
    Retrieval-Augmented Generation (RAG) is a technique that combines retrieval-based and generation-based approaches
    for natural language processing tasks. It involves retrieving relevant information from a knowledge base and then 
    using that information to generate more accurate and informed responses.

    RAG models first retrieve documents that are relevant to a given query, then use these documents as additional context
    for language generation. This approach helps to ground the model's responses in factual information and reduces hallucinations.

    The llama.cpp library is a C/C++ implementation of Meta's LLaMA model, optimized for CPU usage. It allows running LLaMA models
    on consumer hardware without requiring high-end GPUs.

    LocalAI is a framework that enables running AI models locally without relying on cloud services. It provides APIs compatible
    with OpenAI's interfaces, allowing developers to use their own models with the same code they would use for OpenAI services.
    