- Dataset
- 2026-10-27-catalog-ai-v2
- Ruleset
- b2e8889c66f7
- Scanned
- 2026-10-27
- Window
- February–September 15, 2026
本项目设计并实现了一个基于知识图谱 RAG 与大语言模型的医疗智能问答系统。系统以 DiseaseKG 医疗数据集为基础,依托 Neo4j 构建包含 4.4 万实体与 31 万关系的领域知识图谱;结合 BERT 命名实体识别与 32B 大模型意图识别,通过精确的图谱检索与受控的答案生成,有效缓解大模型在医疗场景中的幻觉问题,显著提升回答的准确性与可靠性。
01Result
honeyandme/RAGQnASystem ↗honeyandme/RAGQnASystem AI coding statistics: 0%
No signed agent commits found
No agent signatures were found in the last 1 considered commits. That is a real result, not a failure to measure.
It does not mean no AI was used here. A developer can use an agent and commit by hand without leaving a detectable git signature.
02Drivers
What shapes this result
Mode, timing, and detected signatures show whether the result reflects broad adoption or one concentrated workflow.
03Evidence
Inspect the matches
Public commit samples let you inspect the signatures behind the aggregate result.
04Method
What entered the denominator
The result begins with commit history, removes merges and routine automation, and reports literal agent signatures as a lower bound.
Exclusion ledger
| Commits in window | 1 |
| Merge commits removed | −0 |
| CI / dependency bots removed | −0 |
| Considered | 1 |
Bots are excluded from the denominator entirely. Agent attribution requires a literal supported signature; no code-style inference is used.
Continue exploring
Related coding-agent evidence
Run the same detector locally.
Deterministic signature matching; no code-style inference. Squash merges can remove trailers, so this result remains a measured floor.
npx qmmit-cli honeyandme/RAGQnASystem