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

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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).

00:00 - Intro
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

Join this channel to get access to the perks and support my work:

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

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Комментарии

thanks mate ! subscribed, keep up the good work !!!

subzerosumgame

that's great; llama-parse is sweet. Please make a video showing how to use a knowledge graph index in conjunction with the vector DB!

daviddooling

Thank you for the tutorial very useful and easy to follow, can you please add the UI for this RAG application so that normal user can interact.

RajaRahamathullah

i think RetrievalQA class is deprecated. What about updating it to use the create_retrieval_chain

pelumifagbemi

Great material! My I ask you what model and hardware config are you using to get those performances? Thank youu

doansai

How to use ms doc docx with the same code . Any one suggets or point to code. Everything remains same except the input is docx.

iictiit

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

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