SciPhi

Last night we pushed out R2R `3.2.30` with a number of exciting new updates: We've added GraphRAG auto-tuning, which automatically adapts to whatever type of content you're working with - no manual prompt engineering needed. We've also introduced contextual embedding, which helps maintain context by analyzing surrounding content and finding semantically related information throughout your documents. The whole system is still designed to work well out of the box while staying configurable for those who need it. You can get started with just a few CLI commands. Links below 👇

⏰ New blog post : “Deploying RAG into Production with R2R and Unstructured”   In this blog post, we describe:      ✅ What SciPhi's R2R can enable for Production RAG systems ✅ How to use Unstructured within R2R ✅ Examples of question answering with complex tables, scanned tables, and other messy unstructured text ✅ An example of GraphRAG with R2R and Unstructured ✅ Links to the code where Unstructured is integrated into R2R's OS codebase     Give it a read at:   https://lnkd.in/gzBUzWJ5

We've made Retrieval Augmented Generation (RAG) more configurable than ever with our latest updates to R2R. Inspired by the recent and increasingly popular research development known as HybridRAG, we decided to write a blog post showcasing how it can be implemented right out of the box within R2R. We also demonstrate how you can toggle between other advanced techniques like HyDE and RAG-Fusion at query time. Find out more by reading our full blog post here: https://lnkd.in/eznuggNT

Interested in implementing RAG across document groups for your users? This cookbook demonstrates how to leverage R2R's built-in group permissions to quickly implement this. The cookbook covers creating and managing groups, adding users and documents to groups, and controlling access using R2R's group permissioning system - https://lnkd.in/e9C4c_XR

RAG & Businesses are just a match made in heaven. 💑 Recent algorithmic advancements like replacing positional encoding with ALiBi, Sparse attention and Flash Attention-2 have really extended allowable context-windows, but despite this - all the predictions of these techniques being RAG killers have turned out to be wrong. 📌 When making my most recent RAG application I stumbled onto a great open source RAG engine named R2R from SciPhi, that I found to be quite incredible. R2R seriously made life much easier for me as a developer to build, observe, and optimize RAG. 📌 Another feature that I really liked about R2R was its built-in user permissions and document management. I can see how this will make my next user facing application much easier to build. 📌 You can think of R2R as the Supabase for RAG – a complete platform that's bridging the gap between experimenting and deploying production-ready RAG applications. It even supports open-source models for my local-RAG. 📌 Part of what makes R2R easy to use is the fact that it is built around a simple RESTful API. This means I was able to deploy my application without digging into the nitty gritty details. It's also packing some serious heat with features like - Multimodal support (hello, .txt to .mp3!), - Hybrid search that combines semantic and keyword approaches, - implementing an advanced RAG technique HyDE (Hypothetical Document Embedding) and - Automatic knowledge graph generation. -------- 📌 R2R is an engine for building user-facing Retrieval-Augmented Generation (RAG) applications. It gives developers configurable vector search and RAG right out of the box, as well as direct method calls instead of the client-server architecture seen throughout the docs. The founder's vision is clear: create a tool that accelerates serious LLM application development without the pain points of existing open-source projects. R2R aims to be more opinionated about abstractions and integrations, resulting in a simple yet powerful tool. Whether you're diving into user management, craving some serious observability, or looking to extend your RAG capabilities, R2R's got your back. And for those of us who love a good UI, there's an open-source React+Next.js front-end to play with. --------- 📌 So the core workflow of R2R is the following, and you get all of these both out-of-the box, and also with a Python & Javascript SDKs so we can fully integrate it with your own codebase. - Ingest files into your Postgres vector database - Search over ingested files - Create a RAG (Retrieval-Augmented Generation) response - Perform basic user auth - Observe and analyze a deployed RAG engine. ------ 👉 Full Video - https://lnkd.in/g-FnfMa5 👉 R2R Official Doc - https://lnkd.in/gqRdw6QZ 👉 Github repo of R2R - https://lnkd.in/ggY37Wxs