agentset

https://agentset.ai/ Open-source RAG infrastructure.Every team I talk to has the same experience: RAG works in the demo, breaks in production. We handle ingestion through retrieval with optimizations baked in. 97.9% on HotpotQA vs 88.8% for standard RAG. Model-agnostic, 22+ file types, built-in citations, MCP server. MIT licensed. https://github.com/agentset-ai/agentset

I'm building https://github.com/agentset-ai/agentset, RAG as a service that works quite well out of the box. We achieve this performance by baking in the best practices before any tweaking

I'm not sure there is a chunker in this repo. The file you linked certainly doesn't seem to perform any chunking, it just defines a data model for chunks. The only place I see that actually operates on chunks does so by fetching them from Redis, and AFAICT nothing in the repo actually writes to Redis, so I assume the chunker is elsewhere. https://github.com/agentset-ai/agentset/blob/main/packages/j...

They say the chunker is the most important part, but theirs looks rudimentary: https://github.com/agentset-ai/agentset/blob/main/packages/e... That is, there is nothing here that one could not easily write without a library.

Hi HN, We built one of the largest RAG set-ups that exist toady with Usul.ai (6B tokens). We started by using langchain and llamaindex, they were able to get us to a prototype in a couple of days, but took 3 months of taking pieces apart and optimizing them to make it perform well at such large scale. We put all of these learning into an MIT licensed open-source project — Agentset. Our goal to let people get production quality RAG w/o having to understand or optimize the underlying pieces. It supports 22 file formats, agentic search, deep research, citations, and a UI out of the box. Happy to answer any questions about our journey or the product.

Working on an open source RAG-as-a-service platform that bakes in the best practices and continues to evolve them so that customers get the best retrieval without having to go deep or stay up to date. Our flagship feature is Agentic RAG, which is quite difficult to build from scratch. https://agentset.ai https://github.com/agentset-ai/agentset