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Version: 2.0.0

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.

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The complete source code and detailed instructions for setting up and running this example can be found in the Gaia Cookbook repository.

What You Can Learn and Build:

By exploring and adapting this example, you can learn how to:

  • Process and Chunk PDF Documents: Understand the steps involved in breaking down PDF files into manageable segments for effective retrieval.
  • Implement Semantic Search with Qdrant: See how to leverage a vector database for efficient and context-aware searching of your document chunks.
  • Integrate Local LLMs via Gaia: Learn how to connect to your local Gaia node to utilize the power of locally hosted language models for question answering.
  • Enhance Relevance with Cross-Encoder Reranking: Discover techniques to refine search results and improve the accuracy of retrieved information.
  • Provide a Seamless User Experience with Streaming Responses: Implement real-time feedback for users as the answer is generated.
  • Ensure Trustworthiness with Smart Source Citation: Learn how to provide clear references to the source documents for each answer.
  • Mitigate Hallucinations with Relevance Filtering: Explore strategies to filter out irrelevant information and reduce the likelihood of inaccurate responses.
  • Build a Customizable PDF Q&A Bot: This example provides a solid foundation for you to tailor and deploy your own question-answering system for specific PDF documents or collections.

Key Features Demonstrated:

  • 📑 PDF Document Processing and Chunking: Efficiently handles the extraction and segmentation of content from PDF files.
  • 🔍 Semantic Search using Qdrant: Leverages vector embeddings for intelligent retrieval of relevant document parts.
  • 🤖 Local LLM Integration through Gaia node: Connects to your locally running LLM for generating answers based on retrieved context.
  • ↗️ Cross-encoder reranking for improved relevance: Optimizes search results by applying a more sophisticated ranking model.
  • 💨 Streaming responses for better UX: Provides a more interactive and responsive user experience.
  • 🎯 Smart source citation: Clearly indicates the source documents used to generate each answer.
  • ⚡ Relevance filtering to prevent hallucinations: Enhances the reliability of answers by filtering out less relevant information.

Getting Started:

Prerequisites​

Before running Gaia RAG, 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 gaia-pdf-rag
  1. Create a virtual environment:
python -m venv venv
source venv/bin/activate  # On Windows use: venv\Scripts\activate
  1. 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
  1. 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
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The complete source code and detailed instructions for setting up and running this example can be found in the Gaia Cookbook repository.