Agentset

Agentset runs parsing, chunking, embedding, retrieval, and reranking as one system, so you skip assembling and tuning each piece. The aim is that the defaults already work well enough for production rather than only for a demo. For a developer, that's the difference between adding RAG in an afternoon and adding it over a month.

Automatic citations are the default, not an add-on. Every answer points back to the chunk it came from, so users can check the source instead of trusting the model blindly. That matters most in support and internal tools, where a confident but wrong answer costs more than no answer. The citation data comes from document metadata you control.

Beyond plain text, Agentset parses images, video, and tables, including spreadsheets and tables embedded in documents. If your knowledge lives in half PDFs and half slide decks, that variety gets handled rather than dumped. The ingestion API accepts files by URL, so you can queue a batch without uploading by hand.

Connectors sync external sources like Google Drive, SharePoint, and Notion so the knowledge base stays current rather than frozen at whatever was uploaded on the day you first set everything up. That turns a one-off import into an ongoing feed. Teams that update docs constantly get the most out of it.

It's open-source RAG you can adopt without maintaining the retrieval side yourself. Agentset ships open-source client libraries for both languages, with search, ingestion, and namespace calls wrapped in a few lines. You call a namespace, run a query, and read results back as objects. No REST plumbing required if you don't want it.

You can run on Agentset Cloud, bring your own infrastructure, or deploy on-premise. The same API works across all three, so moving between them isn't a rewrite. On-premise mode is the option for teams with data residency rules that rule out a shared cloud.