Which platform is best for searching internal documentation?
Short answer
dify — Dify. It ingested every document in the corpus and retrieved the correct passage for 66% of the questions — the highest of anything measured — and it did the whole corpus in eleven minutes.
What this use case actually needs
- Mixed formats arriving continuously — PDFs, HTML exports, notes — with no control over how they are produced
- Recall matters more than exact numeric fidelity; a person reading the answer will notice a wrong figure but not a missing paragraph
- Ingestion has to keep up without someone watching it
How the candidates measured up
| Platform | Documents ingested | Parsing | Retrieval | Peak memory | Ready |
|---|---|---|---|---|---|
| dify | 11/11 | 82% | 66% | 2,572.7 MB | 39 s |
| openwebui | 11/11 | 61% | 62% | 912.9 MB | 18 s |
| ragflow | 11/11 | 80% | 45% | 10,326 MB | 511 s |
| fastgpt | 8/11 | 97% | 41% | 3,227 MB | 239 s |
Every figure comes from the same run against the same documents, so these are directly comparable. The full per-document and per-query rows are in the raw data.
Why Dify wins this one
It is the only platform that came first or second on every dimension this use case depends on:
- Ingested 11 of 11 documents, including the large SEC filings that FastGPT and AnythingLLM could not finish
- 66% retrieval accuracy, the highest measured
- 11 minutes for the whole corpus, against 25 for RAGFlow and 93 for FastGPT
- 2.6 GB peak memory, less than FastGPT and a quarter of RAGFlow
The finding underneath the result
Dify stored 1,149 chunks for the same corpus that FastGPT stored 340 chunks for — more than three times as many pieces from identical documents. That is the whole explanation for the retrieval gap.
Ranked by chunk count, the retrieval scores line up almost exactly:
| Platform | Chunks stored | Retrieval accuracy |
|---|---|---|
| Open WebUI | 1,281 | 62% |
| Dify | 1,149 | 66% |
| RAGFlow | 393 | 45% |
| FastGPT | 340 | 41% |
| AnythingLLM | 74 | 41% |
Finer chunking put the answer in a smaller, more specific passage, and a smaller passage ranks better against a specific question. Coarse chunking kept the fact in the knowledge base but buried it in a block that never surfaced.
This is worth stating clearly because it inverts the intuitive ranking: the platform that preserved the most facts retrieved the fewest of them. For internal documentation search, retrievability is the property that matters, and Dify has it.
Open WebUI is closer than expected
At 62% retrieval, one container and 913 MB, Open WebUI is a genuine alternative if you also want a chat interface and can accept thinner knowledge-base controls. Its parsing figure is the lowest measured at 61%, but that figure is approximate — it exposes no way to list stored chunks, so we could only measure what retrieval surfaced.
Where this recommendation stops applying
- Your documents are contracts or statements where a figure must survive exactly — FastGPT preserved 97% of planted figures against Dify's 82%
- The host has no outbound internet access; Dify ships with no model providers and installs them from an external marketplace
- You rely on `docker compose down --volumes` to reset state, which does not reset Dify