Скачать с ютуб Build Your Own RAG Using Unstructured, Llama3 via Groq, Qdrant & LangChain в хорошем качестве

Build Your Own RAG Using Unstructured, Llama3 via Groq, Qdrant & LangChain

In this 5th video in the unstructured playlist, I will explain you how to create your own Retrieval Augmented Generation (RAG) bot using the following tech stack. LangChain as framework UnstructuredIO for data prep Fastembed for embedding Qdrant Cloud as vectorstore Llama3 via GroqInc 80% of enterprise data exists in difficult-to-use formats like HTML, PDF, CSV, PNG, PPTX, and more. Unstructured effortlessly extracts and transforms complex data for use with every major vector database and LLM framework. Link ⛓️‍💥 https://unstructured.io/ Code 👨🏻‍💻 https://github.com/sudarshan-koirala/... ------------------------------------------------------------------------------------------ Timestamps ⏰ 00:00 Introduction 02:33 Setup 04:58 Preprocess PDF 10:42 Preprocess Markdown (Readme) 14:08 Load the document into the VectorDB 17:27 Now the RAG part 22:24 Qdrant Cloud and LangSmith 25:19 Conclusion ------------------------------------------------------------------------------------------ ☕ Buy me a Coffee: https://ko-fi.com/datasciencebasics ✌️Patreon:   / datasciencebasics   ------------------------------------------------------------------------------------------ 🤝 Connect with me: 📺 Youtube:    / @datasciencebasics   👔 LinkedIn:   / sudarshan-koirala   🐦 Twitter:   / mesudarshan   🔉Medium:   / sudarshan-koirala   💼 Consulting: https://topmate.io/sudarshan_koirala #unstructureddata ##unstructuredio #rag #langchain #llm #datasciencebasics