这是数据层的 目录页:有哪些数据、从哪来、多新、怎么取。它不讲注入机制——那是 grounding-layer 的事(见下"边界")。
The data layer’s catalog page: what data exists, where it’s from, how fresh, fetch order. It does not cover the injection mechanism — that’s grounding-layer’s job (see “Boundary” below).
边界:总览 vs grounding-layer / Boundary (the overlap you flagged)
中文。 两页不重叠,职责正交:
- 本页(总览)= 数据目录 + 出处 + 取数顺序:有什么数据、从哪来、多新。
- grounding-layer = 注入机制:这些数据如何被拼成提示词、塞进场景与调查员(
grounding_block/lookup_uni/ 具体性契约)。 - 另两个机制页:[investigator]、[yulai-integration]。
一句话:总览回答 WHAT/WHERE,grounding-layer 回答 HOW。
English. The two pages don’t overlap — orthogonal roles:
- This page (Overview) = data catalog + provenance + fetch order: what data, where from, how fresh.
- grounding-layer = the injection mechanism: how that data is assembled into prompts and fed to scenes/investigator (
grounding_block/lookup_uni/ the concreteness contract). - Two more mechanism pages: investigator (how the web tier is implemented), yulai-integration (how 雨来 connects).
In one line: Overview answers WHAT/WHERE, grounding-layer answers HOW.
数据目录 / The catalog
中文。
| 数据集 | 内容 | 规模 | 来源 | 页面 |
|---|---|---|---|---|
uni_employment.json | 就业率/升学率/出国率/起薪 + 行业/雇主 | 2347 校(起薪 2140 / 就业率 1547) | 就业质量报告(权威受限蒸馏) | data-employment |
major_grounding.json | 专业深度知识(课程/去向/岗位/经验) | 856 专业(859 卡) | 蒸馏 + 事实/观点标注 | data-schools-majors |
major_catalogue.json | 本科专业目录(按门类) | 全目录 | 教育部专业目录 | data-schools-majors |
school_index_sample.json | 985/211/双一流 + 录取批次 | 2347 校 | 公开层次/批次 | data-schools-majors |
| 雨来 API | 录取分/等位分/专业判别 | 分省分年 | 雨来平台(权威,接入中) | data-admissions |
English.
| dataset | content | size | source | page |
|---|---|---|---|---|
uni_employment.json | 就业率/升学率/出国率/salary + industries/employers | 2347 schools (salary 2140 / employ 1547) | 就业质量报告 (authoritative-restricted distill) | data-employment |
major_grounding.json | per-major deep knowledge (courses/paths/jobs/experience) | 859 majors | distilled + fact/opinion labeled | data-schools-majors |
major_catalogue.json | 本科专业目录 (by 门类) | full | MoE major catalogue | data-schools-majors |
school_index_sample.json | 985/211/双一流 + admission batch | 2347 schools | public tier/batch | data-schools-majors |
| 雨来 API | 录取分/等位分/专业判别 | by province/year | 雨来 platform (authoritative, integrating) | data-admissions |
取数顺序与诚信规则 / Source tier & integrity rules
中文。 取数顺序:雨来(权威) → 本地 KB(grounding/索引) → 网络检索 → 定性。诚信规则:① 事实/观点分别标注;② 数字只采信明确覆盖该校该专业的权威来源;③ 缺失即留空(honest-null),绝不编造;④ 不把一个机构的数据安到另一个头上;⑤ 每条带 provenance/时间。
English. Fetch order: 雨来 (authoritative) → local KB (grounding/index) → web search → qualitative. Integrity rules: ① fact vs opinion labeled; ② numbers only from a source explicitly covering that school+major; ③ missing → null (honest-null), never fabricated; ④ never attribute one institution’s data to another; ⑤ each carries provenance/timestamp.
Related
data-employment · data-schools-majors · data-admissions · grounding-layer · investigator · yulai-integration