Pinecone Local is an in-memory Pinecone emulator available as a Docker image.

This page shows you how to use Pinecone Local to develop your applications locally without connecting to your Pinecone account or incurring usage or storage fees.

:::callout{intent="warning"}
Pinecone Local is not suitable for production. See [Limitations](#limitations) for details.
:::

## Limitations

Pinecone Local has the following limitations:

- Pinecone Local uses the `2025-01` API version, which isn't the latest stable version.
- Pinecone Local is available in Docker only.
- Pinecone Local is an in-memory emulator and isn't suitable for production. Records loaded into Pinecone Local don't persist after it's stopped.
- Pinecone Local doesn't authenticate client requests. API keys are ignored.
- Max number of records per index: 100,000.

Pinecone Local doesn't currently support the following features:

- [Import from object storage](/guides/index-data-import-data)
- [Backup/restore of serverless indexes](/guides/manage-data-backups-overview)
- [Collections for pod-based indexes](/guides/using-pods-understanding-collections)
- [Namespace management](/guides/manage-data-manage-namespaces)
- [Pinecone Inference](/guides/apis-introduction#inference)
- [Pinecone Assistant](/guides/get-started-assistant-overview)

## 1. Start Pinecone Local

You can configure Pinecone Local as an index emulator or database emulator:

- **Index emulator** - This approach uses the `pinecone-index` Docker image to create and configure indexes on startup. This is recommended when you want to quickly experiment with reading and writing data without needing to manage the index lifecycle.

  :::callout{intent="note"}
  With index emulation, you can only read and write data to the indexes created at startup. You can't create new indexes, list indexes, or run other operations that don't involve reading and writing data.
  :::

- **Database emulator** - This approach uses the `pinecone-local` Docker image to emulate Pinecone Database more broadly. This is recommended when you want to test your production app or manually create and manage indexes.

### Index emulator

:::::tabs
::::tab{title="Docker Compose"}
Make sure [Docker](https://docs.docker.com/get-docker/) is installed and running on your local machine.

Create a `docker-compose.yaml` file that defines a service for each index that Pinecone Local should create on startup. In this file, include the `pinecone-index` Docker image, a localhost port for the index to use, and other details:

```yaml theme={null}
services:
  dense-index:
    image: ghcr.io/pinecone-io/pinecone-index:latest
    container_name: dense-index
    environment:
      PORT: 5081
      INDEX_TYPE: serverless
      VECTOR_TYPE: dense
      DIMENSION: 2 
      METRIC: cosine
    ports:
      - "5081:5081"
    platform: linux/amd64
  sparse-index:
    image: ghcr.io/pinecone-io/pinecone-index:latest
    container_name: sparse-index
    environment:
      PORT: 5082
      INDEX_TYPE: serverless
      VECTOR_TYPE: sparse
      DIMENSION: 0
      METRIC: dotproduct
    ports:
      - "5082:5082"
    platform: linux/amd64
```

For each index, update the environment variables as needed:

- `PORT`: Specify the port number for the index to listen on.

- `INDEX_TYPE`: Specify the type of Pinecone index to create. Accepted values: `serverless` or `pod`.

- `VECTOR_TYPE`: Specify the [type of vectors](/guides/index-data-indexing-overview#indexes) you will store in the index. Accepted values: `dense` or `sparse`.

  :::callout{intent="note"}
  Sparse is supported only with serverless indexes.
  :::

- `DIMENSION`: Specify the dimension of vectors you will store in the index.

  :::callout{intent="note"}
  For indexes that store only sparse vectors, this must be set to `0`.
  :::

- `METRIC`: Specify the [distance metric](/guides/index-data-indexing-overview#distance-metrics) for calculating the similarity between vectors in the index. Accepted values when storing dense vectors: `cosine`, `euclidean`, or `dotproduct`. Accepted value when storing only sparse vectors: `dotproduct`.

To start Pinecone Local, run the following command:

```shell theme={null}
docker compose up -d
```

You'll see a message with details about each index.
::::

::::tab{title="Docker CLI"}
Make sure [Docker](https://docs.docker.com/get-docker/) is installed and running on your local machine.

Download the latest `pinecone-index` Docker image:

```shell theme={null}
docker pull ghcr.io/pinecone-io/pinecone-index:latest
```

Start Pinecone Local with one or more indexes:

```shell theme={null}
docker run -d \
--name dense-index \
-e PORT=5081 \
-e INDEX_TYPE=serverless \
-e VECTOR_TYPE=dense \
-e DIMENSION=2 \
-e METRIC=cosine \
-p 5081:5081 \
--platform linux/amd64 \
ghcr.io/pinecone-io/pinecone-index:latest
```

```shell theme={null}
docker run -d \
--name sparse-index \
-e PORT=5082 \
-e INDEX_TYPE=serverless \
-e VECTOR_TYPE=sparse \
-e DIMENSION=0 \
-e METRIC=dotproduct \
-p 5082:5082 \
--platform linux/amd64 \
ghcr.io/pinecone-io/pinecone-index:latest
```

For each index, update the environment variables as needed:

- `PORT`: Specify the port number for the index to listen on.

- `INDEX_TYPE`: Specify the type of Pinecone index to create. Accepted values: `serverless` or `pod`.

- `VECTOR_TYPE`: Specify the [type of vectors](/guides/index-data-indexing-overview#indexes) you will store in the index. Accepted values: `dense` or `sparse`.

  :::callout{intent="note"}
  Sparse is supported only with serverless indexes.
  :::

- `DIMENSION`: Specify the dimension of vectors you will store in the index.

  :::callout{intent="note"}
  For indexes that store only sparse vectors, this must be set to `0`.
  :::

- `METRIC`: Specify the [distance metric](/guides/index-data-indexing-overview#distance-metrics) for calculating the similarity between vectors in the index. Accepted values when storing dense vectors: `cosine`, `euclidean`, or `dotproduct`. Accepted value when storing only sparse vectors: `dotproduct`.
::::
:::::

### Database emulator

::::tabs
:::tab{title="Docker Compose"}
Make sure [Docker](https://docs.docker.com/get-docker/) is installed and running on your local machine.

Create a `docker-compose.yaml` file that defines a service for Pinecone local, including the `pinecone-local` Docker image, the host and port that Pinecone Local will run on and the range of ports that will be available for indexes:

```yaml theme={null}
services:
  pinecone:
    image: ghcr.io/pinecone-io/pinecone-local:latest
    environment: 
      PORT: 5080
      PINECONE_HOST: localhost
    ports: 
      - "5080-5090:5080-5090"
    platform: linux/amd64
```

To start Pinecone Local, run the following command:

```shell theme={null}
docker compose up -d
```

You'll see a message with details about the Pinecone Local instance.
:::

:::tab{title="Docker CLI"}
Make sure [Docker](https://docs.docker.com/get-docker/) is installed and running on your local machine.

Download the latest `pinecone-local` Docker image:

```shell theme={null}
docker pull ghcr.io/pinecone-io/pinecone-local:latest
```

Start Pinecone Local:

```shell theme={null}
docker run -d \
--name pinecone-local \
-e PORT=5080 \
-e PINECONE_HOST=localhost \
-p 5080-5090:5080-5090 \
--platform linux/amd64 \
ghcr.io/pinecone-io/pinecone-local:latest
```

This command defines the host and port that Pinecone Local will run on, as well as the range of ports that will be available for indexes.
:::
::::

## 2. Develop your app

Running code against Pinecone Local is just like running code against your Pinecone account, with the following differences:

- Pinecone Local doesn't authenticate client requests. API keys are ignored.

- The latest version of Pinecone Local uses [Pinecone API version](/guides/apis-versioning) `2025-01` and requires [Python SDK](/guides/sdks-python-overview) `v6.x` or later, [Node.js SDK](/guides/sdks-node-overview) `v5.x` or later, [Java SDK](/guides/sdks-java-overview) `v4.x` or later, and [Go SDK](/guides/sdks-go-overview) `v3.x` or later.

:::callout{intent="note"}
Be sure to review the [limitations](#limitations) of Pinecone Local before using it for development or testing.
:::

**Example**

The following example assumes that you have [started Pinecone Local without indexes](/guides/operations-local-development#database-emulator). It initializes a client, creates [an index for dense vectors](/guides/index-data-indexing-overview#semantic-search) and [an index for sparse vectors](/guides/index-data-indexing-overview#sparse-vector-search), upserts records into each, checks their record counts, and queries them.

:::code-group
```python Python theme={null}
from pinecone.grpc import PineconeGRPC, GRPCClientConfig
from pinecone import ServerlessSpec

# Initialize a client.
# API key is required, but the value does not matter.
# Host and port of the Pinecone Local instance
# is required when starting without indexes. 
pc = PineconeGRPC(
    api_key="pclocal", 
    host="http://localhost:5080" 
)                                    

# Create two indexes, one dense and one sparse
dense_index_name = "dense-index"
sparse_index_name = "sparse-index"

if not pc.has_index(dense_index_name):  
    dense_index_model = pc.create_index(
        name=dense_index_name,
        vector_type="dense",
        dimension=2,
        metric="cosine",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
        deletion_protection="disabled",
        tags={"environment": "development"}
    )

print("Index model (dense):\n", dense_index_model)

if not pc.has_index(sparse_index_name):  
    sparse_index_model = pc.create_index(
        name=sparse_index_name,
        vector_type="sparse",
        metric="dotproduct",
        spec=ServerlessSpec(cloud="aws", region="us-east-1"),
        deletion_protection="disabled",
        tags={"environment": "development"}
    )

print("\nIndex model (sparse):\n", sparse_index_model)

# Target each index, disabling tls
dense_index_host = pc.describe_index(name=dense_index_name).host
dense_index = pc.Index(host=dense_index_host, grpc_config=GRPCClientConfig(secure=False))
sparse_index_host = pc.describe_index(name=sparse_index_name).host
sparse_index = pc.Index(host=sparse_index_host, grpc_config=GRPCClientConfig(secure=False))

# Upsert records into the index (dense)
dense_index.upsert(
    vectors=[
        {
            "id": "vec1", 
            "values": [1.0, -2.5],
            "metadata": {"genre": "drama"}
        },
        {
            "id": "vec2", 
            "values": [3.0, -2.0],
            "metadata": {"genre": "documentary"}
        },
        {
            "id": "vec3", 
            "values": [0.5, -1.5],
            "metadata": {"genre": "documentary"}
        }
    ],
    namespace="example-namespace"
)

# Upsert records into the index (sparse)
sparse_index.upsert(
    namespace="example-namespace",
    vectors=[
        {
            "id": "vec1",
            "sparse_values": {
                "values": [1.7958984, 0.41577148, 2.828125, 2.8027344, 2.8691406, 1.6533203, 5.3671875, 1.3046875, 0.49780273, 0.5722656, 2.71875, 3.0820312, 2.5019531, 4.4414062, 3.3554688],
                "indices": [822745112, 1009084850, 1221765879, 1408993854, 1504846510, 1596856843, 1640781426, 1656251611, 1807131503, 2543655733, 2902766088, 2909307736, 3246437992, 3517203014, 3590924191]
            },
            "metadata": {
                "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.",
                "category": "technology",
                "quarter": "Q3"
            }
        },
        {
            "id": "vec2",
            "sparse_values": {
                "values": [0.4362793, 3.3457031, 2.7714844, 3.0273438, 3.3164062, 5.6015625, 2.4863281, 0.38134766, 1.25, 2.9609375, 0.34179688, 1.4306641, 0.34375, 3.3613281, 1.4404297, 2.2558594, 2.2597656, 4.8710938, 0.5605469],
                "indices": [131900689, 592326839, 710158994, 838729363, 1304885087, 1640781426, 1690623792, 1807131503, 2066971792, 2428553208, 2548600401, 2577534050, 3162218338, 3319279674, 3343062801, 3476647774, 3485013322, 3517203014, 4283091697]
            },
            "metadata": {
                "chunk_text": "Analysts suggest that AAPL'\''s upcoming Q4 product launch event might solidify its position in the premium smartphone market.",
                "category": "technology",
                "quarter": "Q4"
            }
        },
        {
            "id": "vec3",
            "sparse_values": {
                "values": [2.6875, 4.2929688, 3.609375, 3.0722656, 2.1152344, 5.78125, 3.7460938, 3.7363281, 1.2695312, 3.4824219, 0.7207031, 0.0826416, 4.671875, 3.7011719, 2.796875, 0.61621094],
                "indices": [8661920, 350356213, 391213188, 554637446, 1024951234, 1640781426, 1780689102, 1799010313, 2194093370, 2632344667, 2641553256, 2779594451, 3517203014, 3543799498, 3837503950, 4283091697]
            },
            "metadata": {
                "chunk_text": "AAPL'\''s strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production",
                "category": "technology",
                "quarter": "Q3"
            }
        }
    ]
)

# Check the number of records in each index
print("\nIndex stats (dense):\n", dense_index.describe_index_stats())
print("\nIndex stats (sparse):\n", sparse_index.describe_index_stats())

# Query the index (dense) with a metadata filter
dense_response = dense_index.query(
    namespace="example-namespace",
    vector=[3.0, -2.0],
    filter={"genre": {"$eq": "documentary"}},
    top_k=1,
    include_values=False,
    include_metadata=True
)

print("\nDense query response:\n", dense_response)

# Query the index (sparse) with a metadata filter
sparse_response = sparse_index.query(
    namespace="example-namespace",
    sparse_vector={
      "values": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
      "indices": [767227209, 1640781426, 1690623792, 2021799277, 2152645940, 2295025838, 2443437770, 2779594451, 2956155693, 3476647774, 3818127854, 4283091697]
    }, 
    filter={
        "quarter": {"$eq": "Q4"}
    },
    top_k=1,
    include_values=False,
    include_metadata=True
)

print("/nSparse query response:\n", sparse_response)

# Delete the indexes
pc.delete_index(name=dense_index_name)
pc.delete_index(name=sparse_index_name)
```

```javascript JavaScript theme={null}
import { Pinecone } from '@pinecone-database/pinecone';

// Initialize a client.
// API key is required, but the value does not matter.
// Host and port of the Pinecone Local instance
// is required when starting without indexes.
const pc = new Pinecone({ 
    apiKey: 'pclocal', 
    controllerHostUrl: 'http://localhost:5080' 
});

// Create two indexes, one dense and one sparse
const denseIndexName = 'dense-index';
const sparseIndexName = 'sparse-index';

const denseIndexModel = await pc.createIndex({
  name: denseIndexName,
  vectorType: 'dense',
  dimension: 2,
  metric: 'cosine',
  spec: {
    serverless: {
      cloud: 'aws',
      region: 'us-east-1'
    }
  },
  deletionProtection: 'disabled',
  tags: { environment: 'development' }, 
});

console.log('Index model (dense):', denseIndexModel);

const sparseIndexModel = await pc.createIndex({
    name: sparseIndexName,
    vectorType: 'sparse',
    metric: 'dotproduct',
    spec: {
      serverless: {
        cloud: 'aws',
        region: 'us-east-1'
      }
    },
    deletionProtection: 'disabled',
    tags: { environment: 'development' }, 
  });

console.log('\nIndex model (sparse):', sparseIndexModel);

// Target each index
const denseIndexHost = (await pc.describeIndex(denseIndexName)).host;
const denseIndex = await pc.index(denseIndexName, 'http://' + denseIndexHost);
const sparseIndexHost = (await pc.describeIndex(sparseIndexName)).host;
const sparseIndex = await pc.index(sparseIndexName, 'http://' + sparseIndexHost);

// Upsert records into the index (dense)
await denseIndex.namespace('example-namespace').upsert([
    {
        id: 'vec1', 
        values: [1.0, -2.5],
        metadata: { genre: 'drama' },
    },
    {
        id: 'vec2', 
        values: [3.0, -2.0],
        metadata: { genre: 'documentary' },
    },
    {
        id: 'vec3', 
        values: [0.5, -1.5],
        metadata: { genre: 'documentary' },
    }
]);

// Upsert records into the index (sparse)
await sparseIndex.namespace('example-namespace').upsert([
    {
        id: 'vec1',
        sparseValues: {
            indices: [822745112, 1009084850, 1221765879, 1408993854, 1504846510, 1596856843, 1640781426, 1656251611, 1807131503, 2543655733, 2902766088, 2909307736, 3246437992, 3517203014, 3590924191],
            values: [1.7958984, 0.41577148, 2.828125, 2.8027344, 2.8691406, 1.6533203, 5.3671875, 1.3046875, 0.49780273, 0.5722656, 2.71875, 3.0820312, 2.5019531, 4.4414062, 3.3554688]
        },
        metadata: { 
            chunk_text: 'AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.', 
            category: 'technology',
            quarter: 'Q3' 
        }
    },
    {
        id: 'vec2',
        sparseValues: {
            indices: [131900689, 592326839, 710158994, 838729363, 1304885087, 1640781426, 1690623792, 1807131503, 2066971792, 2428553208, 2548600401, 2577534050, 3162218338, 3319279674, 3343062801, 3476647774, 3485013322, 3517203014, 4283091697],
            values: [0.4362793, 3.3457031, 2.7714844, 3.0273438, 3.3164062, 5.6015625, 2.4863281, 0.38134766, 1.25, 2.9609375, 0.34179688, 1.4306641, 0.34375, 3.3613281, 1.4404297, 2.2558594, 2.2597656, 4.8710938, 0.5605469]
        },
        metadata: { 
            chunk_text: "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market.", 
            category: 'technology',
            quarter: 'Q4' 
        }
    },
    {
        id: 'vec3',
        sparseValues: {
            indices: [8661920, 350356213, 391213188, 554637446, 1024951234, 1640781426, 1780689102, 1799010313, 2194093370, 2632344667, 2641553256, 2779594451, 3517203014, 3543799498, 3837503950, 4283091697],
            values: [2.6875, 4.2929688, 3.609375, 3.0722656, 2.1152344, 5.78125, 3.7460938, 3.7363281, 1.2695312, 3.4824219, 0.7207031, 0.0826416, 4.671875, 3.7011719, 2.796875, 0.61621094]
        },
        metadata: { 
            chunk_text: "AAPL's strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production", 
            category: 'technology',
            quarter: 'Q3' 
        }
    }
]);

// Check the number of records in each index
console.log('\nIndex stats (dense):', await denseIndex.describeIndexStats());
console.log('\nIndex stats (sparse):', await sparseIndex.describeIndexStats());

// Query the index (dense) with a metadata filter
const denseQueryResponse = await denseIndex.namespace('example-namespace').query({
    vector: [3.0, -2.0],
    filter: {
        'genre': {'$eq': 'documentary'}
    },
    topK: 1,
    includeValues: false,
    includeMetadata: true,
});

console.log('\nDense query response:', denseQueryResponse);

const sparseQueryResponse = await sparseIndex.namespace('example-namespace').query({
    sparseVector: {
        indices: [767227209, 1640781426, 1690623792, 2021799277, 2152645940, 2295025838, 2443437770, 2779594451, 2956155693, 3476647774, 3818127854, 4283091697],
        values: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
    },
    topK: 1,
    includeValues: false,
    includeMetadata: true
});

console.log('\nSparse query response:', sparseQueryResponse);

// Delete the index
await pc.deleteIndex(denseIndexName);
await pc.deleteIndex(sparseIndexName);
```

```java Java theme={null}
import com.google.protobuf.Struct;
import com.google.protobuf.Value;
import io.pinecone.clients.Index;
import io.pinecone.clients.Pinecone;
import io.pinecone.proto.DescribeIndexStatsResponse;
import org.openapitools.db_control.client.model.DeletionProtection;
import io.pinecone.unsigned_indices_model.QueryResponseWithUnsignedIndices;

import java.util.*;

public class PineconeLocalExample {
    public static void main(String[] args) {
        // Initialize a client.
        // API key is required, but the value does not matter.
        // When starting without indexes, disable TLS and
        // provide the host and port of the Pinecone Local instance.
        String host = "http://localhost:5080";
        Pinecone pc = new Pinecone.Builder("pclocal")
                .withHost(host)
                .withTlsEnabled(false)
                .build();

        // Create two indexes, one dense and one sparse
        String denseIndexName = "dense-index";
        String sparseIndexName = "sparse-index";
        HashMap<String, String> tags = new HashMap<>();
        tags.put("environment", "development");
        pc.createServerlessIndex(
                denseIndexName,
                "cosine",
                2,
                "aws",
                "us-east-1",
                DeletionProtection.DISABLED,
                tags
        );
        pc.createSparseServelessIndex(
                sparseIndexName,
                "aws",
                "us-east-1",
                DeletionProtection.DISABLED,
                tags,
                "sparse"
        );

        // Get index connection objects
        Index denseIndexConnection = pc.getIndexConnection(denseIndexName);
        Index sparseIndexConnection = pc.getIndexConnection(sparseIndexName);

        // Upsert records into the index (dense)
        Struct metaData1 = Struct.newBuilder()
                .putFields("genre", Value.newBuilder().setStringValue("drama").build())
                .build();
        Struct metaData2 = Struct.newBuilder()
                .putFields("genre", Value.newBuilder().setStringValue("documentary").build())
                .build();
        Struct metaData3 = Struct.newBuilder()
                .putFields("genre", Value.newBuilder().setStringValue("documentary").build())
                .build();

        denseIndexConnection.upsert("vec1", Arrays.asList(1.0f, -2.5f),  null, null, metaData1, "example-namespace");
        denseIndexConnection.upsert("vec2", Arrays.asList(3.0f, -2.0f),  null, null, metaData2, "example-namespace");
        denseIndexConnection.upsert("vec3", Arrays.asList(0.5f, -1.5f),  null, null, metaData3, "example-namespace");

        // Upsert records into the index (sparse)
        ArrayList<Long> indices1 = new ArrayList<>(Arrays.asList(
                822745112L, 1009084850L, 1221765879L, 1408993854L, 1504846510L,
                1596856843L, 1640781426L, 1656251611L, 1807131503L, 2543655733L,
                2902766088L, 2909307736L, 3246437992L, 3517203014L, 3590924191L
        ));

        ArrayList<Float> values1 = new ArrayList<>(Arrays.asList(
                1.7958984f, 0.41577148f, 2.828125f, 2.8027344f, 2.8691406f,
                1.6533203f, 5.3671875f, 1.3046875f, 0.49780273f, 0.5722656f,
                2.71875f, 3.0820312f, 2.5019531f, 4.4414062f, 3.3554688f
        ));

        Struct sparseMetaData1 = Struct.newBuilder()
                .putFields("chunk_text", Value.newBuilder().setStringValue("AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.").build())
                .putFields("category", Value.newBuilder().setStringValue("technology").build())
                .putFields("quarter", Value.newBuilder().setStringValue("Q3").build())
                .build();

        ArrayList<Long> indices2 = new ArrayList<>(Arrays.asList(
                131900689L, 592326839L, 710158994L, 838729363L, 1304885087L,
                1640781426L, 1690623792L, 1807131503L, 2066971792L, 2428553208L,
                2548600401L, 2577534050L, 3162218338L, 3319279674L, 3343062801L,
                3476647774L, 3485013322L, 3517203014L, 4283091697L
        ));

        ArrayList<Float> values2 = new ArrayList<>(Arrays.asList(
                0.4362793f, 3.3457031f, 2.7714844f, 3.0273438f, 3.3164062f,
                5.6015625f, 2.4863281f, 0.38134766f, 1.25f, 2.9609375f,
                0.34179688f, 1.4306641f, 0.34375f, 3.3613281f, 1.4404297f,
                2.2558594f, 2.2597656f, 4.8710938f, 0.5605469f
        ));

        Struct sparseMetaData2 = Struct.newBuilder()
                .putFields("chunk_text", Value.newBuilder().setStringValue("Analysts suggest that AAPL'\\''s upcoming Q4 product launch event might solidify its position in the premium smartphone market.").build())
                .putFields("category", Value.newBuilder().setStringValue("technology").build())
                .putFields("quarter", Value.newBuilder().setStringValue("Q4").build())
                .build();

        ArrayList<Long> indices3 = new ArrayList<>(Arrays.asList(
                8661920L, 350356213L, 391213188L, 554637446L, 1024951234L,
                1640781426L, 1780689102L, 1799010313L, 2194093370L, 2632344667L,
                2641553256L, 2779594451L, 3517203014L, 3543799498L,
                3837503950L, 4283091697L
        ));

        ArrayList<Float> values3 = new ArrayList<>(Arrays.asList(
                2.6875f, 4.2929688f, 3.609375f, 3.0722656f, 2.1152344f,
                5.78125f, 3.7460938f, 3.7363281f, 1.2695312f, 3.4824219f,
                0.7207031f, 0.0826416f, 4.671875f, 3.7011719f, 2.796875f,
                0.61621094f
        ));

        Struct sparseMetaData3 = Struct.newBuilder()
                .putFields("chunk_text", Value.newBuilder().setStringValue("AAPL'\\''s strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production").build())
                .putFields("category", Value.newBuilder().setStringValue("technology").build())
                .putFields("quarter", Value.newBuilder().setStringValue("Q3").build())
                .build();

        sparseIndexConnection.upsert("vec1", Collections.emptyList(), indices1, values1, sparseMetaData1, "example-namespace");
        sparseIndexConnection.upsert("vec2", Collections.emptyList(), indices2, values2, sparseMetaData2, "example-namespace");
        sparseIndexConnection.upsert("vec3", Collections.emptyList(), indices3, values3, sparseMetaData3, "example-namespace");

        // Check the number of records each the index
        DescribeIndexStatsResponse denseIndexStatsResponse = denseIndexConnection.describeIndexStats(null);
        System.out.println("Index stats (dense):");
        System.out.println(denseIndexStatsResponse);
        DescribeIndexStatsResponse sparseIndexStatsResponse = sparseIndexConnection.describeIndexStats(null);
        System.out.println("Index stats (sparse):");
        System.out.println(sparseIndexStatsResponse);

        // Query the index (dense) with a metadata filter
        List<Float> queryVector = Arrays.asList(1.0f, 1.5f);
        QueryResponseWithUnsignedIndices denseQueryResponse = denseIndexConnection.query(1, queryVector, null, null, null, "example-namespace", null, false, true);
        System.out.println("Dense query response:");
        System.out.println(denseQueryResponse);

        // Query the index (sparse) with a metadata filter
        List<Long> sparseIndices = Arrays.asList(
                767227209L, 1640781426L, 1690623792L, 2021799277L, 2152645940L,
                2295025838L, 2443437770L, 2779594451L, 2956155693L, 3476647774L,
                3818127854L, 428309169L);
        List<Float> sparseValues = Arrays.asList(
                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f);

        QueryResponseWithUnsignedIndices sparseQueryResponse = sparseIndexConnection.query(1, null, sparseIndices, sparseValues, null, "example-namespace", null, false, true);
        System.out.println("Sparse query response:");
        System.out.println(sparseQueryResponse);

        // Delete the indexes
        pc.deleteIndex(denseIndexName);
        pc.deleteIndex(sparseIndexName);
    }
}
```

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

import (
	"context"
	"encoding/json"
	"fmt"
	"log"

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

	// Initialize a client.
	// No API key is required.
	// Host and port of the Pinecone Local instance
	// is required when starting without indexes.
	pc, err := pinecone.NewClientBase(pinecone.NewClientBaseParams{
		Host: "http://localhost:5080",
	})
	if err != nil {
		log.Fatalf("Failed to create Client: %v", err)
	}

	// Create two indexes, one dense and one sparse
	denseIndexName := "dense-index"
	denseVectorType := "dense"
	dimension := int32(2)
	denseMetric := pinecone.Cosine
	deletionProtection := pinecone.DeletionProtectionDisabled
	denseIdx, err := pc.CreateServerlessIndex(ctx, &pinecone.CreateServerlessIndexRequest{
		Name:               denseIndexName,
		VectorType:         &denseVectorType,
		Dimension:          &dimension,
		Metric:             &denseMetric,
		Cloud:              pinecone.Aws,
		Region:             "us-east-1",
		DeletionProtection: &deletionProtection,
		Tags:               &pinecone.IndexTags{"environment": "development"},
	})
	if err != nil {
		log.Fatalf("Failed to create serverless index: %v", denseIdx.Name)
	} else {
		fmt.Printf("Successfully created serverless index: %v\n", denseIdx.Name)
	}

	sparseIndexName := "sparse-index"
	sparseVectorType := "sparse"
	sparseMetric := pinecone.Dotproduct
	sparseIdx, err := pc.CreateServerlessIndex(ctx, &pinecone.CreateServerlessIndexRequest{
		Name:               sparseIndexName,
		VectorType:         &sparseVectorType,
		Metric:             &sparseMetric,
		Cloud:              pinecone.Aws,
		Region:             "us-east-1",
		DeletionProtection: &deletionProtection,
		Tags:               &pinecone.IndexTags{"environment": "development"},
	})
	if err != nil {
		log.Fatalf("Failed to create serverless index: %v", sparseIdx.Name)
	} else {
		fmt.Printf("\nSuccessfully created serverless index: %v\n", sparseIdx.Name)
	}

	// Get the index hosts
	denseIdxModel, err := pc.DescribeIndex(ctx, denseIndexName)
	if err != nil {
		log.Fatalf("Failed to describe index \"%v\": %v", denseIndexName, err)
	}

	sparseIdxModel, err := pc.DescribeIndex(ctx, sparseIndexName)
	if err != nil {
		log.Fatalf("Failed to describe index \"%v\": %v", sparseIndexName, err)
	}

	// Target the indexes.
	// Make sure to prefix the hosts with http:// to let the SDK know to disable tls.
	denseIdxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "http://" + denseIdxModel.Host, Namespace: "example-namespace"})
	if err != nil {
		log.Fatalf("Failed to create IndexConnection for Host: %v", err)
	}

	sparseIdxConnection, err := pc.Index(pinecone.NewIndexConnParams{Host: "http://" + sparseIdxModel.Host, Namespace: "example-namespace"})
	if err != nil {
		log.Fatalf("Failed to create IndexConnection for Host: %v", err)
	}

	// Upsert records into the index (dense)
	denseMetadataMap1 := map[string]interface{}{
		"genre": "drama",
	}

	denseMetadata1, err := structpb.NewStruct(denseMetadataMap1)
	if err != nil {
		log.Fatalf("Failed to create metadata map: %v", err)
	}

	denseMetadataMap2 := map[string]interface{}{
		"genre": "documentary",
	}

	denseMetadata2, err := structpb.NewStruct(denseMetadataMap2)
	if err != nil {
		log.Fatalf("Failed to create metadata map: %v", err)
	}

	denseMetadataMap3 := map[string]interface{}{
		"genre": "documentary",
	}

	denseMetadata3, err := structpb.NewStruct(denseMetadataMap3)
	if err != nil {
		log.Fatalf("Failed to create metadata map: %v", err)
	}

	denseVectors := []*pinecone.Vector{
		{
			Id:       "vec1",
			Values:   &[]float32{1.0, -2.5},
			Metadata: denseMetadata1,
		},
		{
			Id:       "vec2",
			Values:   &[]float32{3.0, -2.0},
			Metadata: denseMetadata2,
		},
		{
			Id:       "vec3",
			Values:   &[]float32{0.5, -1.5},
			Metadata: denseMetadata3,
		},
	}

	denseCount, err := denseIdxConnection.UpsertVectors(ctx, denseVectors)
	if err != nil {
		log.Fatalf("Failed to upsert vectors: %v", err)
	} else {
		fmt.Printf("\nSuccessfully upserted %d vector(s)!\n", denseCount)
	}

	// Upsert records into the index (sparse)
	sparseValues1 := pinecone.SparseValues{
		Indices: []uint32{822745112, 1009084850, 1221765879, 1408993854, 1504846510, 1596856843, 1640781426, 1656251611, 1807131503, 2543655733, 2902766088, 2909307736, 3246437992, 3517203014, 3590924191},
		Values:  []float32{1.7958984, 0.41577148, 2.828125, 2.8027344, 2.8691406, 1.6533203, 5.3671875, 1.3046875, 0.49780273, 0.5722656, 2.71875, 3.0820312, 2.5019531, 4.4414062, 3.3554688},
	}

	sparseMetadataMap1 := map[string]interface{}{
		"chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones",
		"category":   "technology",
		"quarter":    "Q3",
	}

	sparseMetadata1, err := structpb.NewStruct(sparseMetadataMap1)
	if err != nil {
		log.Fatalf("Failed to create metadata map: %v", err)
	}

	sparseValues2 := pinecone.SparseValues{
		Indices: []uint32{131900689, 592326839, 710158994, 838729363, 1304885087, 1640781426, 1690623792, 1807131503, 2066971792, 2428553208, 2548600401, 2577534050, 3162218338, 3319279674, 3343062801, 3476647774, 3485013322, 3517203014, 4283091697},
		Values:  []float32{0.4362793, 3.3457031, 2.7714844, 3.0273438, 3.3164062, 5.6015625, 2.4863281, 0.38134766, 1.25, 2.9609375, 0.34179688, 1.4306641, 0.34375, 3.3613281, 1.4404297, 2.2558594, 2.2597656, 4.8710938, 0.560546},
	}

	sparseMetadataMap2 := map[string]interface{}{
		"chunk_text": "Analysts suggest that AAPL's upcoming Q4 product launch event might solidify its position in the premium smartphone market.",
		"category":   "technology",
		"quarter":    "Q4",
	}

	sparseMetadata2, err := structpb.NewStruct(sparseMetadataMap2)
	if err != nil {
		log.Fatalf("Failed to create metadata map: %v", err)
	}

	sparseValues3 := pinecone.SparseValues{
		Indices: []uint32{8661920, 350356213, 391213188, 554637446, 1024951234, 1640781426, 1780689102, 1799010313, 2194093370, 2632344667, 2641553256, 2779594451, 3517203014, 3543799498, 3837503950, 4283091697},
		Values:  []float32{2.6875, 4.2929688, 3.609375, 3.0722656, 2.1152344, 5.78125, 3.7460938, 3.7363281, 1.2695312, 3.4824219, 0.7207031, 0.0826416, 4.671875, 3.7011719, 2.796875, 0.61621094},
	}

	sparseMetadataMap3 := map[string]interface{}{
		"chunk_text": "AAPL's strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production",
		"category":   "technology",
		"quarter":    "Q3",
	}

	sparseMetadata3, err := structpb.NewStruct(sparseMetadataMap3)
	if err != nil {
		log.Fatalf("Failed to create metadata map: %v", err)
	}

	sparseVectors := []*pinecone.Vector{
		{
			Id:           "vec1",
			SparseValues: &sparseValues1,
			Metadata:     sparseMetadata1,
		},
		{
			Id:           "vec2",
			SparseValues: &sparseValues2,
			Metadata:     sparseMetadata2,
		},
		{
			Id:           "vec3",
			SparseValues: &sparseValues3,
			Metadata:     sparseMetadata3,
		},
	}

	sparseCount, err := sparseIdxConnection.UpsertVectors(ctx, sparseVectors)
	if err != nil {
		log.Fatalf("Failed to upsert vectors: %v", err)
	} else {
		fmt.Printf("\nSuccessfully upserted %d vector(s)!\n", sparseCount)
	}

	// Check the number of records in each index
	denseStats, err := denseIdxConnection.DescribeIndexStats(ctx)
	if err != nil {
		log.Fatalf("Failed to describe index: %v", err)
	} else {
		fmt.Printf("\nIndex stats (dense): %+v\n", prettifyStruct(*denseStats))
	}

	sparseStats, err := sparseIdxConnection.DescribeIndexStats(ctx)
	if err != nil {
		log.Fatalf("Failed to describe index: %v", err)
	} else {
		fmt.Printf("\nIndex stats (sparse): %+v\n", prettifyStruct(*sparseStats))
	}

	// Query the index (dense) with a metadata filter
	queryVector := []float32{3.0, -2.0}

	queryMetadataMap := map[string]interface{}{
		"genre": map[string]interface{}{
			"$eq": "documentary",
		},
	}

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

	denseRes, err := denseIdxConnection.QueryByVectorValues(ctx, &pinecone.QueryByVectorValuesRequest{
		Vector:          queryVector,
		TopK:            1,
		MetadataFilter:  metadataFilter,
		IncludeValues:   false,
		IncludeMetadata: true,
	})
	if err != nil {
		log.Fatalf("Error encountered when querying by vector: %v", err)
	} else {
		fmt.Printf("\nDense query response: %v\n", prettifyStruct(denseRes))
	}

	// Query the index (sparse) with a metadata filter

	sparseValues := pinecone.SparseValues{
		Indices: []uint32{767227209, 1640781426, 1690623792, 2021799277, 2152645940, 2295025838, 2443437770, 2779594451, 2956155693, 3476647774, 3818127854, 4283091697},
		Values:  []float32{1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0},
	}

	sparseRes, err := sparseIdxConnection.QueryByVectorValues(ctx, &pinecone.QueryByVectorValuesRequest{
		SparseValues:    &sparseValues,
		TopK:            1,
		IncludeValues:   false,
		IncludeMetadata: true,
	})
	if err != nil {
		log.Fatalf("Error encountered when querying by vector: %v", err)
	} else {
		fmt.Printf("\nSparse query response: %v\n", prettifyStruct(sparseRes))
	}
	// Delete the indexes
	err = pc.DeleteIndex(ctx, denseIndexName)
	if err != nil {
		log.Fatalf("Failed to delete index: %v", err)
	} else {
		fmt.Printf("\nIndex \"%v\" deleted successfully\n", denseIndexName)
	}

	err = pc.DeleteIndex(ctx, sparseIndexName)
	if err != nil {
		log.Fatalf("Failed to delete index: %v", err)
	} else {
		fmt.Printf("\nIndex \"%v\" deleted successfully\n", sparseIndexName)
	}
}
```

```shell curl theme={null}
PINECONE_LOCAL_HOST="localhost:5080"
DENSE_INDEX_HOST="localhost:5081"
SPARSE_INDEX_HOST="localhost:5082"

# Create two indexes, one dense and one sparse
curl -X POST "http://$PINECONE_LOCAL_HOST/indexes" \
    -H "Accept: application/json" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{
            "name": "dense-index",
            "vector_type": "dense",
            "dimension": 2,
            "metric": "cosine",
            "spec": {
                "serverless": {
                    "cloud": "aws",
                    "region": "us-east-1"
                }
            },
            "tags": {
                "environment": "development"
            },
            "deletion_protection": "disabled"
        }'

curl -X POST "http://$PINECONE_LOCAL_HOST/indexes" \
    -H "Accept: application/json" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{
            "name": "sparse-index",
            "vector_type": "sparse",
            "metric": "dotproduct",
            "spec": {
                "serverless": {
                    "cloud": "aws",
                    "region": "us-east-1"
                }
            },
            "tags": {
                "environment": "development"
            },
            "deletion_protection": "disabled"
        }'

# Upsert records into the index (dense)
curl -X POST "http://$DENSE_INDEX_HOST/vectors/upsert" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{
            "namespace": "example-namespace",
            "vectors": [
                {
                    "id": "vec1", 
                    "values": [1.0, -2.5],
                    "metadata": {"genre": "drama"}
                },
                {
                    "id": "vec2", 
                    "values": [3.0, -2.0],
                    "metadata": {"genre": "documentary"}
                },
                {
                    "id": "vec3", 
                    "values": [0.5, -1.5],
                    "metadata": {"genre": "documentary"}
                }
            ]
        }'

# Upsert records into the index (sparse)
curl -X POST "http://$SPARSE_INDEX_HOST/vectors/upsert" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{
            "namespace": "example-namespace",
            "vectors": [
                {
                    "id": "vec1",
                    "sparseValues": {
                        "values": [1.7958984, 0.41577148, 2.828125, 2.8027344, 2.8691406, 1.6533203, 5.3671875, 1.3046875, 0.49780273, 0.5722656, 2.71875, 3.0820312, 2.5019531, 4.4414062, 3.3554688],
                        "indices": [822745112, 1009084850, 1221765879, 1408993854, 1504846510, 1596856843, 1640781426, 1656251611, 1807131503, 2543655733, 2902766088, 2909307736, 3246437992, 3517203014, 3590924191]
                    },
                    "metadata": {
                        "chunk_text": "AAPL reported a year-over-year revenue increase, expecting stronger Q3 demand for its flagship phones.",
                        "category": "technology",
                        "quarter": "Q3"
                    }
                },
                {
                    "id": "vec2",
                    "sparseValues": {
                        "values": [0.4362793, 3.3457031, 2.7714844, 3.0273438, 3.3164062, 5.6015625, 2.4863281, 0.38134766, 1.25, 2.9609375, 0.34179688, 1.4306641, 0.34375, 3.3613281, 1.4404297, 2.2558594, 2.2597656, 4.8710938, 0.5605469],
                        "indices": [131900689, 592326839, 710158994, 838729363, 1304885087, 1640781426, 1690623792, 1807131503, 2066971792, 2428553208, 2548600401, 2577534050, 3162218338, 3319279674, 3343062801, 3476647774, 3485013322, 3517203014, 4283091697]
                    },
                    "metadata": {
                        "chunk_text": "Analysts suggest that AAPL'\''s upcoming Q4 product launch event might solidify its position in the premium smartphone market.",
                        "category": "technology",
                        "quarter": "Q4"
                    }
                },
                {
                    "id": "vec3",
                    "sparseValues": {
                        "values": [2.6875, 4.2929688, 3.609375, 3.0722656, 2.1152344, 5.78125, 3.7460938, 3.7363281, 1.2695312, 3.4824219, 0.7207031, 0.0826416, 4.671875, 3.7011719, 2.796875, 0.61621094],
                        "indices": [8661920, 350356213, 391213188, 554637446, 1024951234, 1640781426, 1780689102, 1799010313, 2194093370, 2632344667, 2641553256, 2779594451, 3517203014, 3543799498, 3837503950, 4283091697]
                    },
                    "metadata": {
                        "chunk_text": "AAPL'\''s strategic Q3 partnerships with semiconductor suppliers could mitigate component risks and stabilize iPhone production",
                        "category": "technology",
                        "quarter": "Q3"
                    }
                }
            ]
        }'

# Check the number of records in each index
curl -X POST "http://$DENSE_INDEX_HOST/describe_index_stats" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{}'

curl -X POST "http://$SPARSE_INDEX_HOST/describe_index_stats" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{}'

# Query the index (dense) with a metadata filter
curl "http://$DENSE_INDEX_HOST/query" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{
            "vector": [3.0, -2.0],
            "filter": {"genre": {"$eq": "documentary"}},
            "topK": 1,
            "includeMetadata": true,
            "includeValues": false,
            "namespace": "example-namespace"
        }'

# Query the index (sparse) with a metadata filter
curl "http://$SPARSE_INDEX_HOST/query" \
    -H "Content-Type: application/json" \
    -H "X-Pinecone-Api-Version: 2025-10" \
    -d '{
            "sparseVector": {
                "values": [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
                "indices": [767227209, 1640781426, 1690623792, 2021799277, 2152645940, 2295025838, 2443437770, 2779594451, 2956155693, 3476647774, 3818127854, 4283091697]
            },
            "filter": {"quarter": {"$eq": "Q4"}},
            "namespace": "example-namespace",
            "topK": 1,
            "includeMetadata": true,
            "includeValues": false
        }'

# Delete the index
curl -X DELETE "http://$PINECONE_LOCAL_HOST/indexes/dense-index" \
    -H "X-Pinecone-Api-Version: 2025-10"

curl -X DELETE "http://$PINECONE_LOCAL_HOST/indexes/sparse-index" \
    -H "X-Pinecone-Api-Version: 2025-10"
```
:::

## 3. Stop Pinecone Local

:::callout{intent="warning"}
Pinecone Local is an in-memory emulator. Records loaded into Pinecone Local do not persist after Pinecone Local is stopped.
:::

To stop and remove the resources for Pinecone Local, run the following command:

:::code-group
```shell Docker Compose theme={null}
docker compose down
```

```shell Docker CLI theme={null}
# If you started Pinecone Local with indexes:
docker stop dense-index sparse-index
docker rm dense-index sparse-index

# If you started Pinecone Local without indexes:
docker stop pinecone-local
docker rm pinecone-local
```
:::

## Moving from Pinecone Local to your Pinecone account

When you're ready to run your application against your Pinecone account, be sure to do the following:

- Update your application to [use your Pinecone API key](/guides/apis-authentication).
- Update your application to [target your Pinecone indexes](/guides/manage-data-target-an-index).
- [Use Pinecone's import feature](/guides/index-data-import-data) to efficiently load large amounts of data into your indexes and then [use batch upserts](/guides/index-data-upsert-data#upsert-in-batches) for ongoing writes.
- Follow Pinecone's [production best practices](/guides/move-to-production-production-checklist).

## 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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