kb-bench

RAGFlow 0.26.4

A document-understanding-first platform built around deep parsing, deployed and measured against the same corpus as every other platform here.

In one paragraph

Deployed from its published image, RAGFlow runs 5 containers and held 10,326 MB at peak during ingestion. It retained 80% of the facts planted in the corpus and answered 45% of the retrieval questions correctly.

Measured

MeasurementResultHow
Time to first served request 511 s Includes image pull, from a clean host
Containers 5 Running after the stack settles
Idle memory 8,517.3 MB Sum across containers, 60s after ready
Peak memory during ingestion 10,326 MB Sampled every 5s across the whole corpus
Documents ingested 11/11 Failures counted, not excluded
Chunks stored 393 Across the whole corpus
Parsing fidelity 80% Planted facts found in stored chunks
Retrieval accuracy 45% Answer present in top-5 context, 29 questions
Retrieval latency 451.6 / 553 ms p50 / p95, CPU embedding
Licence Apache-2.0 Permits offering it as a service

What it does well

Where it falls short

Choose it when

Choose something else when

Deployment notes

Five containers is the smallest count among the purpose-built platforms, and it is also the most memory-hungry deployment measured — the two figures point in opposite directions and both are worth knowing. Elasticsearch is the reason.

Driving it

Three surfaces have to be understood before anything works: an RSA-encrypted login, a provider-then-instance model registration flow that replaced the older single call, and a two-step ingestion where upload and parse are separate. None of that is difficult once known, but none of it is discoverable from the API documentation alone.