https://github.com/pinecone-io/pinecone-ts-client
The official TypeScript/Node client for the Pinecone vector database
https://github.com/pinecone-io/pinecone-ts-client
llm pinecone semantic-search similarity-search vector-database
Last synced: about 2 months ago
JSON representation
The official TypeScript/Node client for the Pinecone vector database
- Host: GitHub
- URL: https://github.com/pinecone-io/pinecone-ts-client
- Owner: pinecone-io
- License: apache-2.0
- Created: 2022-12-23T19:47:24.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-05-07T23:21:03.000Z (about 2 months ago)
- Last Synced: 2025-05-08T00:26:11.977Z (about 2 months ago)
- Topics: llm, pinecone, semantic-search, similarity-search, vector-database
- Language: TypeScript
- Homepage: https://www.pinecone.io
- Size: 2.08 MB
- Stars: 227
- Watchers: 25
- Forks: 40
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
Awesome Lists containing this project
README
# Pinecone Node.js SDK ·    
This is the official Node.js SDK for [Pinecone](https://www.pinecone.io), written in TypeScript.
## Documentation
- [**Reference Documentation**](https://sdk.pinecone.io/typescript/classes/Pinecone.html)
- If you are upgrading from `v0.x`, check out the [**v1 Migration Guide**](https://github.com/pinecone-io/pinecone-ts-client/blob/main/v1-migration.md).
- If you are upgrading from `v1.x`, check out the [**v2 Migration Guide**](https://github.com/pinecone-io/pinecone-ts-client/blob/main/v2-migration.md).
### Example code
The snippets shown in this README are intended to be concise. For more realistic examples, explore these examples:
- [Semantic search](https://github.com/pinecone-io/semantic-search-example)
- [Article recommender](https://github.com/pinecone-io/recommender-example-typescript)
- [Image search](https://github.com/pinecone-io/image-search-example)
## Upgrading the SDK
#### Upgrading from `2.x` to `3.x`
There is a breaking change involving the `configureIndex` operation in this update. The structure of the object passed
when configuring an index has changed to include `deletionProtection`. The `podType` and `replicas` fields can now be updated through the `spec.pod` object. See [Configure pod-based indexes](#configure-pod-based-indexes) for an example of the code.
#### Upgrading from older versions
- **Upgrading to `2.x`** : There were many changes made in this release to support Pinecone's new Serverless index offering. The changes are covered in detail in the [**v2 Migration Guide**](https://github.com/pinecone-io/pinecone-ts-client/blob/main/v2-migration.md). Serverless indexes are only available in `2.x` release versions or greater.
- **Upgrading to `1.x`** : This release officially moved the SDK out of beta, and there are a number of breaking changes that need to be addressed when upgrading from a `0.x` version. See the [**v1 Migration Guide**](https://github.com/pinecone-io/pinecone-ts-client/blob/main/v1-migration.md) for details.
## Prerequisites
The Pinecone TypeScript SDK is compatible with TypeScript >=4.1 and Node >=18.x.
## Installation
```
npm install @pinecone-database/pinecone
```
## Productionizing
The Pinecone Typescript SDK is intended for **server-side use only**. Using the SDK within a browser context can **expose
your API key(s)**. If you have deployed the SDK to production in a browser, **please rotate your API keys.**
## Usage
### Initializing the client
An API key is required to initialize the client. It can be passed using an environment variable or in code through a configuration object. Get an API key in the [console](https://app.pinecone.io).
#### Using environment variables
The environment variable used to configure the API key for the client is the following:
```bash
PINECONE_API_KEY="your_api_key"
```
`PINECONE_API_KEY` is the only required variable. When this environment variable is set, the client constructor does not require any additional arguments.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
```
#### Using a configuration object
If you prefer to pass configuration in code, the constructor accepts a config object containing the `apiKey` value.
This is the object in which you would pass properties like `maxRetries` (defaults to `3`) for retryable operations
(`upsert`, `update`, and `configureIndex`).
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone({
apiKey: 'your_api_key',
maxRetries: 5,
});
```
### Using a proxy server
If your network setup requires you to interact with Pinecone via a proxy, you can pass a custom `ProxyAgent` from
the [`undici` library](https://undici.nodejs.org/#/). Below is an example of how to
construct an `undici` `ProxyAgent` that routes network traffic through a [`mitm` proxy server](https://mitmproxy.org/) while hitting Pinecone's `/indexes` endpoint.
**Note:** The following strategy relies on Node's native `fetch` implementation, [released in Node v16 and
stabilized in Node v21](https://nodejs.org/docs/latest/api/globals.html#fetch). If you are running Node versions
18-21, you may experience issues stemming from the instability of the feature. There are currently no known issues
related to proxying in Node v18+.
```typescript
import {
Pinecone,
type PineconeConfiguration,
} from '@pinecone-database/pinecone';
import { Dispatcher, ProxyAgent } from 'undici';
import * as fs from 'fs';
const cert = fs.readFileSync('path-to-your-mitm-proxy-cert-pem-file');
const client = new ProxyAgent({
uri: '',
requestTls: {
port: '',
ca: cert,
host: '',
},
});
const customFetch = (
input: string | URL | Request,
init: RequestInit | undefined
) => {
return fetch(input, {
...init,
dispatcher: client as Dispatcher,
keepalive: true, # optional
});
};
const config: PineconeConfiguration = {
apiKey:
'',
fetchApi: customFetch,
};
const pc = new Pinecone(config);
const indexes = async () => {
return await pc.listIndexes();
};
indexes().then((response) => {
console.log('My indexes: ', response);
});
```
## Indexes
### Create Index
#### Create a serverless index with minimal configuration
At a minimum, to create a serverless index you must specify a `name`, `dimension`, and `spec`. The `dimension`
indicates the size of the vectors you intend to store in the index. For example, if your intention was to store and
query embeddings (vectors) generated with OpenAI's [textembedding-ada-002](https://platform.openai.com/docs/guides/embeddings/second-generation-models) model, you would need to create an index with dimension `1536` to match the output of that model. By default, serverless indexes will have a `vectorType` of `dense`.
The `spec` configures how the index should be deployed. For serverless indexes, you define only the cloud and region where the index should be hosted. For pod-based indexes, you define the environment where the index should be hosted, the pod type and size to use, and other index characteristics. For more information on serverless and regional availability, see [Understanding indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes#serverless-indexes).
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'sample-index',
dimension: 1536,
spec: {
serverless: {
cloud: 'aws',
region: 'us-west-2',
},
},
tags: { team: 'data-science' },
});
```
#### Create a sparse serverless index
You can also use `vectorType` to create `sparse` serverless indexes. These indexes enable direct indexing and retrieval of sparse vectors, supporting traditional methods like BM25 and learned sparse models such as [pinecone-sparse-english-v0](https://docs.pinecone.io/models/pinecone-sparse-english-v0). A `sparse` index must have a distance `metric` of `dotproduct` and does not require a specified dimension. If no
metric is provided with a `vectorType` of `sparse`, it will default to `dotproduct`:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'sample-index',
metric: 'dotproduct',
spec: {
serverless: {
cloud: 'aws',
region: 'us-west-2',
},
},
tags: { team: 'data-science' },
vectorType: 'sparse',
});
```
#### Create an integrated index
Integrated inference requires a serverless index configured for a specific embedding model. You can either create a new index for a model or configure an existing index for a model. To create an index that accepts source text and converts it to vectors automatically using an embedding model hosted by Pinecone, use the `createIndexForModel` method:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndexForModel({
name: 'integrated-index',
cloud: 'aws',
region: 'us-east-1',
embed: {
model: 'multilingual-e5-large',
fieldMap: { text: 'chunk_text' },
},
waitUntilReady: true,
});
```
#### Create a pod-based index with optional configurations
To create a pod-based index, you define `pod` in the `spec` object which contains the `environment` where the index should be hosted, and the `podType` and `pods` size to use. Many optional configuration fields allow greater control over hardware resources and availability. To learn more about the purpose of these fields, see [Understanding indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes) and [Scale pod-based indexes](https://docs.pinecone.io/guides/indexes/scale-pod-based-indexes).
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'sample-index-2',
dimension: 1536,
metric: 'dotproduct',
spec: {
pod: {
environment: 'us-east4-gcp',
pods: 2,
podType: 'p1.x2',
metadataConfig: {
indexed: ['product_type'],
},
},
tags: { team: 'data-science' },
},
// This option tells the client not to throw if the index already exists.
suppressConflicts: true,
// This option tells the client not to resolve the promise until the
// index is ready.
waitUntilReady: true,
});
```
#### Checking the status of a newly created index
The `createIndex` method issues a create request to the API that returns quickly, but the resulting index is
not immediately ready for upserting, querying, or performing other data operations. You can use the
`describeIndex` method to find out the status of an index and see whether it is ready for use.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.describeIndex('serverless-index');
// {
// name: 'serverless-index',
// dimension: 1536,
// metric: 'cosine',
// host: 'serverless-index-4zo0ijk.svc.us-west2-aws.pinecone.io',
// deletionProtection: 'disabled',
// spec: {
// serverless: {
// cloud: 'aws',
// region: 'us-west-2'
// }
// },
// status: {
// ready: false,
// state: 'Initializing'
// }
// }
```
#### Waiting until the index is ready
If you pass the `waitUntilReady` option, the client will handle polling for status updates on a newly created index. The promise returned by `createIndex` will not be resolved until the index status indicates it is ready to handle data operations. This can be especially useful for integration testing, where index creation in a setup step will be immediately followed by data operations.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'serverless-index',
dimension: 1536,
spec: {
serverless: {
cloud: 'aws',
region: 'us-west-2',
},
},
waitUntilReady: true,
});
```
#### Create a pod-based index from a Pinecone collection
> ℹ️ **Note**
>
> Serverless and starter indexes do not support collections.
As you use Pinecone for more things, you may wish to explore different index configurations with the same vector data. [Collections](https://docs.pinecone.io/guides/indexes/understanding-collections) provide an easy way to do this. See other client methods for working with collections [here](https://github.com/pinecone-io/pinecone-ts-client#collections).
Given that you have an existing collection:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.describeCollection('product-description-embeddings');
// {
// name: 'product-description-embeddings',
// size: 543427063,
// status: 'Ready',
// dimension: 2,
// vectorCount: 10001498,
// environment: 'us-east4-gcp'
// }
```
**Note:** For pod-based indexes, you can specify a `sourceCollection` from which to create an index. The
collection must be in the same environment as the index.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'product-description-p1x1',
dimension: 256,
metric: 'cosine',
spec: {
pod: {
environment: 'us-east4-gcp',
pods: 1,
podType: 'p1.x1',
sourceCollection: 'product-description-embeddings',
},
},
});
```
When the new index is ready, it should contain all the data that was in the collection, ready to be queried.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.index('product-description-p2x2').describeIndexStats();
// {
// namespaces: { '': { recordCount: 78000 } },
// dimension: 256,
// indexFullness: 0.9,
// totalRecordCount: 78000
// }
```
#### Create or configure an index with deletion protection
You can configure both serverless and pod indexes with `deletionProtection`. Any index with this property set to `'enabled'` will be unable to be deleted. By default, `deletionProtection` will be set to `'disabled'` if not provided as a part of the `createIndex` request. To enable `deletionProtection` you can pass the value while calling `createIndex`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'deletion-protected-index',
dimension: 1536,
metric: 'cosine',
deletionProtection: 'enabled',
spec: {
serverless: {
cloud: 'aws',
region: 'us-west-2',
},
},
});
```
To disable deletion protection, you can use the `configureIndex` operation.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.configureIndex('deletion-protected-index', {
deletionProtection: 'disabled',
});
```
#### Create or configure an index with index tags
You can create or configure serverless and pod indexes with [tags](https://docs.pinecone.io/guides/indexes/tag-an-index).
Indexes can hold an arbitrary number of tags outlining metadata
you would like attached to the index object itself, such as team ownership, project, or any other relevant information.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
// Create index with tag
await pc.createIndex({
name: 'tag-index',
dimension: 1536,
metric: 'cosine',
spec: {
serverless: {
cloud: 'aws',
region: 'us-west-2',
},
},
tags: { team: 'data-science' }, // index tag
});
// Configure index with a new tag
await pc.configureIndex('tag-index', {
tags: { project: 'recommendation' }, // new index tag
});
// Delete an existing tag
await pc.configureIndex('tag-index', {
tags: { project: '' }, // Pass an empty string to an existing key to delete a tag; this will delete the `project` tag
});
```
### Describe Index
You can fetch the description of any index by name using `describeIndex`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.describeIndex('serverless-index');
// {
// name: 'serverless-index',
// dimension: 1536,
// metric: 'cosine',
// host: 'serverless-index-4zo0ijk.svc.us-west2-aws.pinecone.io',
// deletionProtection: 'disabled',
// spec: {
// serverless: {
// cloud: 'aws',
// region: 'us-west-2'
// },
// },
// status: {
// ready: true,
// state: 'Ready'
// }
// }
```
### Configure pod-based indexes
> ℹ️ **Note**
>
> This section applies to [pod-based indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes#pod-based-indexes) only. With serverless indexes, you don't configure any compute or storage resources. Instead, serverless indexes scale automatically based on usage.
You can adjust the number of replicas or scale to a larger pod size (specified with `podType`). See [Scale pod-based indexes](https://docs.pinecone.io/guides/indexes/scale-pod-based-indexes). You cannot downgrade pod size or change the base pod type.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.configureIndex('pod-index', {
spec: {
pod: {
replicas: 2,
podType: 'p1.x4',
},
},
});
const config = await pc.describeIndex('pod-index');
// {
// name: 'pod-index',
// dimension: 1536,
// metric: 'cosine',
// host: 'pod-index-4zo0ijk.svc.us-east1-gcp.pinecone.io',
// deletionProtection: 'disabled',
// spec: {
// pod: {
// environment: 'us-east1-gcp',
// replicas: 2,
// shards: 2,
// podType: 'p1.x4',
// pods: 4,
// metadataConfig: [Object],
// sourceCollection: undefined
// }
// },
// status: {
// ready: true,
// state: 'ScalingUpPodSize'
// }
// }
```
### Delete Index
Indexes are deleted by name.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.deleteIndex('sample-index');
```
### List Indexes
The `listIndexes` command returns an object with an array of index models under `indexes`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.listIndexes();
// {
// indexes: [
// {
// name: 'serverless-index',
// dimension: 1536,
// metric: 'cosine',
// host: 'serverless-index-4zo0ijk.svc.us-west2-aws.pinecone.io',
// deletionProtection: 'disabled',
// spec: {
// serverless: {
// cloud: 'aws',
// region: 'us-west-2',
// },
// },
// status: {
// ready: true,
// state: 'Ready',
// },
// },
// {
// name: 'pod-index',
// dimension: 1536,
// metric: 'cosine',
// host: 'pod-index-4zo0ijk.svc.us-west2-aws.pinecone.io',
// deletionProtection: 'disabled',
// spec: {
// pod: {
// environment: 'us-west2-aws',
// replicas: 1,
// shards: 1,
// podType: 'p1.x1',
// pods: 1,
// },
// },
// status: {
// ready: true,
// state: 'Ready',
// },
// },
// ],
// }
```
## Collections
> ℹ️ **Note**
>
> Serverless and starter indexes do not support collections.
A collection is a static copy of a pod-based index that may be used to create backups, to create copies of indexes, or to perform experiments with different index configurations. To learn more about Pinecone collections, see [Understanding collections](https://docs.pinecone.io/guides/indexes/understanding-collections).
### Create Collection
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createCollection({
name: 'collection-name',
source: 'index-name',
});
```
This API call should return quickly, but the creation of a collection can take from minutes to hours depending on the size of the source index and the index's configuration. Use `describeCollection` to check the status of a collection.
### Delete Collection
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.deleteCollection('collection-name');
```
You can use `listCollections` to confirm the deletion.
### Describe Collection
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const describeCollection = await pc.describeCollection('collection3');
// {
// name: 'collection3',
// size: 3126700,
// status: 'Ready',
// dimension: 3,
// vectorCount: 1234,
// environment: 'us-east1-gcp',
// }
```
### List Collections
The `listCollections` command returns an object with an array of collection models under `collections`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const list = await pc.listCollections();
// {
// collections: [
// {
// name: 'collection1',
// size: 3089687,
// status: 'Ready',
// dimension: 3,
// vectorCount: 17378,
// environment: 'us-west1-gcp',
// },
// {
// name: 'collection2',
// size: 208309,
// status: 'Ready',
// dimension: 3,
// vectorCount: 1000,
// environment: 'us-east4-gcp',
// },
// ];
// }
```
## Backups
A backup is a static copy of a serverless index that only consumes storage. It is a non-queryable representation of a set of records. You can create a backup of a serverless index, and you can create a new serverless index from a backup. You can optionally apply new `tags` and `deletionProtection` configurations to the index. You can read more about [backups here](https://docs.pinecone.io/guides/manage-data/backups-overview).
### Create a backup
You can create a new backup from an existing index using the index name:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const backup = await pc.createBackup({
indexName: 'my-index',
name: 'my-index-backup-1',
description: 'weekly backup',
});
console.log(backup);
// {
// backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
// sourceIndexName: 'my-index',
// sourceIndexId: 'b480770b-600d-4c4e-bf19-799c933ae2bf',
// name: 'my-index-backup-1',
// description: 'weekly backup',
// status: 'Initializing',
// cloud: 'aws',
// region: 'us-east-1',
// dimension: 1024,
// metric: 'cosine',
// recordCount: 500,
// namespaceCount: 4,
// sizeBytes: 78294,
// tags: {},
// createdAt: '2025-05-07T03:11:11.722238160Z'
// }
```
### Create a new index from a backup
You can restore a serverless index by creating a new index from a backup. Optionally, you can provide
new `tags` or `deletionProtection` values when restoring an index. Creating an index from a backup intiates
a new restore job, which can be used to view the progress of the index restoration through `describeRestoreJob`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const response = await pc.createIndexFromBackup({
backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
name: 'my-index-restore-1',
});
console.log(response);
// {
// restoreJobId: '4d4c8693-10fd-4204-a57b-1e3e626fca07',
// indexId: 'deb7688b-9f21-4c16-8eb7-f0027abd27fe'
// }
```
### Describe and list backups
You can use a `backupId` and the `describeBackup` method to describe a specific backup:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const backup = await pc.describeBackup('11450b9f-96e5-47e5-9186-03f346b1f385');
console.log(backup);
// {
// backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
// sourceIndexName: 'my-index',
// sourceIndexId: 'b480770b-600d-4c4e-bf19-799c933ae2bf',
// name: 'my-index-backup-1',
// description: 'weekly backup',
// status: 'Initializing',
// cloud: 'aws',
// region: 'us-east-1',
// dimension: 1024,
// metric: 'cosine',
// recordCount: 500,
// namespaceCount: 4,
// sizeBytes: 78294,
// tags: {},
// createdAt: '2025-05-07T03:11:11.722238160Z'
// }
```
`listBackups` lists all the backups for a specific index, or your entire project. If an `indexName` is provided, only
the backups for that index will be listed.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
// list backups for the entire project
const projectBackups = await pc.listBackups({ limit: 2 });
// list backups for a specific index
const myIndexBackups = await pc.listBackups({
indexName: 'my-index',
limit: 2,
});
console.log(myIndexBackups);
// {
// data: [
// {
// backupId: '6a00902c-d118-4ad3-931c-49328c26d558',
// sourceIndexName: 'my-index',
// sourceIndexId: '0888b4d9-0b7b-447e-a403-ab057ceee4d4',
// name: 'my-index-backup-2',
// description: undefined,
// status: 'Ready',
// cloud: 'aws',
// region: 'us-east-1',
// dimension: 5,
// metric: 'cosine',
// recordCount: 200,
// namespaceCount: 2,
// sizeBytes: 67284,
// tags: {},
// createdAt: '2025-05-07T18:34:13.626650Z'
// },
// {
// backupId: '2b362ea3-b7cf-4950-866f-0dff37ab781e',
// sourceIndexName: 'my-index',
// sourceIndexId: '0888b4d9-0b7b-447e-a403-ab057ceee4d4',
// name: 'my-index-backup-1',
// description: undefined,
// status: 'Ready',
// cloud: 'aws',
// region: 'us-east-1',
// dimension: 1024,
// metric: 'cosine',
// recordCount: 500,
// namespaceCount: 4,
// sizeBytes: 78294,
// tags: {},
// createdAt: '2025-05-07T18:33:59.888270Z'
// },
// ],
// pagination: undefined
// }
```
### Describe and list restore jobs
You can use a `restoreJobId` and the `describeRestoreJob` method to describe a specific backup:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const restoreJob = await pc.describeRestoreJob(
'4d4c8693-10fd-4204-a57b-1e3e626fca07'
);
console.log(restoreJob);
// {
// restoreJobId: '4d4c8693-10fd-4204-a57b-1e3e626fca07',
// backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
// targetIndexName: 'my-index-restore-1',
// targetIndexId: 'deb7688b-9f21-4c16-8eb7-f0027abd27fe',
// status: 'Completed',
// createdAt: 2025-05-07T03:38:37.107Z,
// completedAt: 2025-05-07T03:40:23.687Z,
// percentComplete: 100
// }
```
`listRestoreJobs` lists all the restore jobs for your project.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const projectRestoreJobs = await pc.listRestoreJobs({ limit: 3 });
console.log(projectRestoreJobs);
// {
// data: [
// {
// restoreJobId: '4d4c8693-10fd-4204-a57b-1e3e626fca07',
// backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
// targetIndexName: 'my-index-restore-1',
// targetIndexId: 'deb7688b-9f21-4c16-8eb7-f0027abd27fe',
// status: 'Completed',
// createdAt: 2025-05-07T03:38:37.107Z,
// completedAt: 2025-05-07T03:40:23.687Z,
// percentComplete: 100
// },
// {
// restoreJobId: 'c60a62e0-63b9-452a-88af-31d89c56c988',
// backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
// targetIndexName: 'my-index-restore-2',
// targetIndexId: 'f2c9a846-799f-4b19-81a4-f3096b3d6114',
// status: 'Completed',
// createdAt: 2025-05-07T21:42:38.971Z,
// completedAt: 2025-05-07T21:43:11.782Z,
// percentComplete: 100
// },
// {
// restoreJobId: '792837b7-8001-47bf-9c11-1859826b9c10',
// backupId: '11450b9f-96e5-47e5-9186-03f346b1f385',
// targetIndexName: 'my-index-restore-3',
// targetIndexId: '620dda62-c999-4dd1-b083-6beb087b31e7',
// status: 'Pending',
// createdAt: 2025-05-07T21:48:39.580Z,
// completedAt: 2025-05-07T21:49:12.084Z,
// percentComplete: 45
// }
// ],
// pagination: undefined
// }
```
### Delete backups
You can delete a backup using the backupId and `deleteBackup`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.deleteBackup('6a00902c-d118-4ad3-931c-49328c26d558');
```
## Index operations
Pinecone indexes support operations for working with vector data using methods such as upsert, query, fetch, and delete.
### Targeting an index
To perform data operations on an index, you target it using the `index` method.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('test-index');
// Now perform index operations
await index.fetch(['1']);
```
The first argument is the name of the index you are targeting. There's an optional second argument for providing an
index host override. Providing this second argument allows you to bypass the SDK's default behavior of resolving
your index host via the provided index name. You can find your index host in the [Pinecone console](https://app.pinecone.io), or by using the `describeIndex` or `listIndexes` operations.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('test-index', 'my-index-host-1532-svc.io');
// Now perform index operations against: https://my-index-host-1532-svc.io
await index.fetch(['1']);
```
### Targeting an index, with metadata typing
If you are storing metadata alongside your vector values, you can pass a type parameter to `index()` in order to get proper TypeScript typechecking.
```typescript
import { Pinecone, PineconeRecord } from '@pinecone-database/pinecone';
const pc = new Pinecone();
type MovieMetadata = {
title: string,
runtime: number,
genre: 'comedy' | 'horror' | 'drama' | 'action'
}
// Specify a custom metadata type while targeting the index
const index = pc.index('test-index');
// Now you get type errors if upserting malformed metadata
await index.upsert([{
id: '1234',
values: [
.... // embedding values
],
metadata: {
genre: 'Gone with the Wind',
runtime: 238,
genre: 'drama',
// @ts-expect-error because category property not in MovieMetadata
category: 'classic'
}
}])
const results = await index.query({
vector: [
... // query embedding
],
filter: { genre: { '$eq': 'drama' }}
})
const movie = results.matches[0];
if (movie.metadata) {
// Since we passed the MovieMetadata type parameter above,
// we can interact with metadata fields without having to
// do any typecasting.
const { title, runtime, genre } = movie.metadata;
console.log(`The best match in drama was ${title}`)
}
```
### Targeting a namespace
By default, all data operations take place inside the default namespace of `''`. If you are working with other non-default namespaces, you can target the namespace by chaining a call to `namespace()`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('test-index').namespace('ns1');
// Now perform index operations in the targeted index and namespace
await index.fetch(['1']);
```
See [Use namespaces](https://docs.pinecone.io/guides/indexes/use-namespaces) for more information.
### Managing namespaces
There are several operations for managing namespaces within an index. You can list the namespaces within an index, describe a specific namespace, or delete a namespace entirely.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('test-index');
// list all namespaces
const namespacesResp = await index.listNamespaces();
// describe a namespace
const namespace = await index.describeNamespace('ns1');
// delete a namespace (including all record data)
await index.deleteNamespace('ns1');
```
### Upsert vectors
Pinecone expects records inserted into indexes to have the following form:
```typescript
type PineconeRecord = {
id: string;
values: Array;
sparseValues?: Array;
metadata?: object;
};
```
To upsert some vectors, you can use the client like so:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
// Target an index
const index = pc.index('sample-index');
// Prepare your data. The length of each array
// of vector values must match the dimension of
// the index where you plan to store them.
const vectors = [
{
id: '1',
values: [0.236, 0.971, 0.559],
sparseValues: { indices: [0, 1], values: [0.236, 0.34] }, // Optional; for hybrid search
},
{
id: '2',
values: [0.685, 0.111, 0.857],
sparseValues: { indices: [0, 1], values: [0.345, 0.98] }, // Optional; for hybrid search
},
];
// Upsert the data into your index
await index.upsert(vectors);
```
### Import vectors from object storage
You can now [import vectors en masse](https://docs.pinecone.io/guides/data/understanding-imports) from object
storage. `Import` is a long-running, asynchronous operation that imports large numbers of records into a Pinecone
serverless index.
In order to import vectors from object storage, they must be stored in Parquet files and adhere to the necessary
[file format](https://docs.pinecone.io/guides/data/understanding-imports#parquet-file-format). Your object storage
must also adhere to the necessary [directory structure](https://docs.pinecone.io/guides/data/understanding-imports#directory-structure).
The following example imports vectors from an Amazon S3 bucket into a Pinecone serverless index:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const indexName = 'sample-index';
await pc.createIndex({
name: indexName,
dimension: 10,
spec: {
serverless: {
cloud: 'aws',
region: 'eu-west-1',
},
},
});
const index = pc.Index(indexName);
const storageURI = 's3://my-bucket/my-directory/';
await index.startImport(storageURI, 'continue'); // "Continue" will avoid aborting the operation if errors are encountered.
// {
// "id": "import-id"
// }
```
You can [start, cancel, and check the status](https://docs.pinecone.io/guides/data/import-data) of all or one import operation(s).
**Notes:**
- `Import` only works with Serverless indexes
- `Import` is in [public preview](https://docs.pinecone.io/release-notes/feature-availability)
- The only object storage provider currently supported is [Amazon S3](https://docs.pinecone.io/guides/operations/integrations/integrate-with-amazon-s3)
- Vectors will take _at least 10 minutes_ to appear in your index upon completion of the import operation, since
this operation is optimized for very large workloads
- See [limits](https://docs.pinecone.io/guides/data/understanding-imports#limits) for further information
### Seeing index statistics
When experimenting with data operations, it's sometimes helpful to know how many records/vectors are stored in each
namespace. In that case, target the index and use the `describeIndexStats()` command.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('example-index');
await index.describeIndexStats();
// {
// namespaces: {
// '': { recordCount: 10 }
// foo: { recordCount: 2000 },
// bar: { recordCount: 2000 }
// },
// dimension: 1536,
// indexFullness: 0,
// totalRecordCount: 4010
// }
```
### Querying
#### Querying with vector values
The query method accepts a large number of options. The dimension of the query vector must match the dimension of your index.
```typescript
type QueryOptions = {
topK: number; // number of results desired
vector?: Array; // must match dimension of index
sparseVector?: {
indices: Array; // indices must fall within index dimension
values: Array; // indices and values arrays must have same length
};
id?: string;
includeMetadata: boolean;
includeValues: boolean;
};
```
For example, to query by vector values you would pass the `vector` param in the options configuration. For brevity sake this example query vector is tiny (dimension 2), but in a more realistic use case this query vector would be an embedding outputted by a model. Look at the [Example code](#example-code) to see more realistic examples of how to use `query`.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
await index.query({ topK: 3, vector: [0.22, 0.66] });
// {
// matches: [
// {
// id: '556',
// score: 1.00000012,
// values: [],
// sparseValues: undefined,
// metadata: undefined
// },
// {
// id: '137',
// score: 1.00000012,
// values: [],
// sparseValues: undefined,
// metadata: undefined
// },
// {
// id: '129',
// score: 1.00000012,
// values: [],
// sparseValues: undefined,
// metadata: undefined
// }
// ],
// namespace: '',
// usage: {
// readUnits: 5
// }
// }
```
You include options to `includeMetadata: true` or `includeValues: true` if you need this information. By default,
these are not returned to keep the response payload small.
Remember that data operations take place within the context of a `namespace`, so if you are working with namespaces and do not see expected results you should check that you are targeting the correct namespace with your query.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
// Target the index and namespace
const index = pc.index('my-index').namespace('my-namespace');
const results = await index.query({ topK: 3, vector: [0.22, 0.66] });
```
#### Querying by record id
You can query using the vector values of an existing record in the index by passing a record ID. Please note that
the record with the specified ID [may be in this operation's response](https://docs.pinecone.io/troubleshooting/limitations-of-querying-by-id).
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
const results = await index.query({ topK: 10, id: '1' });
```
#### Hybrid search with sparse vectors
If you are working with [sparse-dense vectors](https://docs.pinecone.io/guides/data/understanding-hybrid-search#sparse-dense-workflow), you can add sparse vector values to perform a hybrid search.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.createIndex({
name: 'hybrid-search-index',
metric: 'dotproduct', // Note: dot product is the only distance metric supported for hybrid search
dimension: 2,
spec: {
pod: {
environment: 'us-west4-gcp',
podType: 'p2.x1',
},
},
waitUntilReady: true,
});
const index = pc.index('hybrid-search-index');
const hybridRecords = [
{
id: '1',
values: [0.236, 0.971], // dense vectors
sparseValues: { indices: [0, 1], values: [0.236, 0.34] }, // sparse vectors
},
{
id: '2',
values: [0.685, 0.111],
sparseValues: { indices: [0, 1], values: [0.887, 0.243] },
},
];
await index.upsert(hybridRecords);
const query = 'What is the most popular red dress?';
// ... send query to dense vector embedding model and save those values in `denseQueryVector`
// ... send query to sparse vector embedding model and save those values in `sparseQueryVector`
const denseQueryVector = [0.236, 0.971];
const sparseQueryVector = { indices: [0, 1], values: [0.0, 0.34] };
// Execute a hybrid search
await index.query({
topK: 3,
vector: denseQueryVector,
sparseVector: sparseQueryVector,
});
```
### Update a record
You may want to update vector `values`, `sparseValues`, or `metadata`. Specify the id and the attribute value you want to update.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('imdb-movies');
await index.update({
id: '18593',
metadata: { genre: 'romance' },
});
```
### List records
The `listPaginated` method can be used to list record IDs matching a particular ID prefix in a paginated format. With
[clever assignment
of record ids](https://docs.pinecone.io/guides/data/manage-rag-documents#use-id-prefixes), this can be used to help model hierarchical relationships between different records such as when there are embeddings for multiple chunks or fragments related to the same document.
Notes:
- When you do not specify a `prefix`, the default prefix is an empty string, which returns all vector IDs
in your index
- There is a hard limit of `100` vector IDs if no `limit` is specified. Consequently, if there are fewer than `100`
vector IDs that match a given `prefix` in your index, and you do not specify a `limit`, your `paginationToken`
will be `undefined`
The following example shows how to fetch both pages of vector IDs for vectors whose IDs contain the prefix `doc1#`,
assuming a `limit` of `3` and `doc1` document being [chunked](https://www.pinecone.io/learn/chunking-strategies/) into `4` vectors.
```typescript
const pc = new Pinecone();
const index = pc.index('my-index').namespace('my-namespace');
// Fetch the 1st 3 vector IDs matching prefix 'doc1#'
const results = await index.listPaginated({ limit: 3, prefix: 'doc1#' });
console.log(results);
// {
// vectors: [
// { id: 'doc1#01' }
// { id: 'doc1#02' }
// { id: 'doc1#03' }
// ...
// ],
// pagination: {
// next: 'eyJza2lwX3Bhc3QiOiJwcmVUZXN0LS04MCIsInByZWZpeCI6InByZVRlc3QifQ=='
// },
// namespace: 'my-namespace',
// usage: { readUnits: 1 }
// }
// Fetch the final vector ID matching prefix 'doc1#' using the paginationToken returned by the previous call
const nextResults = await index.listPaginated({
prefix: 'doc1#',
paginationToken: results.pagination?.next,
});
console.log(nextResults);
// {
// vectors: [
// { id: 'doc1#04' }
// ],
// pagination: undefined,
// namespace: 'my-namespace',
// usage: { readUnits: 1 }
// }
```
### Fetch records by ID(s)
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
const fetchResult = await index.fetch(['id-1', 'id-2']);
```
### Delete records
For convenience there are several delete-related methods. You can verify the results of a delete operation by trying to `fetch()` a record or looking at the index summary with `describeIndexStats()`
#### Delete one
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
await index.deleteOne('id-to-delete');
```
#### Delete many by ID
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
await index.deleteMany(['id-1', 'id-2', 'id-3']);
```
### Delete many by metadata filter
**Note:** deletion by metadata filter only applies to pod-based indexes.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('albums-database');
await index.deleteMany({ genre: 'rock' });
```
#### Delete all records in a namespace
> ℹ️ **NOTE**
>
> Indexes in the [gcp-starter environment](https://docs.pinecone.io/guides/indexes/convert-a-gcp-starter-index-to-serverless) do not support namespaces.
To nuke everything in the targeted namespace, use the `deleteAll` method.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
await index.namespace('foo-namespace').deleteAll();
```
If you do not specify a namespace, the records in the default namespace `''` will be deleted.
## Inference
Use embedding and & reranking models hosted by Pinecone. Learn more about Inference in the [docs](https://docs.pinecone.io/guides/inference/understanding-inference).
### Models
To see available models you can use the `getModel` and `listModels` methods on the `Inference` class:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const client = new Pinecone({ apiKey: '' });
const models = await pc.inference.listModels();
console.log(models);
// {
// models: [
// {
// model: 'llama-text-embed-v2',
// shortDescription: 'A high performance dense embedding model optimized for multilingual and cross-lingual text question-answering retrieval with support for long documents (up to 2048 tokens) and dynamic embedding size (Matryoshka Embeddings).',
// type: 'embed',
// vectorType: 'dense',
// defaultDimension: 1024,
// modality: 'text',
// maxSequenceLength: 2048,
// maxBatchSize: 96,
// providerName: 'NVIDIA',
// supportedDimensions: [Array],
// supportedMetrics: [Array],
// supportedParameters: [Array]
// },
// ...
// {
// model: 'pinecone-rerank-v0',
// shortDescription: 'A state of the art reranking model that out-performs competitors on widely accepted benchmarks. It can handle chunks up to 512 tokens (1-2 paragraphs)',
// type: 'rerank',
// vectorType: undefined,
// defaultDimension: undefined,
// modality: 'text',
// maxSequenceLength: 512,
// maxBatchSize: 100,
// providerName: 'Pinecone',
// supportedDimensions: undefined,
// supportedMetrics: undefined,
// supportedParameters: [Array]
// }
// ]
// }
const model = await pc.inference.getModel('pinecone-sparse-english-v0');
console.log(model);
// {
// model: 'pinecone-sparse-english-v0',
// shortDescription: 'A sparse embedding model for converting text to sparse vectors for keyword or hybrid semantic/keyword search. Built on the innovations of the DeepImpact architecture.',
// type: 'embed',
// vectorType: 'sparse',
// defaultDimension: undefined,
// modality: 'text',
// maxSequenceLength: 512,
// maxBatchSize: 96,
// providerName: 'Pinecone',
// supportedDimensions: undefined,
// supportedMetrics: [ 'DotProduct' ],
// supportedParameters: [
// {
// parameter: 'input_type',
// type: 'one_of',
// valueType: 'string',
// required: true,
// allowedValues: [Array],
// min: undefined,
// max: undefined,
// _default: undefined
// },
// {
// parameter: 'truncate',
// type: 'one_of',
// valueType: 'string',
// required: false,
// allowedValues: [Array],
// min: undefined,
// max: undefined,
// _default: 'END'
// },
// {
// parameter: 'return_tokens',
// type: 'any',
// valueType: 'boolean',
// required: false,
// allowedValues: undefined,
// min: undefined,
// max: undefined,
// _default: false
// }
// ]
// }
```
### Create embeddings
Generate embeddings for documents and queries.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const client = new Pinecone({ apiKey: '' });
const embeddingModel = 'multilingual-e5-large';
const documents = [
'Turkey is a classic meat to eat at American Thanksgiving.',
'Many people enjoy the beautiful mosques in Turkey.',
];
const docParameters = {
inputType: 'passage',
truncate: 'END',
};
async function generateDocEmbeddings() {
try {
return await client.inference.embed(
embeddingModel,
documents,
docParameters
);
} catch (error) {
console.error('Error generating embeddings:', error);
}
}
generateDocEmbeddings().then((embeddingsResponse) => {
if (embeddingsResponse) {
console.log(embeddingsResponse);
}
});
// << Upsert documents into Pinecone >>
const userQuery = ['How should I prepare my turkey?'];
const queryParameters = {
inputType: 'query',
truncate: 'END',
};
async function generateQueryEmbeddings() {
try {
return await client.inference.embed(
embeddingModel,
userQuery,
queryParameters
);
} catch (error) {
console.error('Error generating embeddings:', error);
}
}
generateQueryEmbeddings().then((embeddingsResponse) => {
if (embeddingsResponse) {
console.log(embeddingsResponse);
}
});
// << Send query to Pinecone to retrieve similar documents >>
```
### Rerank documents
Rerank documents in descending relevance-order against a query.
**Note:** The `score` represents the absolute measure of relevance of a given query and passage pair. Normalized
between [0, 1], the `score` represents how closely relevant a specific item and query are, with scores closer to 1
indicating higher relevance.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const rerankingModel = 'bge-reranker-v2-m3';
const myQuery = 'What are some good Turkey dishes for Thanksgiving?';
// Option 1: Documents as an array of strings
const myDocsStrings = [
'I love turkey sandwiches with pastrami',
'A lemon brined Turkey with apple sausage stuffing is a classic Thanksgiving main',
'My favorite Thanksgiving dish is pumpkin pie',
'Turkey is a great source of protein',
];
// Option 1 response
const response = await pc.inference.rerank(
rerankingModel,
myQuery,
myDocsStrings
);
console.log(response);
// {
// model: 'bge-reranker-v2-m3',
// data: [
// { index: 1, score: 0.5633179, document: [Object] },
// { index: 2, score: 0.02013874, document: [Object] },
// { index: 3, score: 0.00035419367, document: [Object] },
// { index: 0, score: 0.00021485926, document: [Object] }
// ],
// usage: { rerankUnits: 1 }
// }
// Option 2: Documents as an array of objects
const myDocsObjs = [
{
title: 'Turkey Sandwiches',
body: 'I love turkey sandwiches with pastrami',
},
{
title: 'Lemon Turkey',
body: 'A lemon brined Turkey with apple sausage stuffing is a classic Thanksgiving main',
},
{
title: 'Thanksgiving',
body: 'My favorite Thanksgiving dish is pumpkin pie',
},
{
title: 'Protein Sources',
body: 'Turkey is a great source of protein',
},
];
// Option 2: Options object declaring which custom key to rerank on
// Note: If no custom key is passed via `rankFields`, each doc must contain a `text` key, and that will act as the default)
const rerankOptions = {
topN: 3,
returnDocuments: false,
rankFields: ['body'],
parameters: {
inputType: 'passage',
truncate: 'END',
},
};
// Option 2 response
const response = await pc.inference.rerank(
rerankingModel,
myQuery,
myDocsObjs,
rerankOptions
);
console.log(response);
// {
// model: 'bge-reranker-v2-m3',
// data: [
// { index: 1, score: 0.5633179, document: undefined },
// { index: 2, score: 0.02013874, document: undefined },
// { index: 3, score: 0.00035419367, document: undefined },
// ],
// usage: { rerankUnits: 1 }
//}
```
## Integrated Inference
When using an index with integrated inference, embedding and reranking operations are tied to index operations and do not require extra steps. This allows working with an index that accepts source text and converts it to vectors automatically using an embedding model hosted by Pinecone.
Integrated inference requires a serverless index configured for a specific embedding model. You can either create a new index for a model or configure an existing index for a model. See [Create an integrated index](#create-an-integrated-index) for specifics on creating these indexes.
Once you have an index configured for a specific embedding model, use the `upsertRecords` operation on the `Index` class to convert your source data to embeddings and upsert them into a namespace.
### Upsert integrated records
Note the following requirements for each record:
- Each record must contain a unique `id`, which will serve as the record identifier in the index namespace.
- Each record must contain a field with the data for embedding. This field must match the field_map specified when creating the index.
- Any additional fields in the record will be stored in the index and can be returned in search results or used to filter search results.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
// Target an integrated index
const namespace = pc.index('integrated-index').namespace('namespace1');
const records = [
{
id: 'rec1',
chunk_text:
"Apple's first product, the Apple I, was released in 1976 and was hand-built by co-founder Steve Wozniak.",
category: 'product',
},
{
id: 'rec2',
chunk_text:
'Apples are a great source of dietary fiber, which supports digestion and helps maintain a healthy gut.',
category: 'nutrition',
},
{
id: 'rec3',
chunk_text:
'Apples originated in Central Asia and have been cultivated for thousands of years, with over 7,500 varieties available today.',
category: 'cultivation',
},
{
id: 'rec4',
chunk_text:
'In 2001, Apple released the iPod, which transformed the music industry by making portable music widely accessible.',
category: 'product',
},
{
id: 'rec5',
chunk_text:
'Apple went public in 1980, making history with one of the largest IPOs at that time.',
category: 'milestone',
},
{
id: 'rec6',
chunk_text:
'Rich in vitamin C and other antioxidants, apples contribute to immune health and may reduce the risk of chronic diseases.',
category: 'nutrition',
},
{
id: 'rec7',
chunk_text:
"Known for its design-forward products, Apple's branding and market strategy have greatly influenced the technology sector and popularized minimalist design worldwide.",
category: 'influence',
},
{
id: 'rec8',
chunk_text:
'The high fiber content in apples can also help regulate blood sugar levels, making them a favorable snack for people with diabetes.',
category: 'nutrition',
},
];
// Upsert the data into your index
await namespace.upsertRecords(records);
```
### Search integrated records
Use the `searchRecords` method to convert a query to a vector embedding and then search your namespace for the most semantically similar records, along with their similarity scores.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
// Target an integrated index
const namespace = pc.index('integrated-index').namespace('namespace1');
// search for 4 records most semantically relevant to the query 'Disease prevention'
const response = await namespace.searchRecords({
query: { topK: 4, inputs: { text: 'Disease prevention' } },
});
```
To rerank initial search results based on relevance to the query, add the rerank parameter, including the [reranking model](https://docs.pinecone.io/guides/inference/understanding-inference#reranking-models) you want to use, the number of reranked results to return, and the fields to use for reranking, if different than the main query.
For example, repeat the search for the 4 documents most semantically related to the query, “Disease prevention”, but this time rerank the results and return only the 2 most relevant documents:
```typescript
const response = await namespace.searchRecords({
query: {
topK: 4,
inputs: { text: 'Disease prevention' },
},
rerank: {
model: 'bge-reranker-v2-m3',
topN: 2,
rankFields: ['chunk_text'],
},
fields: ['category', 'chunk_text'],
});
```
## Pinecone Assistant
The [Pinecone Assistant API](https://docs.pinecone.io/guides/assistant/understanding-assistant) enables you to create and manage AI assistants powered by Pinecone's vector database
capabilities. These Assistants can be customized with specific instructions and metadata, and can interact with
files and engage in chat conversations.
### Create an Assistant
[Creates a new Assistant](https://docs.pinecone.io/guides/assistant/create-assistant) with specified configurations. You can define the Assistant's name, provide instructions
that guide its behavior, and attach metadata for organization and tracking purposes.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistant = await pc.createAssistant({
name: 'product-assistant',
instructions: 'You are a helpful product recommendation assistant.',
metadata: {
team: 'product',
version: '1.0',
},
});
```
### Delete an Assistant
[Deletes an Assistant](https://docs.pinecone.io/guides/assistant/manage-assistants#delete-an-assistant) by name.
**Note:** Deleting an Assistant also deletes all associated files.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.deleteAssistant('test1');
```
### Get information about an Assistant
[Retrieves information](https://docs.pinecone.io/guides/assistant/manage-assistants#get-the-status-of-an-assistant) about an Assistant by name.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const test = await pc.describeAssistant('test1');
console.log(test);
// {
// name: 'test10',
// instructions: undefined,
// metadata: undefined,
// status: 'Ready',
// host: 'https://prod-1-data.ke.pinecone.io',
// createdAt: 2025-01-08T22:24:50.525Z,
// updatedAt: 2025-01-08T22:24:52.303Z
// }
```
### Update an Assistant
[Updates an Assistant](https://docs.pinecone.io/guides/assistant/manage-assistants#add-instructions-to-an-assistant) by name. You can update the Assistant's name, instructions, and/or metadata.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.updateAssistant('test1', {
instructions: 'some new instructions!',
});
```
### List Assistants
Retrieves a [list of all Assistants](https://docs.pinecone.io/guides/assistant/manage-assistants) in your account. This method returns details about each Assistant including their
names, instructions, metadata, status, and host.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistants = await pc.listAssistants();
console.log(assistants);
// {
// assistants: [{
// name: 'product-assistant',
// instructions: 'You are a helpful product recommendation assistant.',
// metadata: { team: 'product', version: '1.0' },
// status: 'Ready',
// host: 'product-assistant-abc123.svc.pinecone.io'
// }]
// }
```
### Chat with an Assistant
You can [chat with Assistants](https://docs.pinecone.io/guides/assistant/chat-with-assistant) using either the `chat` method or the `chatCompletion` methods.
**Note:** Your Assistant must contain files in order for chat to work.
The following example shows how to chat with an Assistant using the `chat` methods:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const chatResp = await assistant.chat({
messages: [
{
role: 'user',
content: 'What is the capital of France?',
},
],
});
console.log(chatResp);
// {
// id: '000000000000000023e7fb015be9d0ad',
// finishReason: 'stop',
// message: {
// role: 'assistant',
// content: 'The capital of France is Paris.'
// },
// model: 'gpt-4o-2024-05-13',
// citations: [ { position: 209, references: [Array] } ],
// usage: { promptTokens: 493, completionTokens: 38, totalTokens: 531 }
// }
```
`chatCompletion` is based on the [OpenAI Chat Completion](https://platform.openai.com/docs/api-reference/chat) format, and is useful if OpenAI-compatible responses. However, it has limited functionality compared to the standard `chat` method. Read more [here](https://docs.pinecone.io/reference/api/2025-01/assistant/chat_completion_assistant).
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const chatResp = await assistant.chatCompletion({
messages: [
{
role: 'user',
content: 'What is the capital of France?',
},
],
});
console.log(chatResp);
// {
// id: '000000000000000023e7fb015be9d0ad',
// choices: [
// {
// finishReason: 'stop',
// index: 0,
// message: {
// role: 'assistant',
// content: 'The capital of France is Paris.'
// }
// }
// ],
// finishReason: 'stop',
// model: 'gpt-4o-2024-05-13',
// usage: { promptTokens: 493, completionTokens: 38, totalTokens: 531 }
// }
```
### Stream Assistant responses
Assistant chat responses can also be streamed using the `chatStream` and `chatCompletionStream` methods on the `Assistant` class. These methods return a `ChatStream` which implements `AsyncIterable`, returning an [async iterator](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/AsyncIterator) object allowing for manipulation of the stream. You can stream either the chat or chat completions operations.
Note: The shape of the JSON returned in each streamed chunk will be different depending which method is being used.
Chat stream:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const chatStream = await assistant.chatStream({
messages: [
{
role: 'user',
content: 'What is the capital of France?',
},
],
});
for await (const chunk of chatStream) {
console.log(chunk);
}
// Each chunk in the stream will have a different shape depending on the type:
//
// {
// type: 'message_start',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// role: 'assistant'
// }
// {
// type: 'content_chunk',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// delta: { content: 'The' }
// }
// {
// type: 'content_chunk',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// delta: { content: ' capital' }
// }
// {
// type: 'content_chunk',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// delta: { content: ' of' }
// }
// {
// type: 'content_chunk',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// delta: { content: ' France' }
// }
// {
// type: 'content_chunk',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// delta: { content: ' is Paris.' }
// }
// {
// type: 'citation',
// id: 'response_id',
// model: 'gpt-4o-2024-05-13',
// citation: { position: 1538, references: [ [Object] ] }
// }
// {
// type: 'message_end',
// id: '000000000000000002378669324ef087',
// model: 'gpt-4o-2024-05-13',
// finishReason: 'stop',
// usage: { promptTokens: 9080, completionTokens: 312, totalTokens: 9392 }
// }
```
Chat completion stream:
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const chatCompletionStream = await assistant.chatCompletionStream({
messages: [
{
role: 'user',
content: 'What is the capital of France?',
},
],
});
for await (const chunk of chatCompletionStream) {
console.log(chunk);
}
// Each chunk will have the same OpenAI compatible completion shape:
//
// {
// id: 'response-id',
// choices: [
// {
// index: 0,
// delta: {
// role: 'assistant'
// },
// finishReason: null
// }
// ],
// model: 'gpt-4o-2024-05-13',
// usage: null
// }
// {
// id: 'response-id',
// choices: [
// {
// index: 0,
// delta: {
// content: 'The capital'
// },
// finishReason: null
// }
// ],
// model: 'gpt-4o-2024-05-13',
// usage: null
// }
// ... rest of stream
// {
// id: 'response-id',
// choices: [],
// model: 'gpt-4o-2024-05-13',
// usage: {
// promptTokens: 9080,
// completionTokens: 338,
// totalTokens: 9418
// }
// }
```
### Inspect context snippets associated with a chat
Returns [context snippets associated with a given query and an Assistant's response](https://docs.pinecone.io/guides/assistant/understanding-context-snippets). This is useful for understanding
how the Assistant arrived at its answer(s).
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const context = await assistant.context({
messages: ['What is the capital of France?'],
topK: 1,
});
console.log(context);
// {
// snippets: [
// {
// type: 'text',
// content: 'The capital of France is Paris.',
// score: 0.9978925,
// reference: [Object]
// },
// ],
// usage: { promptTokens: 527, completionTokens: 0, totalTokens: 527 }
// }
```
### Add files to an Assistant
You can [add files to an Assistant](https://docs.pinecone.io/guides/assistant/manage-files#upload-a-local-file) to enable it to interact with files during chat conversations. The following
example shows how to upload a local `test-file.txt` file to an Assistant.
**Note:** You must upload at least 1 file in order to chat with an Assistant.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
await assistant.uploadFile({
path: 'test-file.txt',
metadata: { 'test-key': 'test-value' },
});
// {
// name: 'test-file.txt',
// id: '921ad74c-2421-413a-8c86-fca81ceabc5c',
// metadata: { 'test-key': 'test-value' },
// createdOn: 2025-01-06T19:14:21.969Z,
// updatedOn: 2025-01-06T19:14:21.969Z,
// status: 'Processing',
// percentDone: null,
// signedUrl: null,
// errorMessage: null
// }
```
### List all files in an Assistant
[Lists all files](https://docs.pinecone.io/guides/assistant/manage-files#list-files-in-an-assistant) that have been uploaded to an Assistant. Optionally, you can pass a filter to list only files that
meet certain criteria.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const files = await assistant.listFiles({
filter: { metadata: { key: 'value' } },
});
console.log(files);
// {
// files: [
// {
// name: 'test-file.txt',
// id: '1a56ddd0-c6d8-4295-80c0-9bfd6f5cb87b',
// metadata: [Object],
// createdOn: 2025-01-06T19:14:21.969Z,
// updatedOn: 2025-01-06T19:14:36.925Z,
// status: 'Available',
// percentDone: 1,
// signedUrl: undefined,
// errorMessage: undefined
// }
// ]
// }
```
### Get the status of a file in an Assistant
[Retrieves information about a file](https://docs.pinecone.io/guides/assistant/manage-files#get-the-status-of-a-file) in an Assistant by ID.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const files = await assistant.listFiles();
let fileId: string;
if (files.files) {
fileId = files.files[0].id;
} else {
fileId = '';
}
const resp = await assistant.describeFile({ fileId: fileId });
console.log(resp);
// {
// name: 'test-file.txt',
// id: '1a56ddd0-c6d8-4295-80c0-9bfd6f5cb87b',
// metadata: undefined,
// createdOn: 2025-01-06T19:14:21.969Z,
// updatedOn: 2025-01-06T19:14:36.925Z,
// status: 'Available',
// percentDone: 1,
// signedUrl: undefined,
// errorMessage: undefined
// }
```
### Delete a file from an Assistant
[Deletes a file(s)](https://docs.pinecone.io/guides/assistant/manage-files#delete-a-file) from an Assistant by ID.
**Note:** Deleting files is a PERMANENT operation. Deleted files _cannot_ be recovered.
```typescript
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const assistantName = 'test1';
const assistant = pc.Assistant(assistantName);
const files = await assistant.listFiles();
let fileId: string;
if (files.files) {
fileId = files.files[0].id;
await assistant.deleteFile({ fileId: fileId });
}
```
## Testing
All testing takes place automatically in CI and is configured using Github actions
and workflows, located in the `.github` directory of this repo.
See [CONTRIBUTING.md](CONTRIBUTING.md) for more information.