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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 install @mastra/mongodb@beta

Usage Example​

import { MongoDBVector } from "@mastra/mongodb";

const store = new MongoDBVector({
  id: 'mongodb-vector',
  url: process.env.MONGODB_URL,
  database: process.env.MONGODB_DATABASE,
});

Constructor Options​

id:

string
Unique identifier for this vector store instance

url:

string
MongoDB connection string (URI)

database:

string
Name of the MongoDB database to use

options?:

MongoClientOptions
Optional MongoDB client options

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.