This is the official Node.js SDK for Pinecone, written in TypeScript.
- Reference Documentation
- If you are upgrading from
v0.x
, check out the v1 Migration Guide. - If you are upgrading from
v1.x
, check out the v2 Migration Guide.
The snippets shown in this README are intended to be concise. For more realistic examples, explore these examples:
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 for an example of the code.
- 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. Serverless indexes are only available in2.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 a0.x
version. See the v1 Migration Guide for details.
The Pinecone TypeScript SDK is compatible with TypeScript >=4.1 and Node >=18.x.
npm install @pinecone-database/pinecone
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.
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.
The environment variable used to configure the API key for the client is the following:
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.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
If you prefer to pass configuration in code, the constructor accepts a config object containing the apiKey
value.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone({
apiKey: 'your_api_key',
});
If your network setup requires you to interact with Pinecone via a proxy, you can pass a custom ProxyAgent
from
the undici
library. Below is an example of how to
construct an undici
ProxyAgent
that routes network traffic through a mitm
proxy server 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. 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+.
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: '<your proxy server URI>',
requestTls: {
port: '<your proxy server port>',
ca: cert,
host: '<your proxy server 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:
'<your Pinecone API key, available in your dashboard at app.pinecone.io>',
fetchApi: customFetch,
};
const pc = new Pinecone(config);
const indexes = async () => {
return await pc.listIndexes();
};
indexes().then((response) => {
console.log('My indexes: ', response);
});
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 model, you would need to create an index with dimension 1536
to match the output of that model.
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.
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',
},
},
});
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 and Scale pod-based indexes.
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'],
},
},
},
// 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,
});
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.
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'
// }
// }
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.
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,
});
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 provide an easy way to do this. See other client methods for working with collections here.
Given that you have an existing collection:
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.
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.
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
// }
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
.
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.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.configureIndex('deletion-protected-index', {
deletionProtection: 'disabled',
});
You can fetch the description of any index by name using describeIndex
.
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'
// }
// }
ℹ️ Note
This section applies to 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. You cannot downgrade pod size or change the base pod type.
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'
// }
// }
Indexes are deleted by name.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.deleteIndex('sample-index');
The listIndexes
command returns an object with an array of index models under indexes
.
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',
// },
// },
// ],
// }
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.
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.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
await pc.deleteCollection('collection-name');
You can use listCollections
to confirm the deletion.
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',
// }
The listCollections
command returns an object with an array of collection models under collections
.
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',
// },
// ];
// }
Pinecone indexes support operations for working with vector data using operations such as upsert, query, fetch, and delete.
To perform data operations on an index, you target it using the index
method.
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, or by using the describeIndex
or listIndexes
operations.
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']);
If you are storing metadata alongside your vector values, you can pass a type parameter to index()
in order to get proper TypeScript typechecking.
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<MovieMetadata>('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}`)
}
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()
.
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 for more information.
Pinecone expects records inserted into indexes to have the following form:
type PineconeRecord = {
id: string;
values: Array<number>;
sparseValues?: Array<number>;
metadata?: object;
};
To upsert some vectors, you can use the client like so:
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);
You can now import vectors en masse 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. Your object storage must also adhere to the necessary directory structure.
The following example imports vectors from an Amazon S3 bucket into a Pinecone serverless index:
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 of all or one import operation(s).
Notes:
Import
only works with Serverless indexesImport
is in public preview- The only object storage provider currently supported is 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 for further information
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.
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
// }
The query method accepts a large number of options. The dimension of the query vector must match the dimension of your index.
type QueryOptions = {
topK: number; // number of results desired
vector?: Array<number>; // must match dimension of index
sparseVector?: {
indices: Array<integer>; // indices must fall within index dimension
values: Array<number>; // 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 to see more realistic examples of how to use query
.
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.
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] });
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.
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' });
If you are working with sparse-dense vectors, you can add sparse vector values to perform a hybrid search.
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,
});
You may want to update vector values
, sparseValues
, or metadata
. Specify the id and the attribute value you want to update.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('imdb-movies');
await index.update({
id: '18593',
metadata: { genre: 'romance' },
});
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, 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.
const pc = new Pinecone();
const index = pc.index('my-index').namespace('my-namespace');
const results = await index.listPaginated({ prefix: 'doc1#' });
console.log(results);
// {
// vectors: [
// { id: 'doc1#01' }, { id: 'doc1#02' }, { id: 'doc1#03' },
// { id: 'doc1#04' }, { id: 'doc1#05' }, { id: 'doc1#06' },
// { id: 'doc1#07' }, { id: 'doc1#08' }, { id: 'doc1#09' },
// ...
// ],
// pagination: {
// next: 'eyJza2lwX3Bhc3QiOiJwcmVUZXN0LS04MCIsInByZWZpeCI6InByZVRlc3QifQ=='
// },
// namespace: 'my-namespace',
// usage: { readUnits: 1 }
// }
// Fetch the next page of results
await index.listPaginated({
prefix: 'doc1#',
paginationToken: results.pagination?.next,
});
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']);
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()
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('my-index');
await index.deleteOne('id-to-delete');
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']);
Note: deletion by metadata filter only applies to pod-based indexes.
import { Pinecone } from '@pinecone-database/pinecone';
const pc = new Pinecone();
const index = pc.index('albums-database');
await index.deleteMany({ genre: 'rock' });
ℹ️ NOTE
Indexes in the gcp-starter environment do not support namespaces.
To nuke everything in the targeted namespace, use the deleteAll
method.
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.
Interact with Pinecone's Inference API (currently in public preview). The Pinecone Inference API is a service that gives you access to inference models hosted on Pinecone's infrastructure.
Notes:
Supported models:
- Embedding: multilingual-e5-large
- Reranking: bge-reranker-v2-m3
Send text to Pinecone's Inference API to generate embeddings for documents and queries.
import { Pinecone } from '@pinecone-database/pinecone';
const client = new Pinecone({ apiKey: '<Your API key from app.pinecone.io>' });
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 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.
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 }
//}
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 for more information.