What this category's data looks like
This page addresses multilingual materials and terminology processing in grade entry and credit recognition scenarios; the materials include three categories: transcripts, deferred examination applications, and supplementary examination arrangements. Transcripts carry course name, credits, grade, and signature/seal fields; deferred examination applications record the application reason, date, and approval signature; supplementary examination arrangements list the supplementary examination time, location, and receipt. The three material categories are often submitted in parallel in different language versions, and the same record must be aligned item by item on name, quantity, and date. Materials enter the knowledge base by semester batch, and the update cadence is synchronized with academic affairs publication; the field characteristics are multilingual alignment, terminology density, and the coexistence of signatures and receipts. Transcripts provide the basis for credit recognition, while deferred examination applications and supplementary examination arrangements provide process nodes; together the three support the generation of a unified terminology glossary and a bilingual/parallel draft.
Configuration and implementation checks
This step is executed by the indexing service and the vector database, outputting vectors and indexing status (whether recall is possible, whether data in the same group is merged). The "unified terminology glossary and bilingual/parallel draft" is then completed by subsequent AI nodes, code nodes, or manual work (retrieval and citation trimming only take supporting materials). Retrieval first recalls vectors, then merges back to the original data by index fields, taking the highest score for the same group of data.
| Object | Check | Verification action |
|---|---|---|
| Transcript | The same course name and credits are written consistently in the two language versions, and the date and signature are complete | Open the data row and check whether the index field contains multiple vector identifiers; after retrieval, confirm whether the same group is merged |
| Deferred examination application | The application reason and approval date match in the two materials, and the signature and receipt are not missing | Check that the index model is consistent with the model actually used by the knowledge base, and review the data scope covered by the rebuild task |
| Supplementary examination arrangement | The supplementary examination time, location, and receipt count are consistent in the two materials, and the versions are synchronized | Retrieve three terms and check their spelling throughout the document; confirm that the data row status is indexed and there are no duplicate entries |
| Data row | Multiple vector identifiers for one piece of data are all present, and the highest score is taken after the same group of data is merged | Check limit, and the tokens of the cited text; record the array count separately |
Three common errors
- Symptom: duplicate entries appear in retrieval results. Inspect the following: multiple vector identifiers for the same group of data are not merged, or the index field is missing, so they cannot be consolidated when returning to the original data.
- Symptom: the name, quantity, and date of the same record do not match in the two materials. Inspect the following: versions are not synchronized, multilingual materials are imported independently, and the terminology glossary is not aligned first.
- Symptom: recall quality drops after changing models. Inspect the following: the rebuild task is triggered only for data that has already been indexed; data rows not covered still have content but cannot be retrieved.
How to verify the setup
This feature's acceptance check: sample one data row and look at the index field to confirm that multiple vector identifiers are all present and the status is indexed; then retrieve once to see whether the same group is merged, and review the coverage scope recorded by the rebuild task.
Business test: sample three terms and check whether their spelling is consistent throughout the document, confirm that the translated names can point back to the source text, and that signatures and receipts are not missing.
- In the knowledge base settings, check whether the index model is consistent with the vector model actually used by the knowledge base.
- Open a data row and check the index field to confirm that multiple vector identifiers are all present, and review the data scope covered by the rebuild task.