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Explore Is RAG (Really) Dead? | RAG with Long Context LLMs | @LangChain x Timescale with 2,813 views, 43 likes, and 6 comments. Experience the impact of this video content that has captured audience attention.

With the advent of Large Language Models with context windows of 1 million tokens, many have declared the utility of retrieval augmented generation (RAG) dead and no longer necessary. But is this actually true? Or simply short-sighted? ​Join Timescale and special guest presenter Lance Martin, engineer at @LangChain for a deep dive into the present and future of RAG with long text LLMs. 🛠 𝗥𝗲𝗹𝗲𝘃𝗮𝗻𝘁 𝗥𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 📌 Free trial of Timescale Vector ⇒ https://tsdb.co/webinar-signup 📌 Presentation slides ⇒ https://tsdb.co/is-rag-dead-slides 📌 Twitter thread with paper links ⇒ https://tsdb.co/is-rag-dead-papers 📌 Getting started with LangChain and Timescale Vector tutorial ⇒ https://tsdb.co/langchain-tutorial 🐯 𝗔𝗯𝗼𝘂𝘁 𝗧𝗶𝗺𝗲𝘀𝗰𝗮𝗹𝗲 Timescale a mature cloud PostgreSQL platform engineered for demanding workloads like time-series, vector, events and analytics data. 💻 𝗙𝗶𝗻𝗱 𝗨𝘀 𝗢𝗻𝗹𝗶𝗻𝗲! 🔍 Website ⇒ https://tsdb.co/homepage 🔍 Slack ⇒ https://slack.timescale.com 🔍 GitHub ⇒ https://github.com/timescale 🔍 Twitter ⇒ https://twitter.com/timescaledb 🔍 Twitch ⇒ https://www.twitch.tv/timescaledb 🔍 LinkedIn ⇒ https://www.linkedin.com/company/timescaledb 🔍 Timescale Blog ⇒ https://tsdb.co/blog 🔍 Timescale Documentation ⇒ https://tsdb.co/docs 📚 𝗖𝗵𝗮𝗽𝘁𝗲𝗿𝘀 00:00 Introduction 03:08 What is RAG? 03:52 Needle in a Haystack 08:15 RAG isn't dead. But it will change. 10:10 Documents as a minimum retrieval unit 11:32 Representation Indexing 13:06 RAPTOR - Questions that reference many documents 15:14 Reasoning: Self RAG 17:02 Summary 18:23 Question and Answer (Variety of Topics) 18:39 Cost of long context models 21:52 Needle in a Haystack in different LLMs 24:10 Self-RAG Eval Metrics 25:45 Self-RAG Latency 27:12 Semantic Utility of Long Context Embeddings 28:40 Testing Methods for RAG Pipelines 31:07 RAG Use Cases 37:31 Public Benchmarks for RAG 39:17 Improvements to Needle in a Haystack 41:53 Number of needles and performance 43:13 RAG and Few Shot Training 45:39 RAFT: Retrieval Aware Fine Tuning

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