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.
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📌 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.
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📌 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.
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👉 Full Video - https://lnkd.in/g-FnfMa5
👉 R2R Official Doc - https://lnkd.in/gqRdw6QZ
👉 Github repo of R2R - https://lnkd.in/ggY37Wxs