BeautiRAG App ✨

BeautiRAG is a multimodal Agentic Retrieval-Augmented Generation (RAG) application built with Next.js for the frontend, LangChain framework and FastAPI for the backend. It allows to upload various document types (text, PDF, images, audio), processes them, stores embeddings locally using FAISS, and provides a chat interface to query the documents using large language models (LLMs).

Features

• Frontend: Built with Next.js and Tailwind CSS
  • Drag-and-drop document upload (TXT, PDF, DOCX, PNG, JPG, MP3, WAV)
  • Chat interface
  • Selection and API key input of different LLMs OpenAI, Anthropic, DeepSeek, Ollama (to be integrated)
• Backend: Built with FastAPI and LangChain framework
  • Document processing using LangChain loaders.
  • OCR for images using Tesseract.
  • Transcription for audio using OpenAI Whisper.
  • Embedding generation using Sentence Transformers all-MiniLM-L6-v2 (planning to extend to other models).
  • Vector storage using FAISS (local).
  • RAG pipeline implementation using LangChain Expression Language (LCEL).
  • API endpoints for file upload, processing, and querying.
• Containerization: Dockerized using Docker Compose for easy setup and deployment.

Project Structure

rag-app/
├── beautirag-app/
│   ├── public/
│   ├── src/
│   │   ├── app/         # Next.js frontend pages/routing
│   │   ├── components/  # React components
│   │   └── backend/     # FastAPI backend application
│   │       ├── core/      # Core components (config)
│   │       ├── services/  # RAG, processor, vector store
│   │       ├── data/      # Local data storage
│   │       ├── .dockerignore
│   │       ├── Dockerfile # Backend Dockerfile
│   │       └── requirements.txt
│   ├── .dockerignore
│   ├── .env           # Will be used for default API key set up
│   ├── Dockerfile     # Frontend Dockerfile
│   ├── next.config.ts
│   ├── package.json
│   └── ...            # Other frontend config files
├── .gitignore
├── docker-compose.yml
└── README.md       

Prerequisites

• Docker and Docker Compose (if using Linux)
• An .env file in the beautirag-app directory (will be necessary in the future).
• System dependencies for backend features:
  • Tesseract: For image OCR. Installation varies by OS (Tesseract Wiki).
  • FFmpeg: For audio processing by Whisper. Installation varies by OS (FFmpeg Download).
  • Note: These are installed automatically within the Docker container. For local development/testing outside Docker, need to install them on the host machine.

Setup & Launch

1. Clone the Repository (if applicable)

git clone <repository-url>
cd rag-app

2. Create Environment File

Create a file named .env inside the beautirag-app directory:

cd beautirag-app
touch .env

Add your API keys and any necessary configurations to this .env file. Example:

OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
DEEPSEEK_API_KEY=sk-...

# WHISPER_MODEL_SIZE=small

# EMBEDDING_MODEL_NAME=...

# If Tesseract is not in system PATH for local dev (not needed for Docker)
# TESSERACT_CMD="C:/Program Files/Tesseract-OCR/tesseract.exe"

3. Launch using Docker Compose

This is the easiest way to run the application with all dependencies included.
From the project root directory (rag-app):

docker compose up --build

• The --build flag is needed the first time or after code changes.
• Wait for both backend and frontend services to start and the backend healthcheck to pass.
• Navigate to http://localhost:3000 in the browser

To stop the application:

docker compose down

4. Local Development/Test (Alternative)

It is possible to run the frontend and backend separately without Docker:

Backend Setup:

1. Navigate to the backend directory:

   cd beautirag-app/src/backend

2. Create a Python virtual environment:

   python -m venv .venv
   .\.venv\Scripts\activate # Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Ensure Tesseract and FFmpeg are installed on your system and accessible
5. Ensure the .env file exists in the beautirag-app directory
6. Run the FastAPI server (from the beautirag-app/src directory):

   cd .. # Go up to src directory
   uvicorn backend.main:app --reload --port 8000

Frontend Setup:

1. Navigate to the frontend directory:

   cd beautirag-app

2. Install dependencies:

   npm install

3. Run the Next.js development server:

   npm run dev

4. Navigate to http://localhost:3000 in the browser

Usage

1. Upload Documents: Use the "Upload Documents" section to drag and drop or select files.
2. Configure Model: Select the desired LLM and enter the API key if required. Click "Validate".
3. Chat: Use the chat interface to ask questions about the content of the uploaded documents.