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Offered By: IBMSkillsNetwork

Enhance LLMs using RAG and Hugging Face

Learn Retrieval-Augmented Generation (RAG) by building context-aware Large Language Models (LLMs). This guided project leverages Hugging Face and Facebook AI Similarity Search (FAISS) for efficient semantic search and natural language generation, enabling personalized, context-rich responses from your own documents. Ideal for advancing your understanding of AI techniques and enhancing the capabilities of LLMs with relevant contextual information.

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Guided Project

Deep Learning

4.0
(9 Reviews)

At a Glance

Learn Retrieval-Augmented Generation (RAG) by building context-aware Large Language Models (LLMs). This guided project leverages Hugging Face and Facebook AI Similarity Search (FAISS) for efficient semantic search and natural language generation, enabling personalized, context-rich responses from your own documents. Ideal for advancing your understanding of AI techniques and enhancing the capabilities of LLMs with relevant contextual information.

In the age of information overload, having the ability to retrieve the most relevant and context-aware information from vast amounts of data is invaluable. Retrieval-Augmented Generation (RAG) represents a cutting-edge technique that combines the strengths of retrieval systems and Large Language Models (LLMs) to generate high-quality, relevant responses from your own custom documents. This project is not only fascinating due to its innovative approach but also practical, as it equips you with the skills to build intelligent, responsive systems that can be applied in various domains such as customer service, content creation, and more. By completing this project, you'll gain deep insights into how state-of-the-art AI models such as BART and DPR, combined with FAISS indexing, can revolutionize document retrieval and content generation.

A look at the project ahead

Throughout this project, you will embark on a journey to master the development of a Retrieval-Augmented Generation (RAG) model, leveraging the power of Hugging Face, BART, DPR, and FAISS. Here's what you'll be able to achieve by the end of this guided project:

  • Understand and implement the Retrieval-Augmented Generation (RAG) framework, integrating Hugging Face’s BART and DPR models for robust document retrieval and response generation.
  • Gain hands-on experience in using FAISS for efficient indexing and retrieval, enabling scalable and fast semantic search within your custom document collection.

What you'll need


Before you begin this guided project, it's recommended that you have a basic understanding of Python programming and some familiarity with deep learning concepts. Experience with natural language processing (NLP) would be advantageous, but is not mandatory. You'll be working in an environment powered by IBM Skills Network Labs, which comes pre-installed with essential tools such as Python, Hugging Face libraries, and FAISS, so you can focus on learning without worrying about setting up your environment. This project is best accessed using the latest versions of Chrome, Edge, Firefox, Internet Explorer, or Safari to ensure optimal performance.

Estimated Effort

30 Minutes

Level

Beginner

Skills You Will Learn

Faiss, HuggingFace, LLM, Python, RAG

Language

English

Course Code

GPXX09OTEN

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