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rag-example

A RAG (Retrieval-Augmented Generation) example project. It builds a knowledge base by processing PDF documents and provides intelligent Q&A functionality.

Features

  • PDF document loading and chunking
  • Text vectorization and storage (using Qdrant)
  • Intelligent Q&A with dynamic context retrieval

Dependencies

  • Rust 1.75+
  • OpenAI API key (for embedding model and LLM)
  • Qdrant vector database (requires URL and API key)

Installation & Run

  1. Clone the repository: git clone https://github.com/atopx/rag-example.git
  2. Install dependencies: cargo build --release
  3. Configure environment variables:
    • OPENAI_API_KEY: OpenAI API key
    • OPENAI_BASE_URL: OpenAI API endpoint (optional, default: https://api.openai.com)
    • QDRANT_URL: Qdrant service URL (e.g., http://localhost:6333)
    • QDRANT_API_KEY: Qdrant API key (leave empty if not required)
  4. Run the program: cargo run

License

This project is licensed under the MIT License - see the LICENSE file for details.