MongoDB vector store

The MongoDBVector class provides vector search using MongoDB Atlas Vector Search. It enables efficient similarity search and metadata filtering within your MongoDB collections.

Installation

npm

npm install @mastra/mongodb@latest

pnpm

pnpm add @mastra/mongodb@latest

Yarn

yarn add @mastra/mongodb@latest

Bun

bun add @mastra/mongodb@latest

Usage example

import { MongoDBVector } from '@mastra/mongodb'

const store = new MongoDBVector({
  id: 'mongodb-vector',
  uri: process.env.MONGODB_URI,
  dbName: process.env.MONGODB_DATABASE,
})

Custom Embedding Field Path

If you need to store embeddings in a nested field structure (e.g., to integrate with existing MongoDB collections), use the embeddingFieldPath option:

import { MongoDBVector } from '@mastra/mongodb'

const store = new MongoDBVector({
  id: 'mongodb-vector',
  uri: process.env.MONGODB_URI,
  dbName: process.env.MONGODB_DATABASE,
  embeddingFieldPath: 'text.contentEmbedding', // Store embeddings at text.contentEmbedding
})

Constructor options

id:

string

Unique identifier for this vector store instance

uri:

string

MongoDB connection string

dbName:

string

Name of the MongoDB database to use

options?:

MongoClientOptions

Optional MongoDB client options

embeddingFieldPath?:

string

= embedding

Path to the field that stores vector embeddings. Supports nested paths using dot notation (e.g., 'text.contentEmbedding').

Methods

createIndex()

Creates a new vector index (collection) in MongoDB.

indexName:

string

Name of the collection 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 collection.

indexName:

string

Name of the collection 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 collection 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 (applies to the `metadata` field)

documentFilter?:

Record<string, any>

Filters on original document fields (not just metadata)

includeVector?:

boolean

= false

Whether to include vector data in results

minScore?:

number

= 0

Minimum similarity score threshold

describeIndex()

Returns information about the index (collection).

indexName:

string

Name of the collection to describe

Returns:

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

deleteIndex()

Deletes a collection and all its data.

indexName:

string

Name of the collection to delete

listIndexes()

Lists all vector collections in the MongoDB database.

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 collection containing the vector

id?:

string

ID of the vector entry 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 to update

update.metadata?:

Record<string, any>

New metadata to update

deleteVector()

Deletes a specific vector entry from an index by its ID.

indexName:

string

Name of the collection containing the vector

id:

string

ID of the vector entry 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 collection 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)

disconnect()

Closes the MongoDB client connection. Should be called when done using the store.

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
}

Error handling

The store throws typed errors that can be caught:

try {
  await store.query({
    indexName: 'my_collection',
    queryVector: queryVector,
  })
} catch (error) {
  // Handle specific error cases
  if (error.message.includes('Invalid collection name')) {
    console.error(
      'Collection name must start with a letter or underscore and contain only valid characters.',
    )
  } else if (error.message.includes('Collection not found')) {
    console.error('The specified collection does not exist')
  } else {
    console.error('Vector store error:', error.message)
  }
}

Best practices

• Index metadata fields used in filters for optimal query performance.
• Use consistent field naming in metadata to avoid unexpected query results.
• Regularly monitor index and collection statistics to ensure efficient search.

Usage example

Vector embeddings with MongoDB

Embeddings are numeric vectors used by memory's semanticRecall to retrieve related messages by meaning (not keywords).

note

You must use a deployment hosted on MongoDB Atlas to successfully use the MongoDB Vector database.

This setup uses FastEmbed, a local embedding model, to generate vector embeddings. To use this, install @mastra/fastembed:

npm

npm install @mastra/fastembed@latest

pnpm

pnpm add @mastra/fastembed@latest

Yarn

yarn add @mastra/fastembed@latest

Bun

bun add @mastra/fastembed@latest

Add the following to your agent:

src/mastra/agents/example-mongodb-agent.ts

import { Memory } from '@mastra/memory'
import { Agent } from '@mastra/core/agent'
import { MongoDBStore, MongoDBVector } from '@mastra/mongodb'
import { fastembed } from '@mastra/fastembed'

export const mongodbAgent = new Agent({
  id: 'mongodb-agent',
  name: 'mongodb-agent',
  instructions:
    'You are an AI agent with the ability to automatically recall memories from previous interactions.',
  model: 'openai/gpt-5.4',
  memory: new Memory({
    storage: new MongoDBStore({
      id: 'mongodb-storage',
      uri: process.env.MONGODB_URI!,
      dbName: process.env.MONGODB_DB_NAME!,
    }),
    vector: new MongoDBVector({
      id: 'mongodb-vector',
      uri: process.env.MONGODB_URI!,
      dbName: process.env.MONGODB_DB_NAME!,
    }),
    embedder: fastembed,
    options: {
      lastMessages: 10,
      semanticRecall: {
        topK: 3,
        messageRange: 2,
      },
      generateTitle: true, // generates descriptive thread titles automatically
    },
  }),
})

Related

• Metadata Filters