector Databases"}
            },
            "setMetadata": {
                "author": "Del Klein"
            } 
        }'

Again, the response contains the total number of records that match the filter expression, but only 100,000 will be updated:

{
    "matchedRecords": 150000
}

Pinecone is eventually consistent, so there can be a slight delay before your update request is processed. Repeat the dry_run request until the number of matching records shows that the first 100,000 records have been updated:

curl

# To get the unique host for an index,
# see https://docs.pinecone.io/guides/indexes/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/vectors/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H 'Content-Type: application/json' \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "dry_run": true,
            "namespace": "example-namespace",
            "filter": {
                "document_title": {"$eq": "Introduction to Vector Databases"}
            },
            "setMetadata": {
                "author": "Del Klein"
            } 
        }'

{
    "matchedRecords": 50000
}

Once the first 100,000 records have been updated, update the remaining records:

curl

curl "https://$INDEX_HOST/vectors/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H 'Content-Type: application/json' \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "namespace": "example-namespace",
            "filter": {
                "document_title": {"$eq": "Introduction to Vector Databases"}
            },
            "setMetadata": {
                "author": "Del Klein"
            } 
        }'

Repeat the dry_run request until the number of matching records shows that the remaining records have been updated:

curl

# To get the unique host for an index,
# see https://docs.pinecone.io/guides/indexes/target-an-index
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/vectors/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H 'Content-Type: application/json' \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "dry_run": true,
            "namespace": "example-namespace",
            "filter": {
                "document_title": {"$eq": "Introduction to Vector Databases"}
            },
            "setMetadata": {
                "author": "Del Klein"
            } 
        }'

{
    "matchedRecords": 0
}

Once the request has completed, all matching records include the author name as metadata:

{
    "id": "document1#chunk1", 
    "values": [0.0236663818359375, -0.032989501953125, ..., -0.01041412353515625, 0.0086669921875], 
    "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases",
        "chunk_number": 1,
        "chunk_text": "First chunk of the document content...",
        "document_url": "https://example.com/docs/document1",
        "author": "Del Klein"
    }
},
{
    "id": "document1#chunk2", 
    "values": [-0.0412445068359375, 0.028839111328125, ..., 0.01953125, -0.0174560546875],
    "metadata": {
        "document_id": "document1",
        "document_title": "Introduction to Vector Databases", 
        "chunk_number": 2,
        "chunk_text": "Second chunk of the document content...",
        "document_url": "https://example.com/docs/document1",
        "author": "Del Klein"
    }
},
...

Limitations

Each request updates a maximum of 100,000 records, per Update limits. Use "dry_run": true to check if you need to run the request multiple times. See the example above for details.
Large update-by-metadata requests can be slow to process. A request that matches many records takes longer to complete and to be reflected, so size the work with dry_run and batch it. See Handling large updates.
You can add or change metadata across multiple records, but you can’t remove metadata fields.

Remove a metadata field

To remove a metadata field from a record, use the upsert operation to replace the record’s metadata, providing the record’s existing ID and vector values along with only the metadata you want to keep. Because upsert replaces a record’s metadata in full, any fields you omit are cleared.

Data freshness

Pinecone is eventually consistent, so there can be a slight delay before updates are visible to queries. You can use log sequence numbers to check whether an update request has completed.

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Legacy

Update vectors in a legacy vector index

Update by ID

Update by metadata

Handling large updates

Limitations

Remove a metadata field

Data freshness

​Update by ID

​Update by metadata

​Handling large updates

​Limitations

​Remove a metadata field

​Data freshness

Update by ID

Update by metadata

Handling large updates

Limitations

Remove a metadata field

Data freshness