Advanced RAG with Llama 3 in LangChain - Building a PDF Chat System

Learn to build an advanced Retrieval-Augmented Generation (RAG) system using LangChain and Llama 3. Discover how to process complex PDF documents, create vector embeddings, and implement intelligent question-answering capabilities. Follow along as the video guides you through setting up Google Colab, parsing documents with LlamaParse, text splitting, creating vector embeddings with Qdrant, reranking with FlashRank, and building a Q&A chain using LangChain, Llama 3, and the Groq API. Gain hands-on experience in developing a chatbot that can effectively interact with PDF content using open-source models and tools. Perfect for developers and AI enthusiasts looking to enhance their skills in natural language processing and document analysis.

- Intro
- Text tutorial on MLExpert.io
- Our RAG Architecture
- Google Colab Setup
- Document Parsing with LlamaParse
- Text Splitting, Vector Embeddings & Vector DB Qdrant
- Reranking with FlashRank
- Q&A Chain with LangChain, Llama 3 and Groq API
- Chat with the PDF
- Conclusion