Gaia RAG: PDF Question-Answering with Gaia and Qdrant

Gaia PDF RAG is a Retrieval-Augmented Generation (RAG) application that allows users to ask questions about PDF documents using a local Gaia node and Qdrant vector database. It combines the power of local LLMs with efficient vector search to provide accurate, context-aware answers.

Multiple Files Example

Features

• 📑 PDF document processing and chunking
• 🔍 Semantic search using Qdrant vector database
• 🤖 Local LLM integration through Gaia node
• ↗️ Cross-encoder reranking for improved relevance
• 💨 Streaming responses for better UX
• 🎯 Smart source citation
• ⚡ Relevance filtering to prevent hallucinations

Prerequisites

Before running GaiaRAG, ensure you have:

1. A local Gaia node running (Check this link to learn how to run your own local LLM: https://docs.gaianet.ai/node-guide/quick-start)
2. Qdrant server running
3. Python 3.8+
4. Required system libraries for PDF processing

Installation

1. Clone the repository:

git clone https://github.com/harishkotra/gaia-pdf-rag.git
cd gaiarag

2. Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

Setting Up Components

1. Gaia Node

Start your local Gaia node:

gaianet init
gaianet start

2. Qdrant Server

Start Qdrant using Docker:

docker run -d -p 6333:6333 -p 6334:6334 \
    -v $(pwd)/qdrant_storage:/qdrant/storage \
    qdrant/qdrant

Running the Application

1. Make sure both Gaia node and Qdrant are running

2. Start the Streamlit app:

streamlit run app.py

3. Open your browser at http://localhost:8501

Usage

1. Upload a PDF document using the sidebar
2. Click "Process Document" to index it
3. Ask questions in the main input field
4. View answers and relevant source documents

Configuration

You can modify the following parameters in app.py:

• GAIA_NODE_URL: URL of your local Gaia node
• QDRANT_HOST: Qdrant server host
• QDRANT_PORT: Qdrant server port
• VECTOR_SIZE: Embedding dimension size
• COLLECTION_NAME: Name for vector database collection

Project Structure

gaia-pdf-rag/
├── app.py              # Main Streamlit application
├── requirements.txt    # Python dependencies
├── .gitignore          # Gitignore file
├── README.md           # This file

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Credits

Inspired by this example.