OG-RAGEnterprise document Q&A
01 / 04

2526/2526Fragments aligned
100%Injection coverage
81.4%+Disambiguation accuracy
0ditamaps leaked
Employee question
“HC/M00 how to enter low-power mode?”

① How it works: a five-stage pipeline

From the source repository to the user, traceability is preserved at every stage.

  1. DITA source repositorysingle source of structured content
  2. Manifest attributionwhich topic and manual each fragment belongs to
  3. Pipeline ingestionchunking, injection, indexing
  4. Service retrieval QArecall, filter, weight, generate
  5. User deliveryanswers with source and version
Dual channel

Keyword injection plus filtered weighting for both recall and precision.

Four-way merge engine

keydef lookup, bookmap tree inheritance, directory rules and bookmeta fallback determine attribution automatically.

② Core features

Six capabilities together make answers trustworthy.

Six-dimension attribution

topic, manual, version, section, language, product line

One-click auto-acquisition

automatic provenance tracing

Multi-condition filtering

narrow by version, product line, language

DITA-aware extraction

topic-level chunking that preserves meaning

Ontology hypergraph

concept relations inform recall

Model-agnostic

own or third-party models, data stays on-premise

Visual search with screenshot
Visual search: ask with a screenshot — the system understands interfaces and drawings.

③ Compared with vector-only RAG

Item-by-item comparison with a vector-only approach.

DimensionVector-only RAGOG-RAG
AttributionSource unclearDown to topic and manual
VerificationHard to verifyClick back to the source
MaintenanceFull rebuild on any changeIncremental, version-isolated
ModelLocked to one vendorSwappable, on-premise

④ Measured results

Figures below come from acceptance testing on real data.

2526/2526fragments aligned
100%injection coverage
81.4%+disambiguation accuracy
0ditamaps leaking into answers

⑤ Value by role

What the system solves, by role.

Chip and hardware makers

many versions and languages; versioned answers prevent wrong parameters.

Documentation platforms

reuse existing structured content instead of rebuilding for AI.

On-premise deployments

swappable models, data stays inside the network.

Baseline V3.2 · on-premise or cloud deployment.