What this category's data looks like
This category's data involves three types of materials: student registration forms, teaching plans, and skills training records. Student registration forms record student identity, registration date, and signature; teaching plans record course name, number of class hours, and version date; skills training records record training items, quantity, and receipt signature. These materials are updated by academic year or semester, and field names, date formats, and signature field positions vary. When entering this function, the three types of materials are imported as knowledge base data rows, each generating a vector index; the original data is stored in the business database data table, and the index field records multiple vector identifiers. Material roles: student registration forms provide identity and registration basis, teaching plans provide course and version basis, and skills training records provide training process and signature basis. During query, vectors are first recalled, then merged back to the original data by identifier, and the highest score is taken for the same group of data.
Configuration and implementation checks
This step is executed by the index service and vector database, outputting vectors and index status (whether recall is possible, whether the same group of data is merged). "Basis entries that can be traced back to the original text" is then completed by subsequent AI nodes, code nodes, or manual work (retrieval and citation trimming only take supporting materials).
| Object | Check | Verification action |
|---|---|---|
| Student registration form | Fields include student name, registration date, and signature field; date and signature must match the teaching plan and skills training records | Open the data row to check whether the index field is complete; after retrieval, see whether the same group is merged and whether the status is indexed |
| Teaching plan | Fields include course name, number of class hours, and version date; the name, quantity, and date of the same course in the two materials must be consistent | Check that the index model is consistent with the vector model actually used by the knowledge base, view the coverage of the rebuild task, and check the values of defaultToken and maxToken |
| Skills training record | Fields include training item, quantity, and receipt signature; if the signature or receipt is missing, the entry cannot be used as a basis | Retrieve one record and see whether the returned result points back to the original text and shows indexed status, and check the defaultToken and maxToken budgets |
| Data row index field | Multiple vector identifiers for one piece of data are all present, and the status is indexed or in the rebuild task record | Open a data row to check the index field, and view the rebuild task record |
Three common errors
- Symptom: the name, quantity, and date of the same record in the two materials do not match. Inspect the following: the student registration form and teaching plan versions are not synchronized, and field alignment was not performed during import.
- Symptom: duplicate entries appear in retrieval results. Inspect the following: the same group of data corresponds to multiple vector identifiers, and they were not merged by highest score during merging.
- Symptom: a data row has content but cannot be retrieved. Inspect the following: after changing the model, rebuild was triggered only for data that had already been indexed, and some data row index fields are missing.
How to verify the setup
This function's acceptance: sample one data row and check 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 of data is merged and the highest score is taken, and check the defaultToken and maxToken budgets.
Business test: ask a basis question that spans materials, see whether the answer gives traceable sources, and check that the number of citations and the citation content budget are recorded separately.
- In the knowledge base settings, check that the index model is consistent with the vector model actually used by the knowledge base, and view the data scope covered by the rebuild task.
- Open a data row to check the index field and confirm that multiple vector identifiers are all present; after retrieval, check whether the same group of data is merged and whether the status is indexed.