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Ana Sayfaya Dön

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

swyx

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30,551 görüntüleme • 1 yıl önce •via X (Twitter)

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Rahul Dave profil fotoğrafı

Rahul Dave1 yıl önce

@corbtt @MerrillLutsky @RazRazcle Add one more reporter. Financial documents.

swyx 🔜 @aiDotEngineer (Jun 3-5) profil fotoğrafı

swyx 🔜 @aiDotEngineer (Jun 3-5)1 yıl önce

@corbtt @MerrillLutsky @RazRazcle wdym

Rainmaker profil fotoğrafı

Rainmaker2 yıl önce

Here I share an XGBoost model that delivers a 25% CAGR with minimal drawdown on Visa stock. In this free Substack post I share code and commentary for a powerful Machine Learning strategy that delivers powerful returns.

Janaka Abeywardhana profil fotoğrafı

Janaka Abeywardhana1 yıl önce

@MerrillLutsky @RazRazcle Hold that orig tweet implies agentic RAG does use semantic search???

Jo Kristian Bergum profil fotoğrafı

Jo Kristian Bergum1 yıl önce

@MerrillLutsky @RazRazcle I had a meeting this week with founders of an AI support bot that said the same thing

Sherwood 💬 profil fotoğrafı

Sherwood 💬1 yıl önce

@MerrillLutsky @RazRazcle A wild @MerrillLutsky appears

Ak profil fotoğrafı

Ak1 yıl önce

@MerrillLutsky @RazRazcle Building a hosted version of this at !

sorta_sota profil fotoğrafı

sorta_sota1 yıl önce

@MerrillLutsky @RazRazcle how can I get snipd to export like that

Raza Habib profil fotoğrafı

Raza Habib1 yıl önce

@MerrillLutsky The first team I saw do this was actually @bloopdotai almost 2 years ago now

swyx 🔜 @aiDotEngineer (Jun 3-5) profil fotoğrafı

swyx 🔜 @aiDotEngineer (Jun 3-5)1 yıl önce

@MerrillLutsky @bloopdotai i wonder if theres a canonical paper bc mine is the jerry liu talk

Christian Nonis profil fotoğrafı

Christian Nonis1 yıl önce

@MerrillLutsky @RazRazcle I am team graph rag

Benzer Videolar

I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.

I'm excited to share my talk on building Agentic RAG systems at last week's event with Arize AI and Google! 🥳 My talk covered: 1. The differences between Vanilla RAG and Agentic RAG 2. The agent ecosystem and how you can build agents today 3. How Weaviate AI Database is building agents with Generative Feedback Loops (GFLs) This snippet presents the multi-agent paradigm for Agentic RAG. I hope you find this talk interesting and would love to know what you think! The slide deck and full video links are below.

Erika Shorten

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R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

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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

This talk by Ben Clavié is the highest value per second talk I have ever watched on RAG Chapter summaries and additional links in next tweet

This talk by Ben Clavié is the highest value per second talk I have ever watched on RAG Chapter summaries and additional links in next tweet

Hamel Husain

173,399 görüntüleme • 2 yıl önce

Let's build a dashboard to evaluate and monitor your Agentic and RAG apps! . . In this video, I'll guide you through creating an evaluation and observability pipeline for your AI apps using a 100% open-source tool! Tech Stack: - Comet's Opik to eval and monitor - LlamaIndex to build a RAG pipeline - Ragas for synthetic datagen You'll learn: - Setting up Opik - Building a RAG pipeline - Creating an eval dataset - Evaluating the RAG pipeline - Monitoring all activities during the process It's a hands on demo with code and step-by-step guide to do everything listed above. CometML's Opik is fully open-source, offering the most Pythonic and easiest way to monitor LLM apps. I have shared link to their repo in next tweet!

Let's build a dashboard to evaluate and monitor your Agentic and RAG apps! . . In this video, I'll guide you through creating an evaluation and observability pipeline for your AI apps using a 100% open-source tool! Tech Stack: - Comet's Opik to eval and monitor - LlamaIndex to build a RAG pipeline - Ragas for synthetic datagen You'll learn: - Setting up Opik - Building a RAG pipeline - Creating an eval dataset - Evaluating the RAG pipeline - Monitoring all activities during the process It's a hands on demo with code and step-by-step guide to do everything listed above. CometML's Opik is fully open-source, offering the most Pythonic and easiest way to monitor LLM apps. I have shared link to their repo in next tweet!

Akshay 🚀

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I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

I present to you my AI automation that converts your Google Drive folder to RAG vector database. It also updates the RAG vector database whenever a new file is added or deleted from the Google Drive folder. You can download the workflow for free (link in comment)

Victor

261,491 görüntüleme • 1 yıl önce

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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ℏεsam

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This video is from a pro-Maduro pro-dictatorship rally in Australia organised by Socialist Alliance and attended by some other rag-tag ignorant losers. Meanwhile, Venezuelans around the global are celebrating in the streets.

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🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model in its intelligence tier. Learn more:

🚀 Amazon Nova Premier, our most capable teacher model for creating custom distilled models, is now available on Amazon Bedrock! Built for complex tasks like Retrieval-Augmented Generation (RAG), function calling, and agentic coding, its one-million-token context window enables analysis of large datasets while being the most cost-effective proprietary model in its intelligence tier. Learn more:

Amazon Science

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Kristi Noem dismisses reporting about Hegseth murdering people at sea: "That entire story is based on anonymous sources. We see the press and that rag use anonymous sources all the time to print things that aren't true, that are lies, that are completely not based in reality."

Kristi Noem dismisses reporting about Hegseth murdering people at sea: "That entire story is based on anonymous sources. We see the press and that rag use anonymous sources all the time to print things that aren't true, that are lies, that are completely not based in reality."

Aaron Rupar

42,205 görüntüleme • 7 ay önce

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Verba is an open source Retrieval Augmented Generation (RAG) application that performs RAG on your own data. To showcase its capabilities, we've customized it as an Airbnb chatbot using Airbnb’s customer documentation. How it works: • Ask any questions, related to your booking, policies, or anything related to your Airbnb experience. • Get relevant, human-like responses: Verba provides natural and informative answers. • Access original sources: One of the standout features of RAG is its ability to directly indicate the sources it used to generate each response. Under the hood, Verba uses a RAG pipeline to deliver these exceptional results. Your query is transformed into a numerical representation (vector) and be used to search through our vector database for the most similar context using Hybrid Search. The most relevant context is then combined with your original question and fed into a powerful large language model (LLM). The LLM will then use all of that information to generate a conversational response. Et voilà! 💫 Try Verba: Verba on GitHub: Learn more in our video:

Femke Plantinga

120,565 görüntüleme • 1 yıl önce

ok so let me explain why subagents kill long context Like you can spend $500m building 100 million context models, and they would be 1) slow, 2) expensive to use, 3) have huge context rot. O(n) is the lower bound. Cog's approach is something you learn in day 1 of CS50 - divide and parallelize. Embeddings are too dumb, Agentic Search is too slow. So train limited-agency (max 4 turns), natively parallel tool calling (avg parallelism of 7-8, custom toolset) fast (2800tok/s) subagents to give the performance of Agentic Search under an acceptable "Flow Window" that feels immaterially slower than Embeddings. The benefit of this is threefold: - 8 ^ 4 toolcalls cover a very large code search space. can compound subagent calls if more needed. - predictable cost & end to end latency - subagent outputs "clean" contexts, free of context failure modes like context poisoning and context rot (h/t Drew Breunig ) we originally called this Rapid Agentic Search, to contrast with RAG. but Fast Context rolls of the tongue better. there's 2 other perspectives that are worthwhile, i'll go into below, but just go try it out. here it is on Andrej Karpathy's fastchat

ok so let me explain why subagents kill long context Like you can spend $500m building 100 million context models, and they would be 1) slow, 2) expensive to use, 3) have huge context rot. O(n) is the lower bound. Cog's approach is something you learn in day 1 of CS50 - divide and parallelize. Embeddings are too dumb, Agentic Search is too slow. So train limited-agency (max 4 turns), natively parallel tool calling (avg parallelism of 7-8, custom toolset) fast (2800tok/s) subagents to give the performance of Agentic Search under an acceptable "Flow Window" that feels immaterially slower than Embeddings. The benefit of this is threefold: - 8 ^ 4 toolcalls cover a very large code search space. can compound subagent calls if more needed. - predictable cost & end to end latency - subagent outputs "clean" contexts, free of context failure modes like context poisoning and context rot (h/t Drew Breunig ) we originally called this Rapid Agentic Search, to contrast with RAG. but Fast Context rolls of the tongue better. there's 2 other perspectives that are worthwhile, i'll go into below, but just go try it out. here it is on Andrej Karpathy's fastchat

swyx 🇸🇬

276,060 görüntüleme • 9 ay önce

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

Andrew Ng

244,284 görüntüleme • 2 yıl önce

There’s been two papers released in the past couple months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.

There’s been two papers released in the past couple months, one by Google and one by NVIDIA, that argue that ordering the documents retrieved by RAG systems can enhance performance. However, they both give two different strategies on HOW these documents should be ordered 🤔 Both papers agree on two main points: 1️⃣ There’s a fundamental issue in RAG - as more documents are retrieved, more irrelevant context (e.g., hard negatives) are introduced, which leads to confusion for the LLM and eventually degrades the quality of the generated output. This is called an inverted-U performance curve. 2️⃣ Ordering the retrieved documents is a key lever for optimizing RAG performance. Google Cloud researchers proposed ordering results based on relevance scores: The authors in this paper argue for relevance-based reordering, or ordering the retrieved chunks based on their similarity scores, so the most relevant documents are at the beginning and the end of the inputs to counter the “lost in the middle” effect. NVIDIA researchers proposed ordering results based on the original sequence of document chunks: The authors of this paper argue for Order-Preserving Reordering, or Order-Preserve RAG (OP-RAG), to maintain the logically coherent content flow of the document. So they preserved the original order of retrieved document chunks in the source text, instead of ranking them by relevance scores. So which one is right? It probably depends on the specific use case and dataset - relevance-based reordering could perform better in tasks where you need fast access to the most critical information (e.g., fact retrieval, QA systems), while order-preserving RAG might be better where you need to understand the sequential structure of information (e.g., narrative or legal documents). There are still so many uncertainties in AI - we don’t actually know what we’re doing, and it takes awhile to figure out the best strategies for most things! Excited to see more research about this.

Victoria Slocum

15,333 görüntüleme • 1 yıl 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.

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.

Santiago

40,441 görüntüleme • 1 yıl önce

Akademiks says if you don’t want a n**ga to CHEAT on you, do what Nicki Minaj did and marry someone like Big ZOO 😭 “Big ZOO is probably doing the dishes, vacuuming the spot. He traded in his red rag to be a personal assistant” 👀

Akademiks says if you don’t want a n**ga to CHEAT on you, do what Nicki Minaj did and marry someone like Big ZOO 😭 “Big ZOO is probably doing the dishes, vacuuming the spot. He traded in his red rag to be a personal assistant” 👀

Akademiks TV

2,680,055 görüntüleme • 6 ay önce

🇺🇦 33rd OSH denies capture of the village of Prydorozhnye by the enemy, Zaporizhzhia region The enemy, as always, draws his maps on credit, and then, in a desire to catch up with the "gains", sends the DRG there - so that they install a rag. The result of such "photo shoots" is one - the enemy DRG is destroyed, the flag of Ukraine flies over Prydorozhnye!

🇺🇦 33rd OSH denies capture of the village of Prydorozhnye by the enemy, Zaporizhzhia region The enemy, as always, draws his maps on credit, and then, in a desire to catch up with the "gains", sends the DRG there - so that they install a rag. The result of such "photo shoots" is one - the enemy DRG is destroyed, the flag of Ukraine flies over Prydorozhnye!

MAKS 25 🇺🇦👀

10,792 görüntüleme • 5 ay önce