What this category's data looks like
In the compulsory education dropout prevention and school retention scenario, persuasion-to-return records, home visit records, and dropout risk ledgers are the three core types of materials. Persuasion-to-return records include student name, persuasion date, persuasion method, parent signature, and receipt; home visit records include home visit time, teacher signature, and parent comments; risk ledgers include risk level, responsible person, and case closure date. These materials often enter the knowledge base in forms such as paper scans, spreadsheets, and photo descriptions. Data versions and change maintenance require that the current version be unique, old versions be traceable, and citations be synchronized. When materials enter the vector model and indexing function, they are stored as raw data rows, and their index fields record multiple vector identifiers to support subsequent retrieval and citation trimming. The update cadence is usually triggered by semester or special inspection milestones, and the consistency of fields, dates, and signatures must be checked.
Configuration and implementation checks
This step is executed by the indexing service and vector database, and outputs vector and index status (whether recall is possible and 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 |
|---|---|---|
| Persuasion-to-return record | The fields include persuasion date, parent signature, and receipt, and are consistent with the name and date in the risk ledger | Open the data row to check whether the index field contains multiple vector identifiers and confirm the status is indexed; check the coverage of the rebuild task record |
| Home visit record | The fields include home visit time, teacher signature, and parent comments, and match the student name and date in the persuasion-to-return record | Retrieve the same student, check whether data in the same group is merged, and verify the budget of limit in the cited text |
| Dropout risk ledger | The fields include risk level, responsible person, and case closure date, and the current version is unique and old versions are traceable | In the knowledge base settings, check that the index model is consistent with the vector model actually used by the knowledge base; check whether the rebuild task record covers data that has already been indexed |
| Data row index field | Multiple vector identifiers for one piece of data are all present and can be recalled | Open a data row to check the index field; after changing models, trigger a rebuild only for data that has already been indexed, and check the rebuild task record |
Three common errors
- Symptom: The name and date for the same student do not match between the persuasion-to-return record and the risk ledger. Inspect the following: the version is not.
- Symptom: The home visit record has content but cannot be retrieved. Inspect the following: the data row index field is missing vector identifiers, or a rebuild was not triggered after changing models, reducing recall quality.
- Symptom: Duplicate entries appear in retrieval results. Inspect the following: the same group of data corresponds to multiple vectors, and the highest score was not taken during merging, or versions were not synchronized, causing old and new records to coexist.
How to verify the setup
Acceptance for this function: sample one data row and inspect the index field to confirm that multiple vector identifiers are all present and the status is indexed; then retrieve once to see whether data in the same group is merged and take the highest score.
Business test: change one piece of content and see whether the version number and citation are updated synchronously; check and record the tokens and array count of the cited text separately, and limit are only for budget reference.
- In the knowledge base settings, check that the index model is consistent with the vector model actually used by the knowledge base, and check the data range covered by the rebuild task record.
- Open the data row to check the index field and confirm that multiple vector identifiers for one piece of data are all present; during retrieval, observe whether data in the same group is merged and take the highest score.