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RAG is dead. long live RAG — index, generate embeddings and start querying in just a few steps with the new @cloudflaredev autoRAG

rita kozlov 🐀

12,473 subscribers

108,052 görüntüleme • 1 yıl önce •via X (Twitter)

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11 Yorum

Vikash Rathee profil fotoğrafı

Vikash Rathee1 yıl önce

I don’t see an option to limit the AI search to subsets of documents. No such feature? For example, I can store 10 customers docs on R2 for my app to query as needed. /client1/a1.txt /client1/a2.txt /client2/x1.txt /client2/x2.txt { prefix: client1/} )

RTTS profil fotoğrafı

RTTS1 yıl önce

API testing of interfaces is critical to determine if they meet requirements for functionality, reliability, performance, and security. Check out RTTS - the automated testing experts since 1996. #API #testautomation #integrationtest

Youssef profil fotoğrafı

Youssef1 yıl önce

@CloudflareDev awesome! it would be great if you could add an option to re-rank the results using a reranking model, and also include metadata filters.

Mohammed Zeeshan profil fotoğrafı

Mohammed Zeeshan1 yıl önce

@CloudflareDev this is so cool! I have a small vending products website built on Pages. I am wondering if i could make a small chatbox for website visitors so they can chat and ask which vending machine suits for their requirement!

rita kozlov 🐀 profil fotoğrafı

rita kozlov 🐀1 yıl önce

@CloudflareDev check out the agents starter too

Pramod profil fotoğrafı

Pramod1 yıl önce

@CloudflareDev I am building CrawlChat and I am not dead! The real world use cases are beyond this but definitely things are changing rapidly

Jorge Colon profil fotoğrafı

Jorge Colon1 yıl önce

@CloudflareDev Man I really love how y’all are releasing some awesome products. For the longest time I’ve been a Cloudflare fan.

Moes profil fotoğrafı

Moes1 yıl önce

@CloudflareDev Actually cooking!! 🔥

Humberto profil fotoğrafı

Humberto1 yıl önce

@CloudflareDev great. i'm a bit out of my depth, but does it work if you want to generate a rag for a end-user only context? lets say i have any number of users, and I want the autoRAG to match only *their* content. would this work and how? reading the docs i didn't get it if its supported..

Samad Ahmed profil fotoğrafı

Samad Ahmed1 yıl önce

@CloudflareDev Saves a ridiculous amount of time, this is great

Nicholas Griffin profil fotoğrafı

Nicholas Griffin1 yıl önce

@CloudflareDev Oh well this looks awesome, I’m off to give it a go.

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cleaning Impulse Labs — since people asked. I just use a wet rag with soapy water and then a dry rag.

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Sam D'Amico

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We saw exactly this in my previous startup RAG + vector DB gives decent results, but agentic search over the repo (glob/grep/read, etc) consistently worked better on real-world codebases. We even pushed further: RAG + embeddings + AST + tree-sitter. The quality was excellent But exactly as Boris Cherny mentions: staleness and privacy, you need continuous re-indexing, and all the code and embeddings must live on your servers. In practice, fast models + bash-style agentic search ended up outperforming general RAG search, even if it requires more tool calls This is what we built 👇

We saw exactly this in my previous startup RAG + vector DB gives decent results, but agentic search over the repo (glob/grep/read, etc) consistently worked better on real-world codebases. We even pushed further: RAG + embeddings + AST + tree-sitter. The quality was excellent But exactly as Boris Cherny mentions: staleness and privacy, you need continuous re-indexing, and all the code and embeddings must live on your servers. In practice, fast models + bash-style agentic search ended up outperforming general RAG search, even if it requires more tool calls This is what we built 👇

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Our own Laurie Voss spoke yesterday with Ankit Khare on w3shi i's webinar about (what else?) RAG: ➡️ Why we do RAG ➡️ How we do RAG ➡️ How LlamaIndex helps you do RAG ➡️ What's coming in 2024 We recommend checking out the full hour-long webinar on Youtube, tons more information and insights:

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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

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Andrew Ng

124,458 görüntüleme • 1 yıl önce

btw literally everybody is reporting that Agentic RAG is beating “trad RAG” by leaps and bounds its probably the #1 most cited result in RAG after “have you tried BM25” from Merrill Lutsky on the Raza Habib pod

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swyx

30,551 görüntüleme • 1 yıl önce

LLMs can make sense of retrieved context because of how transformers work. In one of the lessons from the Retrieval Augmented Generation (RAG) course, we unpack how LLMs process augmented prompts using token embeddings, positional vectors, and multi-head attention. Understanding these internals helps you design more reliable and efficient RAG systems. Watch the breakdown and keep learning how to build production-ready RAG systems in this course, taught by Zain:

LLMs can make sense of retrieved context because of how transformers work. In one of the lessons from the Retrieval Augmented Generation (RAG) course, we unpack how LLMs process augmented prompts using token embeddings, positional vectors, and multi-head attention. Understanding these internals helps you design more reliable and efficient RAG systems. Watch the breakdown and keep learning how to build production-ready RAG systems in this course, taught by Zain:

DeepLearning.AI

11,500 görüntüleme • 11 ay önce

Traditional data pipelines don't work for RAG applications. There are 3 issues with them: 1. Traditional data engineering solutions are optimized to handle structured data. RAG applications rely primarily on unstructured data. 2. The connector ecosystem to load data from unstructured data sources is very immature. 3. Traditional solutions do not offer any way to transform unstructured data into an optimized vector search index. The goal of a RAG Pipeline is to solve these problems. The number one objective is to create a reliable vector search index using factual knowledge and relevant context. This sounds easy, but it's one of the biggest challenges we face when building RAG applications. At a high level, there are four different stages in the architecture of a RAG pipeline: 1. Ingestion: Here is where the pipeline loads the information from the data source. 2. Extraction: Where the pipeline processes the input data and decides how to retrieve the text contained inside them. 3. Transform: Where the pipeline chunks the data and generates document embeddings. 4. Load: Where the pipeline creates a search index in a vector database and loads the document embeddings. There are different rabbit holes at each one of these stages. Here are three of them: 1. Ingesting data once is simple. The hard part is refreshing the vector database whenever the original data source changes. 2. Extracting the content of a plain text document is simple. The hard part is to extract content from complex documents containing tables, images, or cross-references. 3. A simple continual chunking strategy with an overlap is simple. The hard part is to find the optimal strategy for your specific knowledge base and the way you are planning to query it. In the attached video, I'll show you how you can build an enterprise-grade RAG Pipeline that solves every one of the above problems. I'll use Vectorize. They partnered with me on this post. You can use them to build RAG pipelines optimized for accurate context retrieval. If you have a few documents lying around, set up a free account and give it a try.

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OKZOO

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𝕶𝖆𝖑𝖊𝖎𝖉𝖔𝖘𝖈𝖔𝖕𝖊 𝕰𝖞𝖊𝖉 𝕲𝖎𝖗𝖑

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