Chunking in RAG (with hands-on in LangChain and LlamaIndex) - RAG video series скачать в хорошем качестве

Chunking in RAG (with hands-on in LangChain and LlamaIndex) - RAG video series

Retrieval Augmented Generation or RAG is becoming the go-to approach to address the shortcomings of LLMs like hallucinations and model training cut-off. In the video series on RAG, this video is about chunking the input text to be ingested into the Vector DB used in the RAG pipeline. Hope it's useful. ⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️ 0:00 - Intro 0:13 - RAG refresher 1:04 - Ingestion in RAG 1:27 - What is Chunking? 2:05 - Why Chunking? 4:06 - Fixed-Size Chunking 7:15 - Recursive Chunking 10:18 - Document / Code Chunking 12:17 - Semantic Chunking 16:40 - Conclusion RELATED LINKS Introduction to RAG -    • Retrieval Augmented Generation (RAG) expla...   Building RAG app using LangFlow -    • Build a RAG app using LangFlow + @streamli...   RAG paper - https://arxiv.org/abs/2005.11401 🛠 🛠 🛠 MY SOFTWARE TOOLS 🛠 🛠 🛠 ✍️ Notion - https://affiliate.notion.so/aibites-yt ✍️ Notion AI - https://affiliate.notion.so/ys9rqzv2vdd8 MY KEY LINKS YouTube:    / @aibites   Twitter:   / ai_bites​   Patreon:   / ai_bites​   Github: https://github.com/ai-bites​ WHO AM I? I am a Machine Learning researcher/practitioner who has seen the grind of academia and start-ups. I started my career as a software engineer 15 years ago. Because of my love for Mathematics (coupled with a glimmer of luck), I graduated with a Master's in Computer Vision and Robotics in 2016 when the AI revolution started. Life has changed for the better ever since. #machinelearning #deeplearning #aibites