@mastra/vectorize

@mastra/vectorize

Vector store implementation for Vectorize, a managed vector database service optimized for AI applications.

Installation

npm install @mastra/vectorize

Usage

import { VectorizeStore } from '@mastra/vectorize';

const vectorStore = new VectorizeStore({
  apiKey: process.env.VECTORIZE_API_KEY,
  projectId: process.env.VECTORIZE_PROJECT_ID
});

// Create a new index
await vectorStore.createIndex({
  indexName: 'my-index',
  dimension: 1536,
  metric: 'cosine'
});

// Add vectors
const vectors = [[0.1, 0.2, ...], [0.3, 0.4, ...]];
const metadata = [{ text: 'doc1' }, { text: 'doc2' }];
const ids = await vectorStore.upsert({
  indexName: 'my-index',
  vectors,
  metadata
});

// Query vectors
const results = await vectorStore.query({
  indexName: 'my-index',
  queryVector: [0.1, 0.2, ...],
  topK: 10,
  filter: { text: { $eq: 'doc1' } },
  includeVector: false
});

Configuration

The Vectorize vector store requires the following configuration:

• VECTORIZE_API_KEY: Your Vectorize API key
• VECTORIZE_INDEX_NAME: Name of the index to use
• VECTORIZE_PROJECT_ID: Your Vectorize project ID

Features

• Purpose-built for AI and ML workloads
• High-performance vector similarity search
• Automatic index optimization
• Scalable architecture
• Real-time updates and queries

Methods

• createIndex({ indexName, dimension, metric? }): Create a new index
• upsert({ indexName, vectors, metadata?, ids? }): Add or update vectors
• query({ indexName, queryVector, topK?, filter?, includeVector? }): Search for similar vectors
• listIndexes(): List all indexes
• describeIndex(indexName): Get index statistics
• deleteIndex(indexName): Delete an index

Related Links

• Vectorize Documentation