Hybrid Vector Search

When you combine vector similarity search with metadata filters, you can create a hybrid search that is more precise and efficient. This approach combines:

• Vector similarity search to find the most relevant documents
• Metadata filters to refine the search results based on additional criteria

This example demonstrates how to use hybrid vector search with Mastra and PGVector.

Overview

The system implements filtered vector search using Mastra and PGVector. Here’s what it does:

1. Queries existing embeddings in PGVector with metadata filters
2. Shows how to filter by different metadata fields
3. Demonstrates combining vector similarity with metadata filtering

Note: For examples of how to extract metadata from your documents, see the Metadata Extraction guide.

To learn how to create and store embeddings in PGVector, see the Insert Embeddings in PGVector guide.

Setup

Environment Setup

Make sure to set up your environment variables:

OPENAI_API_KEY=your_openai_api_key_here
POSTGRES_CONNECTION_STRING=your_connection_string_here

Dependencies

Import the necessary dependencies:

import { embed } from 'ai';
import { PgVector } from '@mastra/pg';
import { openai } from '@ai-sdk/openai';

Vector Store Initialization

Initialize PgVector with your connection string:

const pgVector = new PgVector(process.env.POSTGRES_CONNECTION_STRING!);

Example Usage

Filter by Metadata Value

// Create embedding for the query
const { embedding } = await embed({
  model: openai.embedding('text-embedding-3-small'),
  value: '[Insert query based on document here]',
});
 
// Query with metadata filter
const result = await pgVector.query('embeddings', embedding, 3, {
  'path.to.metadata': {
    $eq: 'value',
  },
});
 
console.log('Results:', result);