This repository provides a comprehensive guide and implementation of RAG (Retrieval-Augmented Generation) for building state-of-the-art conversational AI chatbots.
What is RAG?
RAG combines the power of pre-trained language models (LLMs) with information retrieval techniques to achieve:
- More informed and accurate responses: Access and leverage external knowledge bases to deliver factually correct information.
- Enhanced fluency and coherence: Utilize LLMs to generate natural and engaging conversational language.
- Improved understanding of user intent: Gain deeper context through information retrieval, leading to more relevant responses.
What you will find:
- Detailed explanation of RAG architecture and its components: Understand the underlying concepts and how they work together.
- Step-by-step guide to build your own RAG chatbot: Learn how to implement and customize RAG for your specific needs.
- Pre-trained RAG models and sample datasets: Get started quickly with ready-to-use resources.
- Code examples and tutorials: Learn through practical examples how to build and train your RAG chatbot.
Who is this for?
- Developers and researchers interested in building next-generation chatbots.
- Anyone who wants to understand how RAG works and its potential for conversational AI.
- Anyone looking for a ready-to-use solution for building their own RAG chatbot.
Get started today!
Clone this repository, explore the documentation, and start building your own intelligent chatbot with RAG!