cache_augumeted_generation

• ⚙️ Setup Instructions
• 💻 Running the Application
• 🔍 Overview
• ✨ Advantages of CAG
• ⚠️ Limitations of CAG
• 📚 References

🔍 Overview

Retrieval-Augmented Generation (RAG) enhances language models by integrating external knowledge but faces challenges like retrieval latency, errors, and system complexity. Cache-Augmented Generation (CAG) addresses these by preloading relevant data into the model's context, leveraging modern LLMs' extended context windows and caching runtime parameters. This eliminates real-time retrieval during inference, enabling direct response generation.

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✨ Advantages of CAG

• Reduced Latency: Faster inference by removing real-time retrieval.
• Improved Reliability: Avoids retrieval errors and ensures context relevance.
• Simplified Design: Offers a streamlined, low-complexity alternative to RAG with comparable or better performance.

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⚠️ Limitations of CAG

• Knowledge Size Limits: Requires fitting all relevant data into the context window, unsuitable for extremely large datasets.
• Context Length Issues: Performance may degrade with very long contexts.

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📚 References

• Research Paper
• https://github.com/hhhuang/CAG/tree/main

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⚙️ Setup Instructions

• Prerequisites

  • Python 3.9 or higher
  • pip (Python package installer)

• Installation

  1. Clone the repository:

     git clone https://github.com/genieincodebottle/genaicodelab.git
     cd genaicodelab/cache_augumeted_generation

  2. Create a virtual environment:

     python -m venv venv
     venv\Scripts\activate # On Linux -> source venv/bin/activate

  3. Install dependencies:

     pip install torch --index-url https://download.pytorch.org/whl/cu118
     pip install -r requirements.txt

  4. Rename .env.example to .env

  5. Get your Hugging Face token:

     • Visit Hugging Face Tokens Page
     • Create a new token with read access

  6. Copy the token to HF_TOKEN in your .env file

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💻 Running the Application

To start the application, run:

streamlit run app.py