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).

Follow me on X: [ Ссылка ]
AI Bootcamp: [ Ссылка ]
Discord: [ Ссылка ]
Subscribe: [ Ссылка ]
GitHub repository: [ Ссылка ]

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

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

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

свернуть