Retrieval-Augmented Generation (RAG) is a cutting-edge approach in NLP and AI. Llama_Index, an innovative framework, facilitates the ingestion, structuring, and access of private or domain-specific data for building RAG applications. The framework is used in various tools and platforms, including LangChainAI, DataStax, and MistralAI. Several organizations and individuals are actively involved in developing and promoting RAG-based AI applications, offering courses, tutorials, and practical projects. The RAG technique is continuously evolving, with new advanced techniques and features being introduced, such as step-wise agent execution and multimodal RAG with LlamaIndex and Neo4j.
New RAG technique alert 🚨 We’ve come up with an advanced RAG technique in @llama_index that lets you ask structured questions over many documents ✨: 1. Model each document as a metadata dictionary - store more attributes beyond a simple text summary. (e.g. a row in SQL… https://t.co/F72ZdY1nVa https://t.co/z173g2Xj3w
If you are building a RAG-based tool, you will need to build a good search engine. I've been building search engines before and after LLMs happened. They are challenging to build but well worth it because research indicates that LLMs can be improved with external knowledge in a… https://t.co/06skHoKKQa
Ever heard of @llama_index? 🦙 This innovative framework, created by @jerryjliu0 and @disiok, is a game-changer for developers looking to integrate custom data sources with LLMs.🧠💡 https://t.co/IhZ1dHK9qc
Explore the cutting-edge world of Retrieval Augmented Generation with LlamaIndex! 🦙 Dive into the details on the link below: https://t.co/ByuWVnXSiD #AI #NLP #TechInnovation https://t.co/0gsqpeSp8t
Not sure about using retrieval augmented generation (#RAG) for your #LLM applications? Get your technical questions answered in our Q&A post — from guidance on when to fine-tune an LLM vs. use RAG to tips on how to secure your data. 👇 https://t.co/MjMLVLlD2x
Multimodal RAG with LlamaIndex + @neo4j 🖼️🔎 Here’s a brand-new blog post by @tb_tomaz ✨ showing how you can index and query over multi-modal Medium articles (with images and text) using @llama_index multi-modal + @neo4j vector store capabilities 📚. Get back both image and… https://t.co/3jGuO1YH7L
LLamaIndex now integrates OpenRouter natively! It's a leading framework for retrieval-augmented generation (RAG), letting you easily use AI to work with your own data. Here's what it looks like to set up Mixtral with @llama_index: https://t.co/fLB2tpqmm6 https://t.co/e2K0fkkRNL
What we’re building 🏗️, shipping 🚢, and sharing 🚀this week: @LangChainAI's LangSmith! If you want to learn strategy and tactics for improving LLM applications in production, join us! RSVP: https://t.co/sMQxo8k27c #llms #production
Our co-founder and ML veteran, @doppenhe, shares some insights 💡 in the product challenges of building accurate and reliable LLM apps. https://t.co/PlglhvbNNb
Knowledge retrieval can make or break a generative AI solution. Retrieval Augmented Generation (RAG), which separates the knowledge retrieval from the generation process, can be key to ensuring great retrieval quality. Learn more about RAG in our blog: https://t.co/1ajNW8tx9b
Building Human-in-the-Loop, Advanced RAG 🗣️💬🤖 Our latest feature lets you add step-wise feedback for complex query executions over a RAG pipeline. Context: A key step for advanced RAG is to build an agentic layer capable of handling complex questions over your data. But an… https://t.co/1lPej2olap
Retrieval Augmented Generation (RAG) for Production with LangChain & LlamaIndex Course Introducing our new comprehensive course on Retrieval Augmented Generation (RAG), brought to you by @activeloopai, @towards_AI, and the Intel Disruptor Initiative! https://t.co/K38ZCPB8HI https://t.co/130xZC0QeB
We just launched our new Advanced Retrieval Augmented Generation (RAG) course with @LangChainAI & @llama_index! 20+ free lessons and practical projects on building RAG apps for production by @towards_AI, @activeloopai, @intel Disruptor, & @llama_index. What's in store? https://t.co/L5iOd82P3o
What is Retrieval-Augmented Generation (RAG)? We will discuss: - The origins of RAG - The LLM's limitations that it tries to fix - Its architecture - Why is it so popular Bonus: A list of open-source tools for RAG implementation! https://t.co/8pPH0qLV3q
Just launched: Advanced Retrieval Augmented Generation course with @LangChainAI & @llama_index! 40+ free lessons and practical projects on building RAG apps for production by @activeloopai, @towards_AI, @intel Disruptor, & @llama_index. What's in store? https://t.co/bRPXYiwJsO
Build and scale LLM applications within minutes using the @AbacusAI platform — 20x cheaper than #GPT4! • Set up and scale RAG applications • Pick any open-source LLM to power your RAG apps • Customize chunking, embedding, and retrieval strategies See: https://t.co/YqUp4GNhqH https://t.co/7e5wOdssjo
Join us to learn about Retrieval Augmented Generation (RAG) as we work through building a generative question answering system using RAG from start to finish. 📌 Date: 21st December | 10:30 am ET https://t.co/ZTfr3Lh2pR https://t.co/IGGRn27NJ2
Introducing GPT-RAG: A novel Machine Learning Library developed by Microsoft, designed to offer an enterprise-grade reference architecture for seamlessly deploying Large Language Models (LLMs) using the Retrieval-Augmented Generation (RAG) pattern on Azure OpenAI. This… https://t.co/dWQ7YwWyS1
"Neural Search" - Vectara Founder, Amr Awadallah @ KM World, Enterprise. Understand why Retrieval Augmented Generation (RAG) wins over Fine-tuning via 8 reasons... https://t.co/VXI0XIT7KE via @YouTube
This two-part tutorial series will walk you through the steps in implementing Retrieval Augmented Generation (RAG) based on Amazon Bedrock, Amazon Titan, and Amazon OpenSearch Serverless. https://t.co/DxyIIZqZcS #ArtificialIntelligence #TechNews
Here's a great video by @mesudarshan on how to use the new @MistralAI APIs with LlamaIndex: both the LLM variants and embedding model. https://t.co/AtbL6eb4cV
There's still a lot of challenges in building advanced RAG. This article by @chiajy2000 captures some of the core components. Check it out below! 👇 - Query Understanding / Contextualization - Synthesizing over context without hallucinating - Latency/Performance - Privacy/Cost… https://t.co/DILy6S9Gq2 https://t.co/K5Vy99POPA
Short on time but eager to learn about retrieval augmented generation (#RAG)? Our 101 blog series breaks down the basics of a RAG pipeline and highlights the benefits for #LLM application developers. Dive into part 1 👇 https://t.co/Ory0K9AJSM
4 Key Tips for Building Better LLM-Powered Apps https://t.co/TvwccbMgds https://t.co/JBmEXOxNhZ
One of the most comprehensive overviews of advanced RAG architectures Granted, that @llama_index has most of them built in natively Might switch there as lib to go https://t.co/77KZVPP2mP https://t.co/IFisqA9CIA
Today I’m excited to introduce a brand-new capability in @llama_index: step-wise agent execution. 🤖👣 We’ve written extensively on how agentic reasoning can significantly improve the capability of your RAG pipeline. But they’re oftentimes unreliable, and it’s hard to tell… https://t.co/ZTyM1KVanb https://t.co/cKdFpqghW8
🤖 From this week's issue: An article that dives into how different document loaders in LangChain impact a Retrieval Augmented Generation (RAG) system. https://t.co/jCuvvYAd8U
The cool thing about @OLLAMA is that it's the easiest command line llm I've found. I strongly prefer to use rather than build computer tools. I can build if I have to. LLMs are just an incredible tool. https://t.co/wzCfBk0sjy
Just published in @towards_AI an illustrated overview of the Advanced RAG techniques, covering all stages of your RAG pipeline, referenced with @llama_index implementations. You can chat with the collection of references in iki: https://t.co/MZaMJ5hBUX https://t.co/H877tw0PTj https://t.co/TIJlaCVJd4
Retrieval-augmented generation (RAG) is a cutting-edge approach in NLP and AI. Badrul Sarwar, a machine learning scientist, shares his tips. https://t.co/8HNB7NKjii #AI #LargeLanguageModels #MachineLearning #NLP #LLMs
When building retrieval-augmented generation (RAG) systems, it’s critical to provide the most important context to the generative model to best respond to the user inputs. Nils Reimers explains how Embed v3 applies to RAG use cases. https://t.co/ksiWJZKkpd
RAG: no-code edition We’re excited to feature EmbedAI (by @matchaman11) - a no-code platform for building RAG over your data sources (web pages, PDFs, Notion), uses @llama_index under the hood. Our latest blog post highlights its data integrations with different services, and… https://t.co/2HWop3GzqI
Creating AI applications with RAG involves resolving issues in using vector databases for context enhancement in LLM prompts to improve results. https://t.co/YMzmXmft9c #AI #DataScience @DataStax
LLMWARE is a unified, open, extensible framework for LLM-based application patterns including Retrieval Augmented Generation (RAG). Github: https://t.co/esZBxYbKUD
What is RAG? Retrieval Augmented Generation is three intimidating words for newcomers to the world of ChatGPT and LLMs. Today, @tonykipkemboi and I coined an acronym live at @streamlit. BOWS: Better Output with Search. Thanks @CarolineFrasca! Very fun! https://t.co/D0L6nmAAhq
✨ Demystifying RAG apps with LlamaIndex! https://t.co/8wqmWIWcgA
.@Mozilla has launced an #opensource project called llamafile, which is a way to run an #LLM on your own computer. https://t.co/sy3GH7pdrb https://t.co/SS623MmkGW
🌟 Join our AMA on "Building LLM-Powered Apps" - Dec 20, 11 AM ET! Dive into designing, experimenting, & evaluating LLM-based apps. Get the chance to gain expert insights and win prizes! 📣: @dk21, @ayushthakur0, @ParamBharat & Anish Shah RSVP 👉 https://t.co/7pqMUijONF https://t.co/KSco71vDkJ
Use the @AbacusAI Retrieval Augmented Generation (RAG) platform with fine-tuned #LLMs, and start chatting with your #KnowledgeBase within minutes. It's at: https://t.co/jU8egNJvQA ——— #AI #GenerativeAI #MachineLearning #DeepLearning #Chatbot #BigData #DataScience #DataScientists https://t.co/ZOMV5xrEIl
Build a RAG system with @LangChainAI and Clarifai! Here is a step by step walkthrough of building Retrieval Augmented System for Generative Question Answering. Check this out: https://t.co/enJ0ydymGd https://t.co/erf3OdXo1g
Excited to learn more about retrieval APIs and RAG at this @abacusai workshop next Tuesday: https://t.co/EyLh4neRgX
🎁Enter to win airpods & bonus prizes in our #30DaysOfLLM contest! 🗓 Day 14/30: Unveils advanced LLM app design! Learn about embedding models, vector databases, and innovative architecture. Enhance your LLM apps with new search techniques. ✔️Register: https://t.co/bvJFsMHgf6… https://t.co/jVzy9hCJ73
Best-performing search and retrieval-augmented generation (RAG) systems require the top accuracy of retrieved results. Using embedding models optimized for search accuracy in real-world data scenarios makes all the difference. We measure the search accuracy of Embed v3 on the… https://t.co/zxr4Wejt0s
How can you evaluate the performance of your RAG applications? @helloiamleonie proposes RAGAs and explains how to implement the framework effectively. https://t.co/5XcpEIfzdn
Building RAG-based AI Applications with DataStax and Fiddler https://t.co/bQYtcatMBn from @DataStax by @gstachni
🦙@Llama_Index is a data framework tailored for #LLM applications. It facilitates the ingestion, structuring, and access of private or domain-specific data. Learn how to use the LlamaIndex integration to build #RAG applications with #Zilliz Cloud. 👉 https://t.co/MWeYgBFYnr https://t.co/5kr5iZQVND
Looking to build, ship, and share awesome LLM applications with the latest tools? Start here 👇 https://t.co/UPpeUzFIoq
🤖 From this week's issue: A blog post on how to improve the performance of your Retrieval-Augmented Generation (RAG) pipeline with certain “hyperparameters” and tuning strategies. https://t.co/f0rlwvyKlf