這堂課深入講解如何運用 RAG 技術擴充大型語言模型（如 ChatGPT）的知識來源與推理能力。你將學會 LangChain、Flowise 框架應用、RAG 管線設計、文件分割、向量索引與開源 LLM 整合等技巧，適合開發者、AI 工程師與企業應用者全面掌握 RAG 在文件問答、知識庫整合與高精度輸出中的關鍵應用。
此教程為高質量英文原版視頻教學資源，英文不好的可以使用翻譯插件學習。
=====================================================
教程名稱：RAG: Raising the Potential of ChatGPT LLMs to the next level
下載連結：https://www.nidown.com/chatgpt-423115.html
=====================================================
最便捷、最實惠的 ChatGPT Plus 升級服務來了！！！
點擊查看詳情：https://www.kkmac.com/go/chatgpt
=====================================================
英文原版介紹
Published 7/2024
Created by Data Bootcamp
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 79 Lectures ( 4h 31m ) | Size: 1.75 GB
Learn how to implement RAGs to enrich the knowledge of ChatGPT and LLMs, increasing their effectiveness and capabilities
What you’ll learn:
Introduction to Generative AI and Large Language Models
Techniques for Improving LLMs
Fundamentals of Retrieval Augmented Generation (RAG)
Applications of RAGs
Tools for the development of a RAG
Custom GPTs
Langchain
Components of the RAG
Flowise the perfect framework for the development of RAGs
Indexing Pipeline and RAG Pipeline
Document Fragmentation
Embeddings and Vector Databases
Information search and retrieval
Open-source LLMs for RAGS: the best ally for data protection and privacy
RAG performance evaluation
Requirements:
not needed
