Tự Tạo Chatbot AI Với Dữ Liệu Của Bạn | RAG Made Simple

About this video

• Video Title: Tự Tạo Chatbot AI Với Dữ Liệu Của Bạn | RAG Made Simple
• Channel: Timi
• Speakers: Timi
• Duration: 00:48:48

Overview

This video explains and demonstrates how to build a custom AI chatbot using your own data through Retrieval Augmented Generation (RAG). The presenter, Timi, walks through the fundamental concepts of RAG, including keyword matching and vector embeddings, and then applies these concepts to a practical project. The tutorial covers data preparation, using embedding models, constructing a RAG pipeline, and testing/improving performance, with a focus on making AI accessible and understandable.

Key takeaways

• RAG Fundamentals: Retrieval Augmented Generation (RAG) enhances AI chatbot accuracy by incorporating internal data into prompts, combining retrieval and language generation.
• Keyword Matching vs. Vector Embeddings: The video contrasts basic keyword matching with the more sophisticated approach of vector embeddings, which capture the semantic meaning of text.
• Project Walkthrough: A practical example is used to build a chatbot for a Korean study abroad consultancy, demonstrating data loading, chunking, embedding, and querying.
• Tools and Technologies: Key tools mentioned include OpenAI's embedding models, Chroma for vector storage, and LangChain for orchestrating the RAG pipeline.
• Pipeline Stages: The process involves preparing data, applying vector embeddings, building a RAG pipeline (language model, retriever, memory), and testing for performance improvement.
• Improving Accuracy: The importance of clear system prompts and strategies for debugging and optimizing RAG performance are highlighted, such as adjusting chunking strategies and using hybrid retrievers.