Step-by-Step Guide to Building a RAG LLM App with LLamA2 and LLaMAindex

Summary

In this video, Krishak guides viewers through building a retrieval-augmented generation (RAG) system using open-source models like Llama 2. He covers the step-by-step process, from installing necessary libraries like PyPDF and Transformers to creating embeddings, indexing documents, and querying with Llama 2 via Hugging Face. The tutorial also introduces techniques like quantization for optimizing models in Google Colab, integrating LangChain, and using Hugging Face for embeddings. Krishak plans to explore more models like Mistral and Falcon in future videos, offering valuable insights for developers working with RAG systems.

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