grepai-storage-postgres

GrepAI Storage with PostgreSQL

This skill covers using PostgreSQL with the pgvector extension as the storage backend for GrepAI.

When to Use This Skill

• Team environments with shared index
• Large codebases (10K+ files)
• Need concurrent access
• Integration with existing PostgreSQL infrastructure

Prerequisites

1. PostgreSQL 14+ with pgvector extension
2. Database user with create table permissions
3. Network access to PostgreSQL server

Advantages

Benefit	Description
👥 Team sharing	Multiple users can access same index
📏 Scalable	Handles large codebases
🔄 Concurrent	Multiple simultaneous searches
💾 Persistent	Data survives machine restarts
🔧 Familiar	Standard database tooling

Setting Up PostgreSQL with pgvector

Option 1: Docker (Recommended for Development)

# Run PostgreSQL with pgvector
docker run -d \
  --name grepai-postgres \
  -e POSTGRES_USER=grepai \
  -e POSTGRES_PASSWORD=grepai \
  -e POSTGRES_DB=grepai \
  -p 5432:5432 \
  pgvector/pgvector:pg16

Option 2: Install on Existing PostgreSQL

# Install pgvector extension (Ubuntu/Debian)
sudo apt install postgresql-16-pgvector

# Or compile from source
git clone https://github.com/pgvector/pgvector.git
cd pgvector
make
sudo make install

Then enable the extension:

-- Connect to your database
CREATE EXTENSION IF NOT EXISTS vector;

Option 3: Managed Services

• Supabase: pgvector included by default
• Neon: pgvector available
• AWS RDS: Install pgvector extension
• Azure Database: pgvector available

Configuration

Basic Configuration

# .grepai/config.yaml
store:
  backend: postgres
  postgres:
    dsn: postgres://user:password@localhost:5432/grepai

With Environment Variable

store:
  backend: postgres
  postgres:
    dsn: ${DATABASE_URL}

Set the environment variable:

export DATABASE_URL="postgres://user:password@localhost:5432/grepai"