Mastra LanceDB Storage & Vector Usage Guide

This guide explains how to use LanceDB as both a storage backend and vector database with Mastra. LanceDB provides a high-performance, open-source, and embeddable vector database built on Lance file format.

Installation

pnpm add @mastra/lance @lancedb/lancedb apache-arrow

Setup & Configuration

Basic Setup

import { LanceStorage } from '@mastra/lance';
import { Mastra } from '@mastra/core/mastra';

// Initialize LanceStorage
const storage = await LanceStorage.create(
  'myApp', // Name for your storage instance
  'path/to/db', // Path to database directory
);

// Configure Mastra with LanceStorage
const mastra = new Mastra({
  storage: storage,
});

Connection Options

LanceStorage supports multiple connection configurations:

// Local database
const localStore = await LanceStorage.create('myApp', '/path/to/db');

// LanceDB Cloud
const cloudStore = await LanceStorage.create('myApp', 'db://host:port');

// S3 bucket
const s3Store = await LanceStorage.create('myApp', 's3://bucket/db', { storageOptions: { timeout: '60s' } });

Basic Operations

Creating Tables

import { TABLE_MESSAGES } from '@mastra/core/storage';
import type { StorageColumn } from '@mastra/core/storage';

// Define schema with appropriate types
const schema: Record<string, StorageColumn> = {
  id: { type: 'uuid', nullable: false },
  threadId: { type: 'uuid', nullable: false },
  content: { type: 'text', nullable: true },
  createdAt: { type: 'timestamp', nullable: false },
  metadata: { type: 'jsonb', nullable: true },
};

// Create table
await storage.createTable({
  tableName: TABLE_MESSAGES,
  schema,
});

Inserting Records

// Insert a single record
await storage.insert({
  tableName: TABLE_MESSAGES,
  record: {
    id: '123e4567-e89b-12d3-a456-426614174000',
    threadId: '123e4567-e89b-12d3-a456-426614174001',
    content: 'Hello, world!',
    createdAt: new Date(),
    metadata: { tags: ['important', 'greeting'] },
  },
});

// Batch insert multiple records
await storage.batchInsert({
  tableName: TABLE_MESSAGES,
  records: [
    {
      id: '123e4567-e89b-12d3-a456-426614174002',
      threadId: '123e4567-e89b-12d3-a456-426614174001',
      content: 'First message',
      createdAt: new Date(),
      metadata: { priority: 'high' },
    },
    {
      id: '123e4567-e89b-12d3-a456-426614174003',
      threadId: '123e4567-e89b-12d3-a456-426614174001',
      content: 'Second message',
      createdAt: new Date(),
      metadata: { priority: 'low' },
    },
  ],
});

Querying Data

// Load a record by id
const message = await storage.load({
  tableName: TABLE_MESSAGES,
  keys: { id: '123e4567-e89b-12d3-a456-426614174000' },
});

// Load messages from a thread
const messages = await storage.getMessages({
  threadId: '123e4567-e89b-12d3-a456-426614174001',
});

Working with Threads & Messages

Creating Threads

import type { StorageThreadType } from '@mastra/core';

// Create a new thread
const thread: StorageThreadType = {
  id: '123e4567-e89b-12d3-a456-426614174010',
  resourceId: 'resource-123',
  title: 'New Discussion',
  createdAt: new Date(),
  metadata: { topic: 'technical-support' },
};

// Save the thread
const savedThread = await storage.saveThread({ thread });

Working with Messages

import type { MessageType } from '@mastra/core';

// Create messages
const messages: MessageType[] = [
  {
    id: 'msg-001',
    threadId: '123e4567-e89b-12d3-a456-426614174010',
    resourceId: 'resource-123',
    role: 'user',
    content: 'How can I use LanceDB with Mastra?',
    createdAt: new Date(),
    type: 'text',
    toolCallIds: [],
    toolCallArgs: [],
    toolNames: [],
  },
  {
    id: 'msg-002',
    threadId: '123e4567-e89b-12d3-a456-426614174010',
    resourceId: 'resource-123',
    role: 'assistant',
    content: 'You can use LanceDB with Mastra by installing @mastra/lance package.',
    createdAt: new Date(),
    type: 'text',
    toolCallIds: [],
    toolCallArgs: [],
    toolNames: [],
  },
];

// Save messages
await storage.saveMessages({ messages });

// Retrieve messages with context
const retrievedMessages = await storage.getMessages({
  threadId: '123e4567-e89b-12d3-a456-426614174010',
  selectBy: [
    {
      id: 'msg-001',
      withPreviousMessages: 5, // Include up to 5 messages before this one
      withNextMessages: 5, // Include up to 5 messages after this one
    },
  ],
});

Working with Workflows

Mastra's workflow system uses LanceDB to persist workflow state for continuity across runs:

import type { WorkflowRunState } from '@mastra/core';

// Persist a workflow snapshot
await storage.persistWorkflowSnapshot({
  workflowName: 'documentProcessing',
  runId: 'run-123',
  snapshot: {
    context: {
      steps: {
        step1: { status: 'success', payload: { data: 'processed' } },
        step2: { status: 'waiting' },
      },
      triggerData: { documentId: 'doc-123' },
      attempts: { step1: 1, step2: 0 },
    },
  } as WorkflowRunState,
});

// Load a workflow snapshot
const workflowState = await storage.loadWorkflowSnapshot({
  workflowName: 'documentProcessing',
  runId: 'run-123',
});

Using Lance for Vector Storage

The LanceDB integration in Mastra can be used for both traditional storage and vector search:

// Create a schema with vector field
const vectorSchema: Record<string, StorageColumn> = {
  id: { type: 'uuid', nullable: false },
  content: { type: 'text', nullable: true },
  embedding: { type: 'binary', nullable: false }, // Vector embedding
  metadata: { type: 'jsonb', nullable: true },
};

// Create a vector table
await storage.createTable({
  tableName: 'vector_store',
  schema: vectorSchema,
});

// Insert a vector with content and metadata
await storage.insert({
  tableName: 'vector_store',
  record: {
    id: 'vec-001',
    content: 'This is a document about LanceDB and Mastra integration',
    embedding: new Float32Array([0.1, 0.2, 0.3, 0.4]), // Your embedding vector
    metadata: { source: 'documentation', category: 'integration' },
  },
});

Data Management

// Drop a table
await storage.dropTable(TABLE_MESSAGES);

// Clear all records from a table
await storage.clearTable({ tableName: TABLE_MESSAGES });

// Get table schema
const schema = await storage.getTableSchema(TABLE_MESSAGES);