Create a legacy integrated index

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

A legacy integrated index is created with create_index_for_model and served by the legacy Records API. Pinecone converts the source text you upsert and search with to vectors, using one hosted embedding model per index. To have Pinecone embed text in a document index instead, declare embed or sparse_embed on a string field. See Integrated embedding.

Legacy integrated indexes (created with create_index_for_model and served by the legacy Records API) don’t support updating or importing with text.

Dense embedding

If you want to upsert and search with source text and have Pinecone convert it to dense vectors automatically, create an index with integrated embedding as follows:

Provide a name for the index.
Set cloud and region to the cloud and region where the index should be deployed.
Set embed.model to one of Pinecone’s hosted embedding models.
Set embed.field_map to the name of the field in your source document that contains the data for embedding.

Other parameters are optional. See Create an index with integrated embedding for details.

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_name = "integrated-dense-py"

if not pc.has_index(index_name):
    pc.create_index_for_model(
        name=index_name,
        cloud="aws",
        region="us-east-1",
        embed={
            "model":"llama-text-embed-v2",
            "field_map":{"text": "chunk_text"}
        }
    )

import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

await pc.createIndexForModel({
  name: 'integrated-dense-js',
  cloud: 'aws',
  region: 'us-east-1',
  embed: {
    model: 'llama-text-embed-v2',
    fieldMap: { text: 'chunk_text' },
  },
  waitUntilReady: true,
});

import io.pinecone.clients.Pinecone;
import org.openapitools.db_control.client.ApiException;
import org.openapitools.db_control.client.model.CreateIndexForModelRequestEmbed;
import java.util.HashMap;
import java.util.Map;

public class CreateIntegratedIndex {
    public static void main(String[] args) throws ApiException {
        Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
        String indexName = "integrated-dense-java";
        String region = "us-east-1";
        HashMap<String, String> fieldMap = new HashMap<>();
        fieldMap.put("text", "chunk_text");
        CreateIndexForModelRequestEmbed embed = new CreateIndexForModelRequestEmbed()
                .model("llama-text-embed-v2")
                .fieldMap(fieldMap);
        Map<String, String> tags = new HashMap<>();
        tags.put("environment", "development");
        pc.createIndexForModel(
                indexName,
                "aws",
                region,
                embed,
                "disabled",
                tags
        );
    }
}

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v7/pinecone"
)

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)
    }

  	indexName := "integrated-dense-go"
   	deletionProtection := pinecone.DeletionProtectionDisabled

    idx, err := pc.CreateIndexForModel(ctx, &pinecone.CreateIndexForModelRequest{
		Name:   indexName,
		Cloud:  pinecone.CloudAWS,
		Region: "us-east-1",
		Embed: pinecone.CreateIndexForModelEmbed{
			Model:    "llama-text-embed-v2",
			FieldMap: map[string]interface{}{"text": "chunk_text"},
		},
        DeletionProtection: &deletionProtection,
        Tags:   &pinecone.IndexTags{ "environment": "development" },
	})
    if err != nil {
        log.Fatalf("Failed to create serverless integrated index: %v", err)
    } else {
        fmt.Printf("Successfully created serverless integrated index: %v", idx.Name)
    }
}

PINECONE_API_KEY="YOUR_API_KEY"

curl -X POST "https://api.pinecone.io/indexes/create-for-model" \
     -H "Content-Type: application/json" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2026-07" \
     -d '{
           "name": "integrated-dense-curl",
           "cloud": "aws",
           "region": "us-east-1",
           "embed": {
             "model": "llama-text-embed-v2",
             "field_map": {
               "text": "chunk_text"
             }
           }
         }'

# Target the project where you want to create the index.
pc target -o "example-org" -p "example-project"
# Create the index.
pc index create \
  --name "integrated-dense-cli" \
  --metric "cosine" \
  --cloud "aws" \
  --region "us-east-1" \
  --model "llama-text-embed-v2" \
  --field-map "text=chunk_text" \
  --tags "environment=development"

Sparse embedding

If you want to upsert and search with source text and have Pinecone convert it to sparse vectors automatically, create an index with integrated embedding as follows:

Provide a name for the index.
Set cloud and region to the cloud and region where the index should be deployed.
Set embed.model to one of Pinecone’s hosted sparse embedding models.
Set embed.field_map to the name of the field in your source document that contains the text for embedding.
If needed, embed.read_parameters and embed.write_parameters can be used to override the default model embedding behavior.

Other parameters are optional. See Create an index with integrated embedding for details.

from pinecone import Pinecone

pc = Pinecone(api_key="YOUR_API_KEY")

index_name = "integrated-sparse-py"

if not pc.has_index(index_name):
    pc.create_index_for_model(
        name=index_name,
        cloud="aws",
        region="us-east-1",
        embed={
            "model":"pinecone-sparse-english-v0",
            "field_map":{"text": "chunk_text"}
        }
    )

import { Pinecone } from '@pinecone-database/pinecone'

const pc = new Pinecone({ apiKey: 'YOUR_API_KEY' });

await pc.createIndexForModel({
  name: 'integrated-sparse-js',
  cloud: 'aws',
  region: 'us-east-1',
  embed: {
    model: 'pinecone-sparse-english-v0',
    fieldMap: { text: 'chunk_text' },
  },
  waitUntilReady: true,
});

import io.pinecone.clients.Pinecone;
import org.openapitools.db_control.client.ApiException;
import org.openapitools.db_control.client.model.CreateIndexForModelRequestEmbed;
import java.util.HashMap;
import java.util.Map;

public class CreateIntegratedIndex {
    public static void main(String[] args) throws ApiException {
        Pinecone pc = new Pinecone.Builder("YOUR_API_KEY").build();
        String indexName = "integrated-sparse-java";
        String region = "us-east-1";
        HashMap<String, String> fieldMap = new HashMap<>();
        fieldMap.put("text", "chunk_text");
        CreateIndexForModelRequestEmbed embed = new CreateIndexForModelRequestEmbed()
                .model("pinecone-sparse-english-v0")
                .fieldMap(fieldMap);
        Map<String, String> tags = new HashMap<>();
        tags.put("environment", "development");
        pc.createIndexForModel(
                indexName,
                "aws",
                region,
                embed,
                "disabled",
                tags
        );
    }
}

package main

import (
    "context"
    "fmt"
    "log"

    "github.com/pinecone-io/go-pinecone/v7/pinecone"
)

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)
    }

  	indexName := "integrated-sparse-go"
	deletionProtection := pinecone.DeletionProtectionDisabled

    idx, err := pc.CreateIndexForModel(ctx, &pinecone.CreateIndexForModelRequest{
		Name:   indexName,
		Cloud:  pinecone.CloudAWS,
		Region: "us-east-1",
		Embed: pinecone.CreateIndexForModelEmbed{
			Model:    "pinecone-sparse-english-v0",
			FieldMap: map[string]interface{}{"text": "chunk_text"},
		},
        DeletionProtection: &deletionProtection,
        Tags:   &pinecone.IndexTags{ "environment": "development" },

	})
    if err != nil {
        log.Fatalf("Failed to create serverless integrated index: %v", err)
    } else {
        fmt.Printf("Successfully created serverless integrated index: %v", idx.Name)
    }
}

PINECONE_API_KEY="YOUR_API_KEY"

curl -X POST "https://api.pinecone.io/indexes/create-for-model" \
     -H "Content-Type: application/json" \
     -H "Api-Key: $PINECONE_API_KEY" \
     -H "X-Pinecone-Api-Version: 2026-07" \
     -d '{
           "name": "integrated-sparse-curl",
           "cloud": "aws",
           "region": "us-east-1",
           "embed": {
             "model": "pinecone-sparse-english-v0",
             "field_map": {
               "text": "chunk_text"
             }
           }
         }'

# Target the project where you want to create the index.
pc target -o "example-org" -p "example-project"
# Create the index.
pc index create \
  --name "integrated-sparse-cli" \
  --cloud "aws" \
  --region "us-east-1" \
  --model "pinecone-sparse-english-v0" \
  --field-map "text=chunk_text" \
  --tags "environment=development"

Wait for the index to be ready

A new index can’t accept data until its status.ready field is true. See Wait for the index to be ready.

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Legacy

Create a legacy integrated index

Dense embedding

Sparse embedding

Wait for the index to be ready

​Dense embedding

​Sparse embedding

​Wait for the index to be ready

Dense embedding

Sparse embedding

Wait for the index to be ready