grepai-storage-postgres

name	grepai-storage-postgres
description	Configure PostgreSQL with pgvector for GrepAI. Use this skill for team environments and large codebases.

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"

Full DSN Options

store:
  backend: postgres
  postgres:
    dsn: postgres://user:password@host:5432/database?sslmode=require

DSN components:

• user: Database username
• password: Database password
• host: Server hostname or IP
• 5432: Port (default: 5432)
• database: Database name
• sslmode: SSL mode (disable, require, verify-full)

SSL Modes

Mode	Description	Use Case
disable	No SSL	Local development
require	SSL required	Production
verify-full	SSL + verify certificate	High security

# Production with SSL
store:
  backend: postgres
  postgres:
    dsn: postgres://user:[e-mail]:5432/grepai?sslmode=require

Database Schema

GrepAI automatically creates these tables:

-- Vector embeddings table
CREATE TABLE IF NOT EXISTS embeddings (
    id SERIAL PRIMARY KEY,
    file_path TEXT NOT NULL,
    chunk_index INTEGER NOT NULL,
    content TEXT NOT NULL,
    start_line INTEGER,
    end_line INTEGER,
    embedding vector(768),  -- Dimension matches your model
    created_at TIMESTAMP DEFAULT NOW(),
    UNIQUE(file_path, chunk_index)
);

-- Index for vector similarity search
CREATE INDEX ON embeddings USING ivfflat (embedding vector_cosine_ops);

Verifying Setup

Check pgvector Extension

-- Connect to database
psql -U grepai -d grepai

-- Check extension is installed
SELECT * FROM pg_extension WHERE extname = 'vector';

-- Check GrepAI tables exist (after first grepai watch)
\dt

Test Connection from GrepAI

# Check status
grepai status

# Should show PostgreSQL backend info

Performance Tuning

PostgreSQL Configuration

For better vector search performance:

-- Increase work memory for vector operations
SET work_mem = '256MB';

-- Adjust for your hardware
SET effective_cache_size = '4GB';
SET shared_buffers = '1GB';

Index Tuning

For large indices, tune the IVFFlat index:

-- More lists = faster search, more memory
CREATE INDEX ON embeddings
USING ivfflat (embedding vector_cosine_ops)
WITH (lists = 100);  -- Adjust based on row count

Rule of thumb: lists = sqrt(rows)

Concurrent Access

PostgreSQL handles concurrent access automatically:

• Multiple grepai search commands work simultaneously
• One grepai watch daemon per codebase
• Many users can share the same index

Team Setup

Shared Database

All team members point to the same database:

# Each developer's .grepai/config.yaml
store:
  backend: postgres
  postgres:
    dsn: postgres://team:[e-mail]:5432/grepai

Per-Project Databases

For isolated projects, use separate databases:

# Create databases
createdb -U postgres grepai_projecta
createdb -U postgres grepai_projectb

# Project A config
store:
  backend: postgres
  postgres:
    dsn: postgres://user:pass@localhost:5432/grepai_projecta

Backup and Restore

Backup

pg_dump -U grepai -d grepai > grepai_backup.sql

Restore

psql -U grepai -d grepai < grepai_backup.sql

Migrating from GOB

1. Set up PostgreSQL with pgvector
2. Update configuration:

store:
  backend: postgres
  postgres:
    dsn: postgres://user:pass@localhost:5432/grepai

3. Delete old index:

rm .grepai/index.gob

4. Re-index:

grepai watch

Common Issues

❌ Problem: FATAL: password authentication failed ✅ Solution: Check DSN credentials and pg_hba.conf

❌ Problem: ERROR: extension "vector" is not available ✅ Solution: Install pgvector:

sudo apt install postgresql-16-pgvector
# Then: CREATE EXTENSION vector;

❌ Problem: ERROR: type "vector" does not exist ✅ Solution: Enable extension in the database:

CREATE EXTENSION IF NOT EXISTS vector;

❌ Problem: Connection refused ✅ Solution:

• Check PostgreSQL is running
• Verify host and port
• Check firewall rules

❌ Problem: Slow searches ✅ Solution:

• Add IVFFlat index
• Increase work_mem
• Vacuum and analyze tables

Best Practices

1. Use environment variables: Don't commit credentials
2. Enable SSL: For remote databases
3. Regular backups: pg_dump before major changes
4. Monitor performance: Check query times
5. Index maintenance: Regular VACUUM ANALYZE

Output Format

PostgreSQL storage status:

✅ PostgreSQL Storage Configured

   Backend: PostgreSQL + pgvector
   Host: localhost:5432
   Database: grepai
   SSL: disabled

   Contents:
   - Files: 2,450
   - Chunks: 12,340
   - Vector dimension: 768

   Performance:
   - Connection: OK
   - IVFFlat index: Yes
   - Search latency: ~50ms