Elasticsearch vector store

The ElasticSearchVector class provides vector search using Elasticsearch with its dense_vector field type and k-NN search capabilities. It's part of the @mastra/elasticsearch package.

Installation

npm

npm install @mastra/elasticsearch@latest

pnpm

pnpm add @mastra/elasticsearch@latest

Yarn

yarn add @mastra/elasticsearch@latest

Bun

bun add @mastra/elasticsearch@latest

Usage

import { ElasticSearchVector } from "@mastra/elasticsearch";

const store = new ElasticSearchVector({
  id: "elasticsearch-vector",
  url: process.env.ELASTICSEARCH_URL,
});

// Create an index
await store.createIndex({
  indexName: "my-collection",
  dimension: 1536,
});

// Add vectors with metadata
const vectors = [[0.1, 0.2, ...], [0.3, 0.4, ...]];
const metadata = [
  { text: "first document", category: "A" },
  { text: "second document", category: "B" }
];
await store.upsert({
  indexName: "my-collection",
  vectors,
  metadata,
});

// Query similar vectors
const results = await store.query({
  indexName: "my-collection",
  queryVector: [0.1, 0.2, ...],
  topK: 10,
  filter: { category: "A" },
});

Constructor options

id:

string

Unique identifier for this vector store instance

url:

string

Elasticsearch connection URL (e.g., 'http://localhost:9200')

Methods

createIndex()

Creates a new index with the specified configuration.

indexName:

string

Name of the index to create

dimension:

number

Vector dimension (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'

= cosine

Distance metric for similarity search

upsert()

Adds or updates vectors and their metadata in the index.

indexName:

string

Name of the index to insert into

vectors:

number[][]

Array of embedding vectors

metadata?:

Record<string, any>[]

Metadata for each vector

ids?:

string[]

Optional vector IDs (auto-generated if not provided)

query()

Searches for similar vectors with optional metadata filtering.

indexName:

string

Name of the index to search in

queryVector:

number[]

Query vector to find similar vectors for

topK?:

number

= 10

Number of results to return

filter?:

Record<string, any>

Metadata filters

includeVector?:

boolean

= false

Whether to include vector data in results

describeIndex()

Gets information about an index.

indexName:

string

Name of the index to describe

Returns:

interface IndexStats {
  dimension: number
  count: number
  metric: 'cosine' | 'euclidean' | 'dotproduct'
}

deleteIndex()

Deletes an index and all its data.

indexName:

string

Name of the index to delete

listIndexes()

Lists all vector indexes.

Returns: Promise<string[]>

updateVector()

Update a single vector by ID or by metadata filter. Either id or filter must be provided, but not both.

indexName:

string

Name of the index containing the vector

id?:

string

ID of the vector to update (mutually exclusive with filter)

filter?:

Record<string, any>

Metadata filter to identify vector(s) to update (mutually exclusive with id)

update:

object

Update data containing vector and/or metadata

update.vector?:

number[]

New vector data

update.metadata?:

Record<string, any>

New metadata

deleteVector()

Deletes a single vector by its ID.

indexName:

string

Name of the index containing the vector

id:

string

ID of the vector to delete

deleteVectors()

Delete multiple vectors by IDs or by metadata filter. Either ids or filter must be provided, but not both.

indexName:

string

Name of the index containing the vectors to delete

ids?:

string[]

Array of vector IDs to delete (mutually exclusive with filter)

filter?:

Record<string, any>

Metadata filter to identify vectors to delete (mutually exclusive with ids)

Response types

Query results are returned in this format:

interface QueryResult {
  id: string
  score: number
  metadata: Record<string, any>
  vector?: number[] // Only included if includeVector is true
}

Related

• Metadata Filters