Local Directory RAG

A simple tool for Retrieval-Augmented Generation (RAG) using documents from your local filesystem.

Overview

Local Directory RAG allows you to:

1. Create vector embeddings from your local documents (PDF, TXT)
2. Query these documents using natural language, leveraging OpenAI's language models

Requirements

• Python 3.13 or higher
• OpenAI API key (set in your .env file)

Installation

# Clone the repository
git clone https://github.com/sualeh/local-dir-rag.git
cd local-dir-rag

# Install dependencies
pip install -e .

Configuration

Create a .env file in the project root with your OpenAI API key:

OPENAI_API_KEY=your-api-key-here

You can also create a .env.params file to set default directories:

DOCS_DIRECTORY=path/to/your/documents
VECTOR_DB_PATH=path/to/save/vector/database

Run Locally

Run locally with the following command, with the approproate arguments:

poetry run python -m local_dir_rag.main

Usage

Create Vector Database

# Using command line arguments
python -m local_dir_rag.main embed --docs-directory /path/to/docs --vector-db-path /path/to/vector_db

# Or if installed as a package
local-dir-rag embed --docs-directory /path/to/docs --vector-db-path /path/to/vector_db

Query Documents

# Using command line arguments
python -m local_dir_rag.main query --vector-db-path /path/to/vector_db

# Or if installed as a package
local-dir-rag query --vector-db-path /path/to/vector_db

Docker Usage

You can run Local RAG using Docker:

# Pull the Docker image
docker pull sualeh/local-dir-rag:latest

# Embed documents
docker run -v /path/to/your/docs:/data/docs -v /path/to/vector_db:/data/vector_db \
  -e OPENAI_API_KEY=your-api-key-here \
  sualeh/local-dir-rag embed

# Query your documents
docker run -v /path/to/vector_db:/data/vector_db \
  -e OPENAI_API_KEY=your-api-key-here \
  sualeh/local-dir-rag query

You can also pass command line arguments directly:

docker run -v /path/to/your/docs:/data/docs -v /path/to/output:/data/vector_db \
  -e OPENAI_API_KEY=your-api-key-here \
  sualeh/local-dir-rag embed --docs-directory /data/docs --vector-db-path /data/vector_db

Docker Compose Usage

You can also use Docker Compose for easier management of the Local RAG container:

1. Create a docker-compose.yml file:

version: '3'
services:
  local-dir-rag:
    image: sualeh/local-dir-rag:latest
    environment:
      - OPENAI_API_KEY=${OPENAI_API_KEY}
    volumes:
      - ./docs:/data/docs
      - ./vector_db:/data/vector_db

2. Run the application:

# For embedding documents
docker-compose run local-dir-rag embed

# For querying documents
docker-compose run local-dir-rag query

You can also pass additional arguments:

docker-compose run local-dir-rag embed --docs-directory /data/docs --vector-db-path /data/vector_db

This approach simplifies volume mounting and environment variable management, especially when working with the tool regularly.