DocsReferenceRAGDefaultVectorDB

DefaultVectorDB Store

The DefaultVectorDB class provides vector search using LibSQL, a fork of SQLite with vector extensions, and Turso with vector extensions, offering a lightweight and efficient vector database solution. It’s part of the @mastra/core package and offers efficient vector similarity search with metadata filtering.

Installation

Default vector store is included in the core package:

npm install @mastra/core

Usage

import { DefaultVectorDB } from '@mastra/core/storage';
 
// Create a new vector store instance
const store = new DefaultVectorDB({
  connectionUrl: process.env.DATABASE_URL,
  // Optional: for Turso cloud databases
  authToken: process.env.DATABASE_AUTH_TOKEN,
});
 
// Create an index
await store.createIndex("my-collection", 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("my-collection", vectors, metadata);
 
// Query similar vectors
const queryVector = [0.1, 0.2, ...];
const results = await store.query(
  "my-collection",
  queryVector,
  10, // top K results
  { category: "A" } // optional metadata filter
);

Constructor Options

connectionUrl:

string
LibSQL database URL. Use ':memory:' for in-memory database, 'file:local.db' for local file, or a LibSQL-compatible connection string like 'libsql://your-database.turso.io'.

authToken?:

string
Authentication token for Turso cloud databases

syncUrl?:

string
URL for database replication (Turso specific)

syncInterval?:

number
Interval in milliseconds for database sync (Turso specific)

Methods

createIndex()

Creates a new vector collection. The index name must start with a letter or underscore and can only contain letters, numbers, and underscores. The dimension must be a positive integer.

indexName:

string
Name of the index to create

dimension:

number
Vector dimension size (must match your embedding model)

metric?:

'cosine' | 'euclidean' | 'dotproduct'
= cosine
Distance metric for similarity search. Note: Currently only cosine similarity is supported by LibSQL.

upsert()

Adds or updates vectors and their metadata in the index. Uses a transaction to ensure all vectors are inserted atomically - if any insert fails, the entire operation is rolled back.

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?:

Filter
Metadata filters

includeVector?:

boolean
= false
Whether to include vector data in results

minScore?:

number
= 0
Minimum similarity score threshold

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 in the database.

Returns: Promise<string[]>

truncateIndex()

Removes all vectors from an index while keeping the index structure.

indexName:

string
Name of the index to truncate

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 specific errors for different failure cases:

try {
  await store.query("my-collection", queryVector);
} catch (error) {
  // Handle specific error cases
  if (error.message.includes("Invalid index name format")) {
    console.error(
      "Index name must start with a letter/underscore and contain only alphanumeric characters",
    );
  } else if (error.message.includes("Table not found")) {
    console.error("The specified index does not exist");
  } else {
    console.error("Vector store error:", error.message);
  }
}

Common error cases include:

  • Invalid index name format
  • Invalid vector dimensions
  • Table/index not found
  • Database connection issues
  • Transaction failures during upsert

Related

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