Docspgvector

pgvector Integration

Native vector similarity search for AI-powered applications, semantic search, and RAG pipelines.

Vector search through REST and GraphQL

Postrust provides native support for pgvector, enabling vector similarity search directly through your REST and GraphQL APIs. Build AI-powered applications with:

Semantic search using embeddings
Product recommendations
RAG (Retrieval Augmented Generation) pipelines
Image similarity search
Document clustering

Setup

SQL

-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create table with vector column
CREATE TABLE documents (
  id SERIAL PRIMARY KEY,
  title TEXT NOT NULL,
  content TEXT,
  embedding vector(1536)  -- OpenAI dimensions
);

-- Create index for fast similarity search
CREATE INDEX ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

REST API

cURL

# Insert document with embedding
curl -X POST "localhost:3000/documents" \
  -H "Content-Type: application/json" \
  -d '{
    "title": "Introduction to Rust",
    "content": "Rust is a systems programming language...",
    "embedding": [0.1, 0.2, 0.3, ...]
  }'

# Search similar documents via RPC
curl -X POST "localhost:3000/rpc/search_similar" \
  -H "Content-Type: application/json" \
  -d '{
    "query_embedding": [0.1, 0.2, ...],
    "match_count": 10
  }'

Similarity Search Function

Create a PostgreSQL function for semantic search:

SQL

CREATE OR REPLACE FUNCTION search_similar(
  query_embedding vector(1536),
  match_count int DEFAULT 10,
  match_threshold float DEFAULT 0.8
)
RETURNS TABLE (
  id int,
  title text,
  content text,
  similarity float
)
LANGUAGE sql STABLE
AS $$
  SELECT
    documents.id,
    documents.title,
    documents.content,
    1 - (documents.embedding <=> query_embedding) AS similarity
  FROM documents
  WHERE 1 - (documents.embedding <=> query_embedding) > match_threshold
  ORDER BY documents.embedding <=> query_embedding
  LIMIT match_count;
$$;

GraphQL API

GraphQL

# Query documents with embeddings
query {
  documents {
    id
    title
    embedding
  }
}

# Similarity search via RPC
query SearchSimilar($embedding: [Float!]!) {
  searchSimilar(queryEmbedding: $embedding, matchCount: 10) {
    id
    title
    content
    similarity
  }
}

Distance Operators

Operator	Description	Use Case
<->	L2 distance (Euclidean)	General purpose
<#>	Negative inner product	Normalized vectors
<=>	Cosine distance	Text embeddings

Index Types

IVFFlat

Faster build, less memory. Good for most use cases.

USING ivfflat (embedding vector_cosine_ops)

HNSW

More accurate, faster queries. Slower to build.

USING hnsw (embedding vector_cosine_ops)

GraphQL Realtime Subscriptions