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# libSQL vector store

The libSQL storage implementation provides a SQLite-compatible vector search [libSQL](https://github.com/tursodatabase/libsql), a fork of SQLite with vector extensions, and [Turso](https://turso.tech/) with vector extensions, offering a lightweight and efficient vector database solution. It's part of the `@mastra/libsql` package and offers efficient vector similarity search with metadata filtering.

## Installation

**npm**:

```bash
npm install @mastra/libsql@latest
```

**pnpm**:

```bash
pnpm add @mastra/libsql@latest
```

**Yarn**:

```bash
yarn add @mastra/libsql@latest
```

**Bun**:

```bash
bun add @mastra/libsql@latest
```

## Usage

```typescript
import { LibSQLVector } from "@mastra/libsql";

// Create a new vector store instance
const store = new LibSQLVector({
  id: 'libsql-vector',
  url: process.env.DATABASE_URL,
  // Optional: for Turso cloud databases
  authToken: process.env.DATABASE_AUTH_TOKEN,
});

// Create an index
await store.createIndex({
  indexName: "myCollection",
  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: "myCollection",
  vectors,
  metadata,
});

// Query similar vectors
const queryVector = [0.1, 0.2, ...];
const results = await store.query({
  indexName: "myCollection",
  queryVector,
  topK: 10, // top K results
  filter: { category: "A" } // optional metadata filter
});
```

## Constructor options

**url** (`string`): libSQL database URL. Use ':memory:' for in-memory database, 'file:dbname.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'`): Distance metric for similarity search. Note: Currently only cosine similarity is supported by libSQL. (Default: `cosine`)

### `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`): Number of results to return (Default: `10`)

**filter** (`Filter`): Metadata filters

**includeVector** (`boolean`): Whether to include vector data in results (Default: `false`)

**minScore** (`number`): Minimum similarity score threshold (Default: `0`)

### `describeIndex()`

Gets information about an index.

**indexName** (`string`): Name of the index to describe

Returns:

```typescript
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

### `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 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 index 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 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:

```typescript
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:

```typescript
try {
  await store.query({
    indexName: 'my-collection',
    queryVector: 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

## Usage example

### Local embeddings with fastembed

Embeddings are numeric vectors used by memory's `semanticRecall` to retrieve related messages by meaning (not keywords). This setup uses `@mastra/fastembed` to generate vector embeddings.

Install `fastembed` to get started:

**npm**:

```bash
npm install @mastra/fastembed@latest
```

**pnpm**:

```bash
pnpm add @mastra/fastembed@latest
```

**Yarn**:

```bash
yarn add @mastra/fastembed@latest
```

**Bun**:

```bash
bun add @mastra/fastembed@latest
```

Add the following to your agent:

```typescript
import { Memory } from '@mastra/memory'
import { Agent } from '@mastra/core/agent'
import { LibSQLStore, LibSQLVector } from '@mastra/libsql'
import { fastembed } from '@mastra/fastembed'

export const libsqlAgent = new Agent({
  id: 'libsql-agent',
  name: 'libSQL Agent',
  instructions:
    'You are an AI agent with the ability to automatically recall memories from previous interactions.',
  model: 'openai/gpt-5.5',
  memory: new Memory({
    storage: new LibSQLStore({
      id: 'libsql-agent-storage',
      url: 'file:libsql-agent.db',
    }),
    vector: new LibSQLVector({
      id: 'libsql-agent-vector',
      url: 'file:libsql-agent.db',
    }),
    embedder: fastembed,
    options: {
      lastMessages: 10,
      semanticRecall: {
        topK: 3,
        messageRange: 2,
      },
      generateTitle: true, // Explicitly enable automatic title generation
    },
  }),
})
```

## Related

- [Metadata Filters](https://mastra.ai/reference/rag/metadata-filters)