The tweets discuss the emergence of Retrieval Augmented Generation (RAG) and the RAGatouille library, which facilitates the use of ColBERT, an advanced retrieval model, in large language models (LLMs). The RAG framework aims to prevent LLM hallucination by providing context through relevant document retrieval. The library simplifies the training and usage of ColBERT, enabling its integration into various applications. Companies and individuals are actively exploring and promoting the RAG framework and its applications, with a focus on improving efficiency and accessibility.
🚀New LangChain reranker ColBERT via @bclavie's RAGatouille framework https://t.co/pRZ8VqmF99
If one release today isn't enough, look at what @bclavie has built! Now you have two ways to use ColBERT in RAGatouille: (1) Index the collection in advance. Search it quickly at full scale! (2) Retrieve documents with a different method and re-rank on-the-fly with ColBERT.⤵️ https://t.co/1p5p9m1Rax
We're excited to announce an exclusive session on Retrieval-Augmented Generation (RAG) with Domino's John Alexander! Join us Feb. 13 to unlock the power of RAG for your projects. Limited spots available! 🔍 https://t.co/NxG0hFDOZj #DataScience #GenerativeAI #TechTalks https://t.co/eCfH7AL0CD
Does Retrieval Augmented Generation, or RAG, eliminate LLM hallucination? Find out in our next 1 minute explainer video. #LLMs #RAG #generativeAI #hallucination https://t.co/fUltZAH8bc
If you are working with LLM application, especially RAG systems, the major thing you would be looking for is to know what exactly is happening behind the scenes or what is the data which gets passed on to your model. With the recent launch of more stable version of @LangChainAI… https://t.co/gO7OZl8go8
Looking to take your #LLM performance to the next level with Retrieval Augmented Generation? 🚀✨ Simplify the initial stages of the #RAG workflow through AskYoda, providing an intuitive platform to effortlessly upload and manage your data.💡⬇️ https://t.co/WZcGSNeMDo #AI #API
In our next one-minute explainer video, we explain what Retrieval Augmented Generation (RAG) is. #generativeAI #LLM #chatbots #RAG https://t.co/tFFG8UaWJy
Want to understand ColBERT-V2? Curious how it compares to vanilla embedding retrieval? Check out a tiny intro I wrote up where I walk through a simple example: https://t.co/m2xZxcOH8a S/o to @lateinteraction for his work on ColBERT & @bclavie for the wonderful RAGatouille…
Want to understand ColBERT-V2? Curious how it compares to vanilla embedding retrieval? Check out a tiny intro I wrote up: https://t.co/m2xZxcOH8a S/o to @lateinteraction for his work on ColBERT & @bclavie for the wonderful RAGatouille library
So glad RAGatouille is unleashing the potential of @lateinteraction’s ColBERT! Try your own needle-in-a-haystack in seconds in @LangChainAI thanks to @hwchase17’s integration! More to come soon, stay tuned 👀 https://t.co/Fr9T5hxENK
Read about optimizing RAG efficiency with LlamaIndex ➡️ https://t.co/3TMNEkdC9X Image source: LlamaIndex https://t.co/4NTOAhDaqW
🛠️@llama_index now integrates with Zilliz Cloud Pipelines, a fully-managed retrieval service. When building RAG apps, have you ever struggled with hosting retrieval solutions? Do you feel the pain of writing working Dockerfiles, resolving conflicting dependencies, maintaining a… https://t.co/LI1tOrVszd
Over the past 4 years, we've built a *massive* stack around ColBERT: a retrieval paradigm that performs robustly on hard queries, adapts easily to hard domains, and searches billions of tokens in milliseconds. Watch closely as ColBERT becomes accessible to application builders⤵️ https://t.co/ISkrnYu7rD
🐀Improved RAGatouille <> LangChain Integration We really enjoyed @bclavie's new RAGatouille library. It makes advanced retrieval techniques (like ColBERT - more on that below) really easy to use We've worked on a tighter integration to make it *super simple* to use RAGatouille… https://t.co/5TjFz0ZFry
🧔♂️(?) ColBERT🤝🪤RAGatouille🤝🛵@vespaengine 🤝🤗@huggingface Wondering how you can use a ColBERT model to support large-scale search? Now in RAGatouille: push a ColBERT model to the 🤗 hub & export any ColBERT (local or remote) to VespaColBERT, plug&play with @vespaengine! https://t.co/hXPeZEvIhs
There was a lot of cool RAG research in the past year or two, and luckily for you, all of these efforts are tracked under one place! “Retrieval-Augmented Generation for Large Language Models: A Survey” by Gao et al. does an admirable job categorizing all RAG research into three… https://t.co/uf0U8YdgWV https://t.co/hzDALJNzgI
Say hello to a new suite of RAG tools ✨ Learn how these features can help build high-quality, production LLM apps using your enterprise data: https://t.co/gSW87Ib4Gd https://t.co/CHJSEF9fvx
👀 What is Multimodal RAG (Retrieval Augmented Generation)? @QuantumMarks, ML Engineer at @Voxel51 breaks it down for you. Check out the full conversation of how large language models (#LLMs) are transforming computer vision here: https://t.co/MZG1nLO9NY https://t.co/cqjcTnWPmO
Made a video on this showcasing how to create llm apps using the RAG framework! https://t.co/AhtVoqQfeu https://t.co/EMZqvYdHNV
Can LLM hallucination be prevented? Watch this brief video to find out. #generativeAI #customersuccess #chatbots https://t.co/NuYh9kZRW6
RAGatouille (@bclavie) is an awesome way to easily use ColBERT, a more advanced retrieval model compared to dense embedding-based retrieval techniques. In turn we’ve made it easy to use ColBERT in an e2e @llama_index RAG pipeline in one line of code ⚡️, with our brand-new… https://t.co/LENrAl6A08 https://t.co/6reMeIRxzD
With all the excitement around ColBERT today, thanks to the RAGatouille library, this is a good time to understand: • What makes ColBERT work so well? • How is it different from standard dense retriever? • How can it search 100M docs in 0.1 seconds, with no GPU? https://t.co/qMgWdwGuK1
See what RAG actually looks like in production! Press ▶️ on our workshop with @Llama_Index and learn how to build an open-source retrieval augmented generation application. @PatrickMcFadin @yi_ding #DataStax https://t.co/oNj99zAC1K
ColBERT is known for particularly strong quality, but RAG apps need more than just a good retrieval model. 🪤RAGatouille by @bclavie is the easiest & cleanest way I've seen to use ColBERT in apps—with a great roadmap too. I'll be user #1, and we'll make sure it works w DSPy. https://t.co/P3qvunX1p3
The RAG wave is here to stay, but in practice, it's hard to retrieve the right docs w/ embdings, & better IR models are hard to use! Let's fix that: Introducing 🪤RAGatouille, a lib to train&use SotA retrieval model, ColBERT, in just a few lines of code! https://t.co/VRHiGQl0Xv https://t.co/0EpOfV6UWn
New short course on advanced retrieval for RAG (retrieval augmented generation)! RAG fetches relevant documents to give context to an LLM. In Advanced Retrieval for AI with Chroma, taught by @trychroma founder @atroyn, you’ll learn: (i) Query expansion using an LLM to rewrite… https://t.co/7MHX4HT09V
1/n An ontology for hallucination mitigation techniques in Large Language Models (LLMs). Prompt Engineering category A. Retrieval Augmented Generation (RAG) - Before Generation: Strategies where information retrieval happens before text generation, e.g. LLM-Augmenter -… https://t.co/MpuchFjfM7