Build your own RAG based LLM Application (Completely Offline!): AI for your documents скачать в хорошем качестве

Build your own RAG based LLM Application (Completely Offline!): AI for your documents

Retrieval Augmented Generation is one of most essential use cases with Large Language Models. You can ground your large language model to answer questions based on the contents of your document. For this tutorial, we are building a complete offline RAG-based LLM app which utilizes Ollama for inference, ChromaDB for vector store, streamlit for UI. --- 🔥 Resources *Code Example*: https://github.com/yankeexe/llm-rag-w... Ollama Download: https://ollama.dev/download Ollama Llama3.2:3b: https://ollama.dev/library/llama3.2 Ollama nomic-embed-text: https://ollama.com/library/nomic-embe... Cross Encoder: https://huggingface.co/sentence-trans... Streamlit: https://docs.streamlit.io/develop/api... ChromaDB: https://docs.trychroma.com/guides Ollama SDK: https://github.com/ollama/ollama-python Example Docs AI Practitioner Doc: https://d1.awsstatic.com/training-and... CKS Doc: https://github.com/cncf/curriculum/bl... --- ⚡️ Follow me Github: https://github.com/yankeexe LinkedIn:   / yankeemaharjan   Twitter (X): https://x.com/yankexe Website: https://yankee.dev -- 🎞️ Chapters 0:00 Intro 0:16 Application Demo 1:13 Prerequisites 1:50 Code: Env Setup 2:40 Code: App UI 3:40 Code: Splitting Document + Data Structures 8:06 Code: Embedding +Vector Database 15:58 Code: Adding LLM + Grounding 20:30 Code: Re-ranking with Cross-Encoders 24:29 Demo: Multi-document Relevance Scoring 25:47: Outro