This page shows you how to update the data in an index [namespace](/guides/index-data-indexing-overview#namespaces). The kind of index determines which operation you use. An index with a [document schema](/guides/index-data-search-full-text-search#schema-definition) uses the Documents API, and a vector index uses the [Vectors API](/guides/database-2026-07-data-plane-update). If you aren't sure which kind of index you have, see [Adopt the Documents API](/guides/index-data-adopt-the-documents-api).

- **[Update documents](#update-documents)**: Patch fields on a document by ID, or patch metadata fields across every document matching a filter.
- **[Update by ID](#update-by-id)**: Update a single record's metadata (add or change fields) or vector values.
- **[Update by metadata](#update-by-metadata)**: Update metadata (add or change fields) across multiple records using a metadata filter. Vector values can't be updated.

To replace whole documents or records, use the [upsert](/guides/index-data-upsert-data) operation instead.

:::callout{intent="tip"}
Updates consume [write units (WUs)](/guides/manage-cost-understanding-cost#write-units). See [Understanding cost](/guides/manage-cost-understanding-cost#update) for how update cost is calculated.
:::

## Update documents

In an index with a document schema, [upsert](/guides/index-data-upsert-data#upsert-documents) replaces the whole document for a given `_id`. To change individual fields instead, use the documents update operation, which leaves the fields you don't mention unchanged.

To patch specific documents, send one entry per `_id`. Include the fields you want to set, and name any fields to drop in `_remove_fields`. Both schema-declared fields and metadata fields can be set or removed this way:

:::code-group
```Python Python theme={null}
from pinecone import 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.documents.update(
    namespace="articles",
    documents=[
        {"_id": "doc1", "body": "Updated body text."},  # schema-declared field
        {"_id": "doc2", "_remove_fields": ["author"]},  # metadata field
    ],
)
```

```bash curl theme={null}
PINECONE_API_KEY="YOUR_API_KEY"
INDEX_HOST="INDEX_HOST"

curl "https://$INDEX_HOST/namespaces/articles/documents/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "documents": [
                { "_id": "doc1", "body": "Updated body text." },
                { "_id": "doc2", "_remove_fields": ["author"] }
            ]
        }'
```
:::

To apply the same patch to every document matching a [metadata filter expression](/guides/index-data-indexing-overview#metadata-filter-expressions), send `filter` with `set_fields`, `remove_fields`, or both:

:::code-group
```Python Python theme={null}
# Connect to an index as shown above.
index.documents.update(
    namespace="articles",
    filter={"category": {"$eq": "news"}},
    set_fields={"status": "archived"},
    remove_fields=["author"],
)
```

```bash curl theme={null}
curl "https://$INDEX_HOST/namespaces/articles/documents/update" \
    -H "Api-Key: $PINECONE_API_KEY" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2026-07" \
    -d '{
            "filter": { "category": { "$eq": "news" } },
            "set_fields": { "status": "archived" },
            "remove_fields": ["author"]
        }'
```
:::

The response reports how many documents matched the filter:

```json theme={null}
{
    "matched_records": 2
}
```

For the full request and response schema, see [Update documents](/guides/database-2026-07-data-plane-update-documents).

### Limitations

- **By-ID requests**: Each request can patch up to 1,000 documents. See [Update limits](/guides/apis-database-limits-operation-limits#update-limits).
- **By-filter requests**: `set_fields` and `remove_fields` both accept metadata fields only, and reject a field declared in the schema with a `400`. A by-filter update applies one value to every matched document, which would leave vector and full-text-searchable fields identical across the whole match set. To change a schema-declared field, patch each document by ID.

:::callout{intent="warning"}
This operation ignores the `dry_run` parameter that [Update by metadata](#update-by-metadata) supports. A `dry_run` request returns `202` with a `matched_records` count, exactly like the preview you expect, and the update is applied anyway. It also accepts unrecognized parameters rather than rejecting them, so check the parameter names before you send a request you can't undo.
:::

## Update by ID

To update the vector and/or metadata of a single record, use the [`update`](/guides/database-2026-07-data-plane-update) operation with the following parameters:

- `namespace`: The [namespace](/guides/index-data-indexing-overview#namespaces) 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.

:::callout{intent="warning"}
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"}
)
```

:::code-group
```Python Python theme={null}
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.update(
	namespace="example-namespace",
	id="id-3", 
	values=[5.0, 3.0], 
	set_metadata={"genre": "comedy"}
)
```

```JavaScript JavaScript theme={null}
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")

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

```java Java theme={null}
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/manage-data/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);
        Index index = new Index(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);
    }
}
```

```go Go theme={null}
package main

import (
    "context"
    "log"

    "github.com/pinecone-io/go-pinecone/v4/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/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)
  	}

    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)
    }
}
```

```bash curl theme={null}
# To get the unique host for an index,
# see https://docs.pinecone.io/guides/manage-data/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](/guides/index-data-indexing-overview#namespaces) containing the records to update. To use the default namespace, set this to `"__default__"`.
- `filter`: A [metadata filter expression](/guides/index-data-indexing-overview#metadata-filter-expressions) 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.

  :::callout{intent="note"}
  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:

```json theme={null}
{
    "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:

:::code-group
```Python Python theme={null}
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")

# 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"}
)
```

```JavaScript JavaScript theme={null}
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")

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

```java Java theme={null}
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/manage-data/target-an-index
        config.setHost("INDEX_HOST");
        PineconeConnection connection = new PineconeConnection(config);
        Index index = new Index(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);
    }
}
```

```go Go theme={null}
package main

import (
    "context"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v4/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/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)
    }

    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)
}
```

```bash curl theme={null}
# To get the unique host for an index,
# see https://docs.pinecone.io/guides/manage-data/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

:::callout{intent="tip"}
If you need to update most of the records in a large namespace, [contact Support](https://app.pinecone.io/organizations/-/settings/support/ticket) 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:

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

   ```bash curl {11} theme={null}
   # To get the unique host for an index,
   # see https://docs.pinecone.io/guides/manage-data/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:

   ```json theme={null}
   {
       "matchedRecords": 150000
   }
   ```

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

2. Initiate the first update by sending the request without the `dry_run` parameter:

   ```bash curl theme={null}
   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:

   ```json theme={null}
   {
       "matchedRecords": 150000
   }
   ```

3. 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:

   ```bash curl {11} theme={null}
   # To get the unique host for an index,
   # see https://docs.pinecone.io/guides/manage-data/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"
               } 
           }'
   ```

   ```json theme={null}
   {
       "matchedRecords": 50000
   }
   ```

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

   ```bash curl theme={null}
   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"
               } 
           }'
   ```

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

   ```bash curl {11} theme={null}
   # To get the unique host for an index,
   # see https://docs.pinecone.io/guides/manage-data/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"
               } 
           }'
   ```

   ```json theme={null}
   {
       "matchedRecords": 0
   }
   ```

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

   ```json {10,22} theme={null}
   {
       "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](/guides/apis-database-limits-operation-limits#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](#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](/guides/index-data-upsert-data) 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.

In an index with a document schema, remove a field with `_remove_fields` instead of replacing the document. See [Update documents](#update-documents).

## 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](/guides/index-data-check-data-freshness#check-the-log-sequence-number) to check whether an update request has completed.

## See also

- [Update an entire document](/guides/index-data-data-modeling#update-an-entire-document)

## Related pages

- [Account management](./account-management-index.md)
- [Admin](./admin-2-index.md)
- [Admin](./admin-index.md)
- [APIs](./apis-index.md)
- [Architecture](./architecture-index.md)
- [Assistants](./assistants-index.md)
- [Bring Your Own Cloud](./bring-your-own-cloud-index.md)
- [Build an assistant](./build-an-assistant-index.md)
- [Build an integration](./build-an-integration-index.md)
- [Changelog](./changelog-index.md)

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