Vector Stores

Vector stores persist the embeddings and enable similarity search.

Available Stores

Backend	Type	Pros	Cons
GOB	File-based	Simple, no setup	Single machine only
PostgreSQL	Database	Scalable, team-friendly	Requires PostgreSQL + pgvector

GOB (File-based)

The default storage backend. Stores vectors in a binary file.

Configuration

store:
  backend: gob
  gob:
    path: .grepai/index.gob

Characteristics

• Pros:

  • Zero dependencies
  • Fast for small/medium codebases
  • Portable (just copy the file)

• Cons:

  • Single machine only
  • Loads entire index into memory
  • No concurrent access

Best For

• Personal projects
• Quick experimentation
• CI/CD pipelines (ephemeral index)

PostgreSQL with pgvector

Scalable vector storage using PostgreSQL and the pgvector extension.

Setup

1. Install PostgreSQL with pgvector:

# macOS with Homebrew
brew install postgresql pgvector

# Ubuntu/Debian
sudo apt install postgresql postgresql-contrib
# Then install pgvector from source or package

2. Create the database and extension:

CREATE DATABASE grepai;
\c grepai
CREATE EXTENSION vector;

3. Create the table (auto-created by grepai, but for reference):

CREATE TABLE IF NOT EXISTS chunks (
    id SERIAL PRIMARY KEY,
    file_path TEXT NOT NULL,
    start_line INT NOT NULL,
    end_line INT NOT NULL,
    content TEXT NOT NULL,
    embedding vector(768),  -- Adjust dimensions based on embedder
    created_at TIMESTAMP DEFAULT NOW()
);

CREATE INDEX ON chunks USING ivfflat (embedding vector_cosine_ops);

Configuration

store:
  backend: postgres
  postgres:
    connection_string: postgres://user:password@localhost:5432/grepai

Environment Variable

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

store:
  backend: postgres
  postgres:
    connection_string: ${GREPAI_POSTGRES_URL}

Characteristics

• Pros:

  • Scales to large codebases
  • Concurrent access (team use)
  • Advanced filtering with SQL
  • Persistent and reliable

• Cons:

  • Requires PostgreSQL setup
  • More complex deployment

Best For

• Team environments
• Large codebases (100k+ lines)
• Production deployments
• When you need SQL queries on metadata

Adding a New Store

To add a new storage backend:

1. Implement the VectorStore interface in store/:

type VectorStore interface {
    Store(ctx context.Context, chunks []Chunk) error
    Search(ctx context.Context, embedding []float32, limit int) ([]Result, error)
    Delete(ctx context.Context, filePath string) error
    Close() error
}

2. Add configuration in config/config.go

3. Wire it up in the CLI commands

See Contributing for more details.