LlamaIndex, a tool for Retrieval Augmented Generation (RAG) systems, has been gaining attention in the tech community. It offers a production ETL pipeline for RAG/LLM apps, enabling the indexing of thousands of documents in seconds. The tool is being scaled to handle thousands of documents and is integrated with other popular components. Additionally, there are blog posts and courses available to help users understand and implement RAG systems using LlamaIndex and other open-source LLMs. The tool also integrates with Zilliz Cloud Pipelines, providing a fully-managed retrieval service for RAG apps.
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
Retrieval Augmented Generation. https://t.co/tRhNqb07xH
I continue to be really impressed by what LlamaIndex has been able to build in the RAG space. Approaching IMO a one-stop shop for flexible, off-the-shelf search workflows for LM application developers. Impressive work, @jerryjliu0 and team! https://t.co/hJ1CmnpSC2
I continue to be really impressed by what LlamaIndex has been able to build in the RAG space. Approaching IMO a one-stop shop for flexible off-the-shelf search and augmentation workflows. Impressive work, @jerryjliu0 and team! https://t.co/hJ1CmnpSC2
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
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
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
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
If you have a GPU, then this special blog post by @bentomlai is a perfect resource to deploy a fully open-source LLM + RAG pipeline. Use OpenLLM + vllm to serve any of the latest open-source models (Mistral, Yi, Llama) 1️⃣ behind an API, and 2️⃣ blazing fast ⚡️ With some extra… https://t.co/ExkjLac1Pl
Easily host any open-source LLM in production with OpenLLM from @bentomlai! Big commercial models can get pricey and might not be the best fit for your use case. But you can build a retrieval-augmented generation application using LlamaIndex on top of any model out there using… https://t.co/JHYfgyTb8U
Dive into the synergy of OpenLLM and @llama_index in our latest blog post! Leverage the power of open-source LLMs and learn to build RAG systems that understand and respond accurately to your custom dataset. 🔗 https://t.co/hRFO7lK80Z
RAG with LLMS seems quite simple but is quite difficult to pull off. Creating a ChatGPT-like tool with a custom knowledge base requires multiple non-trivial components: you need a semantic understanding of the query and a full-scale search engine for the “retrieval.” We at… https://t.co/c5jzcpEN6K
9 Effective Techniques To Boost Retrieval Augmented Generation (RAG) Systems - ReRank, Hybrid Search, Query Expansion, Leveraging Metadata, and more… by @ahmed_besbes_ https://t.co/JGkT81ZqZm
Scaling @llama_index to Thousands of Documents 📈 Productionizing RAG involves deploying core components like data ingestion into a backend architecture - this can be way more complex than building RAG in a notebook. Our new `llamaindex_aws_ingestion` repo is a perfect… https://t.co/6j0r5elYOe https://t.co/vfQYxfds0K
Today we’re launching a repo that lets you setup a production ETL pipeline for your RAG/LLM app 💫 Index thousands of documents in seconds ⚡️ (and orders of magnitude faster than running on your laptop). It’s a full architecture which bundles LlamaIndex with other popular… https://t.co/lIXcCZB6bb