Retrieval Augmented Generation (RAG) is a prominent topic in the world of ChatGPT and Large Language Models (LLMs). It involves using vector databases to enhance context in LLM prompts for better results. Numerous experts and organizations, including Microsoft, NVIDIA, and LangChain, are involved in developing RAG techniques and frameworks. Open-source tools and courses for RAG implementation are being launched by various entities, such as LlamaIndex, LangChainAI, and Intel Disruptor Initiative. The focus is on improving generative AI solutions by separating knowledge retrieval from the generation process. Techniques for multilingual RAG solutions and the integration of custom data sources with LLMs are also being explored.
Retrieval-Augmented Generation for Large Language Models: A Survey Provides a comprehensive overview of RAG for LLMs, outlining current paradigms, key components, evaluation methods, and future outlooks. 📝https://t.co/ffniSYnX15 👨🏽💻https://t.co/3RDbd3ieEo https://t.co/HOcffvNCxk
use @OLLAMA with @llama_index to create a completely local, open-source retrieval-augmented generation app complete with an API https://t.co/ANe2d12sHg
Ollama works well with @llama_index and friends! Run open-source models (ex. Mixtral 8x7b) with ollama + @llama_index + @qdrant_engine to create an all local retrieval augmented generation application. Tutorial here https://t.co/sLVRqgtV2V Thank you @llama_index & @seldo! https://t.co/wdFF24WYB0
Running @MistralAI's Mixtral 8x7b on your laptop is now a one-liner! Check out this post in which we show you how to use @OLLAMA with LlamaIndex to create a completely local, open-source retrieval-augmented generation app complete with an API: https://t.co/ooeg9eWK3G Bonus: see… https://t.co/WrlN35bruN
Running @MistralAI's Mixtral 8x7b on your laptop is now a one-liner! Check out this post in which we show you how to use @OLLAMA with LlamaIndex to create a completely local, open-source retrieval-augmented generation app complete with an API: https://t.co/ooeg9eWK3G (Bonus:… https://t.co/om3QRZXvSs
👉 In this blog, we are enhancing our Language Model (LLM) experience by adopting the Retrieval-Augmented Generation (RAG) approach: https://t.co/98PSsyTBDf #largelanguagemodels #llmdojo #rag #llms
RAG systems are one of the most important components to unlocking business value with LLMs. @svpino covered our course launched in collaboration with @towards_AI, @llama_index, #IntelDisruptor. Read more: https://t.co/CrJykPoOFk
building a retrieval augmented generation orchestration framework called Ragatouille.
Retrieval-Augmented Generation (RAG): From Theory to LangChain Implementation From the theory of the original academic paper to its Python implementation with OpenAI, Weaviate, and LangChain by @helloiamleonie. https://t.co/k6LhxJ14FF
Beyond English: Implementing a multilingual RAG solution - An introduction to the do’s and don’ts when implementing a non-english Retrieval Augmented Generation (RAG) system by Jesper Alkestrup https://t.co/nVcJV5lDuy
[CL] Retrieval-Augmented Generation for Large Language Models: A Survey https://t.co/o4ABxxgj2N The paper provides an overview of Retrieval-Augmented Generation (RAG) in large language models. RAG improves answer accuracy and reduces model hallucination by retrieving… https://t.co/GxEZcV2aag
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
❓When using LLMs is unsupervised fine-tuning better than RAG for knowledge-intensive tasks? Should you do both? If you want to augment an LLM with knowledge of your enterprise data you can do so by augmenting the parametric (finetune) or non-parametric(w/ a vector db like… https://t.co/oOdMM5CfMS
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
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
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
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
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
🤖 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
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
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