local-dir-rag 0.4.1

RAG from documents in a local directory

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 and other parameters (set in your .env file)

Installation

This project uses Poetry for dependency management.

1. Install Poetry by following the instructions in the official documentation.

   Quick installation methods:

   # For Linux, macOS, Windows (WSL)
   curl -sSL https://install.python-poetry.org | python3 -
   

   # For Windows PowerShell
   (Invoke-WebRequest -Uri https://install.python-poetry.org -UseBasicParsing).Content | python -
   

2. Install Project Dependencies

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

3. Install dependencies using Poetry

   poetry install --extras "dev"
   poetry show --tree
   

Configuration

Copy the ".env.example" file as ".env" in the project root. Update it with your OpenAI API key, location of your documents, and where you would like the vector database to be created.

Usage

1. Create Vector Database

   poetry run python -m local_dir_rag.main embed --docs-directory /path/to/docs --vector-db-path /path/to/vector_db
   

2. Query Documents

   poetry run python -m local_dir_rag.main query --vector-db-path /path/to/vector_db
   

Development and Testing

1. Install dependencies, as above.

2. Run all tests:

   poetry run pytest
   

   Or, run a single test:

   poetry run pytest tests/test_document_loader.py::test_load_document
   

Docker Compose Usage

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

1. Clone the project, as described above.

2. Configure the ".env" file as described above.

3. Run the application using Docker Compose:

   # 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.