Vector Store

Vector store implementation for document embeddings

Vector Store Implementation Guide #

✅ Implementation Complete #

This implementation provides pgvector support with multi-tenant vector storage and similarity search for your TypeORM + Supabase Postgres application.

This document describes the pgvector and LangChain integration for embedding storage and similarity search.

Setup #

1. Environment Variables #

Add the following to your .env file:

# Required
OPENAI_API_KEY=your-openai-api-key
EMBEDDING_DIM=1536  # Default dimensions for text-embedding-3-small

# Database (should already be configured)
DB_HOST=your-supabase-host
DB_PORT=5432
DB_USERNAME=your-username
DB_PASSWORD=your-password
DB_NAME=your-database

2. Run Migrations #

# Run the embeddings table migration
npm run migration:run

# Optional: Run RLS migration for Supabase deployments
# Edit the RLS migration file first to match your JWT structure

Usage #

Basic Usage in Code #

import { vectorStoreService } from "./services/vector-store.service";

// Initialize (after DataSource is ready)
await vectorStoreService.initialize();

// Add embeddings
const chunks = [
  { content: "First chunk of text", metadata: { source: "doc1" } },
  { content: "Second chunk of text", metadata: { source: "doc1" } },
];

const ids = await vectorStoreService.addChunks(
  organizationId,
  documentId, // optional
  chunks,
);

// Search for similar content
const results = await vectorStoreService.search(
  organizationId,
  "search query",
  10, // top K results
);

Running the Example Script #

# Set environment variables
export ORGANIZATION_ID="your-org-uuid"
export DOCUMENT_ID="your-doc-uuid"  # optional

# Run the example
cd server
npx ts-node scripts/ingest-example.ts

# With cleanup
CLEANUP=true npx ts-node scripts/ingest-example.ts

Architecture #

Database Schema #

The embeddings table structure:

• id: UUID primary key
• organizationId: UUID for multi-tenancy
• documentId: Optional UUID linking to documents
• content: The text content
• metadata: JSONB for additional data
• embedding: vector(1536) for similarity search

Indexes #

1. B-tree index on organizationId for filtering
2. HNSW index on embedding using cosine distance for similarity search

Performance Optimization #

For large-scale deployments:

1. Partial HNSW indexes per large organization:

CREATE INDEX embeddings_embedding_hnsw_org_xyz
ON embeddings USING hnsw (embedding vector_cosine_ops)
WHERE "organizationId" = 'specific-org-uuid';

2. LIST partitioning for massive scale (see migration comments)

API Reference #

VectorStoreService Methods #

initialize() #

Initialize the vector store. Must be called after DataSource is initialized.

addChunks(organizationId, docId, chunks) #

Add text chunks to the vector store.

• Returns: Array of embedding IDs

search(organizationId, query, k) #

Search for similar content within an organization.

• Returns: Array of results with similarity scores

deleteByDocumentId(organizationId, docId) #

Delete all embeddings for a document.

• Returns: Number of deleted rows

getStatistics(organizationId) #

Get embedding statistics for an organization.

• Returns: Statistics object

Switching Embedding Models #

To use a different embedding model:

1. Update .env:

OPENAI_EMBEDDING_MODEL=text-embedding-3-large
EMBEDDING_DIM=3072  # For large model

2. Create a new migration to update the vector dimension:

ALTER TABLE embeddings
ALTER COLUMN embedding TYPE vector(3072);

Switching Distance Metrics #

Currently using cosine distance (default). To switch:

For L2 (Euclidean) distance: #

-- Drop old index
DROP INDEX embeddings_embedding_hnsw;

-- Create new index with L2
CREATE INDEX embeddings_embedding_hnsw
ON embeddings USING hnsw (embedding vector_l2_ops);

-- Update queries to use <-> operator

For Inner Product: #

-- Create index with inner product
CREATE INDEX embeddings_embedding_hnsw
ON embeddings USING hnsw (embedding vector_ip_ops);

-- Update queries to use <#> operator

Troubleshooting #

Extension not found #

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS pgcrypto;

Performance issues #

1. Check index usage: EXPLAIN ANALYZE your_query
2. Consider partial indexes for large orgs
3. Monitor lists and ef_construction HNSW parameters

Multi-tenancy concerns #

• Always filter by organizationId first
• Use RLS policies in Supabase deployments
• Consider partitioning for 1000+ organizations

Testing #

Run the integration tests:

npm test -- tests/integration/vector-store.test.ts

Security Considerations #

1. Multi-tenancy: All queries are scoped by organizationId
2. RLS: Optional Row-Level Security for Supabase
3. API Keys: Store OpenAI keys securely
4. Data deletion: Cascade delete with documents

Resources #

• pgvector documentation
• LangChain TypeORM integration
• OpenAI embeddings