What this category's data looks like
This page addresses multilingual materials and terminology processing in graduate training process management. The materials come from training plans, proposal reports, and mid-term assessment forms, and include foreign-language literature abstracts, translated course names, and terminology comparison drafts. The update cadence follows the progression of training stages: concentrated revisions before and after the proposal, supplementary entry of receipts and signed documents after the mid-term assessment, and unified archiving before graduation. In terms of field characteristics, the training plan contains course names, credits, training directions, and dates; the proposal report contains the title, keywords, references, and supervisor signature; the mid-term assessment form contains the assessment conclusion, material list, quantity, and signature field. When multilingual materials enter the vector model and index, they are first split into retrievable chunks, the indexing service generates vector identifiers and writes them into business database data rows, and the original data is retained in the business database for subsequent retrieval and citation trimming.
Configuration and implementation checks
This step is executed by the indexing service and the vector database, outputting vectors and index status (whether recall is possible, whether data in the same group is merged). The "unified terminology glossary and comparison draft" is 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 | Course names, credits, training directions, dates, and signature fields must be aligned item by item with the proposal report and mid-term assessment form, and the name and quantity of the same record must not be written twice | Open a training plan data row, check whether the index field contains multiple vector identifiers, then search the course name to see whether the same group is merged, and check the values of defaultToken and maxToken |
| Proposal report | The title, keywords, reference entries, and supervisor signature must be traceable back to the original text, and the same terminology must be written consistently throughout | Search once by title and once by keyword, check whether the recalled chunks point to the same data row, and review the coverage of the rebuild task record |
| Mid-term assessment form | The assessment conclusion, material list quantity, date, and signature or receipt must be consistent with the training plan and proposal report, and archiving may proceed only after versions are synchronized | Sample three terms and check whether their spelling is consistent throughout, and check whether the material list quantity and date match between the two materials |
| Multilingual material chunks | When foreign-language abstracts and translated course names enter the vector database, the original data is retained in the business database data row, and the index field records multiple vector identifiers | Check that the index model is consistent with the vector model actually used by the knowledge base, and confirm that the data row status is indexed |
Three common errors
- Symptom: duplicate entries appear in retrieval results. Inspect the following: the same group of original data corresponds to multiple vector identifiers, and after recall they are not merged back to the original data by identifier, and the score is not taken as the highest score.
- Symptom: the name, quantity, and date of the same record do not match between two materials. Inspect the following: the training plan and mid-term assessment form are revised separately, versions are not synchronized, and terminology spelling is not unified.
- Symptom: a data row has content but cannot be retrieved. Inspect the following: after changing the model, rebuild is triggered only for data that has already completed indexing; data rows not covered still have status indexed, and the actual index model is inconsistent.
How to verify the setup
This feature's acceptance check: sample a data row and inspect the index field to confirm that multiple vector identifiers are all present; then search once to see whether the same group of data is merged and whether the score is taken as the highest score, and check that the status is indexed and the rebuild task record, while also checking the values of defaultToken and maxToken.
Business test: sample three terms and check whether their spelling is consistent throughout, and whether translated names can be traced back to the original text.
- In the knowledge base settings, check that the index model is consistent with the vector model actually used by the knowledge base, and review the data range covered by the rebuild task.
- Open a data row and check the index field to confirm that multiple vector identifiers for one piece of data are all present, then search once to see whether the same group is merged.