What this category's data looks like
Graduate training process management involves three core types of materials: training plans, proposal reports, and mid-term assessment forms. Sources are multi-level submissions from supervisors, colleges, and graduate schools, with update cadence driven by semesters and key milestones. Training plans record courses, credits, and research directions; proposal reports carry topic selection rationale, literature review, and schedule arrangements; mid-term assessment forms summarize stage outcomes, problems, and improvement measures. When these three types of materials enter the vector model and indexing stage, each plays a different role: training plans provide structured fields and a time baseline, proposal reports provide long-text argumentation and citation clues, and mid-term assessment forms provide assessment conclusions and signed receipts. Field characteristics include name, student ID, course name, credits, dates, signature fields, and attachment lists. The same record often appears repeatedly across materials, and version consistency must be maintained before indexing.
Configuration and implementation checks
This step is executed by the indexing service and vector database, outputting vectors and index status (whether recall is possible, whether data in the same group is merged). The "version ledger and change records" are then completed by subsequent AI nodes, code nodes, or manual work (retrieval and citation trimming only take supporting materials).
| Object | Check | Verification action |
|---|---|---|
| Training plan | Fields, dates, and signatures must align item by item with the proposal report and mid-term assessment form; course names and credits must not be inconsistent in two places | Open a data row and check the index fields, confirm that multiple vector identifiers are complete, and the status is indexed |
| Proposal report | Topic name, literature count, and date must be consistent with the citations in the mid-term assessment form; missing signature or receipt means it is not closed-loop | In the knowledge base settings, check that the index model matches the actual vector model, and review the coverage of the rebuild task |
| Mid-term assessment form | Assessment conclusion, date, and signature must correspond to the training plan milestones; the count and names of the same record must not mismatch | Run a retrieval to see whether data in the same group is merged, and record the tokens of the cited text and the number of array entries separately |
| Data row | Multiple vector identifiers for one data row are all present; after changing models, rebuild is triggered only for data that has already been indexed | Check defaultToken, maxToken, and the rebuild task records, and confirm that no index fields are missing |
Three common errors
- Symptom: The course names and credit counts for the same record do not match between the training plan and the mid-term assessment form. Inspect the following: The two materials are submitted separately, versions are not synchronized.
- Symptom: The proposal report has been updated, but the mid-term assessment form still cites the old version's date and conclusion. Inspect the following: Change records are missing, the old version is traceable but the current version is not unique, and citations are not synchronized.
- Symptom: The mid-term assessment form lacks a signature or receipt, but retrieval can still recall that entry. Inspect the following: Indexing only records vectors and original data identifiers, does not verify signature status, and requires manual checking.
How to verify the setup
Acceptance for this feature: sample a data row and check the index fields, confirm that multiple vector identifiers are present and the status is indexed; then run a retrieval to see whether data in the same group is merged and whether the vector score takes the highest value.
Business test: change one piece of content and see whether the version number and citations are updated synchronously; check limit, and the rebuild task records, and confirm that after changing models, rebuild is triggered only for data that has already been indexed.
- In the knowledge base settings, check that the index model matches the vector model actually used, and open a data row to check whether the index fields are complete.
- Review the data range covered by the rebuild task records, run a retrieval to confirm that data in the same group is merged, and record the tokens of the cited text and the number of array entries separately.