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 Kastrax and PGVector.

Overview ✅

The system implements filtered vector search using Kastrax 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, see the Upsert Embeddings 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 '@kastrax/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({
  indexName: 'embeddings',
  queryVector: embedding,
  topK: 3,
  filter: {
    'path.to.metadata': {
      $eq: 'value',
    },
  },
});
 
console.log('Results:', result);