🐧 Linux Documentation RAG Chatbot (Kernel + System Docs AI Assistant)

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📌 Introduction

This project builds a domain-specific AI assistant that answers Linux-related queries using:

• Official kernel documentation from :contentReference[oaicite:2]{index=2}
• Linux man pages and guides from :contentReference[oaicite:3]{index=3}

The system uses Retrieval-Augmented Generation (RAG) to:

• Retrieve relevant documentation sections
• Generate accurate, context-aware answers
• Reduce hallucination in LLM responses

Unlike generic chatbots, this assistant is grounded in real Linux documentation, making it useful for:

• Developers
• System engineers
• Firmware engineers

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🎯 Objectives

• Build a Linux domain-specific AI assistant
• Perform semantic search over system documentation
• Implement RAG pipeline using real-world technical data
• Enable developer productivity use-cases

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🌍 Dataset (Real-World Data Sources)

1. Linux Documentation Portal

From :contentReference[oaicite:4]{index=4}:

• Man pages (commands, syscalls)
• Bash scripting guides
• System administration guides :contentReference[oaicite:5]{index=5}

2. Linux Kernel Documentation

From :contentReference[oaicite:6]{index=6}:

• Kernel APIs
• Driver development docs
• Subsystems, tracing, debugging :contentReference[oaicite:7]{index=7}

👉 Kernel docs include:

• Core APIs
• Driver interfaces
• Development tools and debugging guides :contentReference[oaicite:8]{index=8}

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⚙️ Pre-requisites & System Requirements

💻 Hardware

• CPU sufficient
• Optional GPU (for faster embeddings)

🧠 Software

• Python 3.10+
• Linux environment (recommended)

📦 Libraries

pip install langchain faiss-cpu sentence-transformers \
            beautifulsoup4 requests lxml \
            fastapi uvicorn streamlit langchain-text-splitters

License

MIT