Advanced RAG with Llama 3 in Langchain | Chat with PDF using Free Embeddings, Reranker & LlamaParse

Let's build an advanced Retrieval-Augmented Generation (RAG) system with LangChain! You'll learn how to "teach" a Large Language Model (Llama 3) to read a complex PDF document and intelligently answer questions about it. We'll simplify the process by breaking the document into small pieces, converting these into vectors, and organizing them for fast answers. We'll build our RAG using only open models (Llama 3, FlagEmbedding & MS Marco reranker).

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AI Bootcamp: www.mlexpert.io/bootcamp
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GitHub repository: github.com/curiousily/AI-Bootcamp

00:00 - Intro
00:17 - Text tutorial on MLExpert.io
00:43 - Our RAG Architecture
05:11 - Google Colab Setup
06:36 - Document Parsing with LlamaParse
09:07 - Text Splitting, Vector Embeddings & Vector DB (Qdrant)
13:26 - Reranking with FlashRank
14:45 - Q&A Chain with LangChain, Llama 3 and Groq API
16:32 - Chat with the PDF
21:30 - Conclusion

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#artificialintelligence #langchain #chatbot #llama #chatgpt #llm