What this category's data looks like
Vocational colleges' formulation of talent cultivation programs involves three types of core materials: talent cultivation programs, course standards, and job competency analysis tables. The talent cultivation program contains cultivation objectives, course settings, class hours and credits, signatures, and dates; the course standard contains the course name, teaching content, assessment method, version number, and approval signature; the job competency analysis table contains the job name, competency items, corresponding courses, survey date, and expert signature. The material sources are academic affairs system exports, teaching and research office internal review drafts, enterprise survey receipts, and superior approval documents, and the update rhythm changes with specialty adjustments, semester revisions, and evaluation cycles. When the materials enter the document parsing and chunking function, each assumes an independent role: the talent cultivation program serves as the programmatic basis, the course standard serves as course-level support, and the job competency analysis table serves as evidence for job alignment. The fields, dates, and signatures of the three types of materials need to be compared item by item, and it is common for the name, quantity, and date of the same record to fail to match across two materials, for versions not to be synchronized, and for signatures or receipts to be missing.
Configuration and implementation checks
This step is executed by the parsing and training task (server-side task), which outputs data rows and index status (parsed, indexing, indexed). "Basis items that can be traced back to the original text" are then completed by retrieval and subsequent processing. After parsing is completed, parsed chunks are immediately persisted as visible data rows and bound to a unique data identifier; the index transitions through the three states parsed, indexing, and indexed; data pending indexing does not participate in recall; and changing the model only rebuilds data that has already completed indexing.
| Object | Check | Verification action |
|---|---|---|
| Talent cultivation program | Fields, dates, and signatures need to be aligned item by item with the course standard and the job competency analysis table, and the version must be currently valid | Open the data row to check the status, confirm that the three states parsed, indexing, and indexed are consistent with the interface, and check whether signatures and dates are complete |
| Course standard | The course name, class hours and credits, and assessment method are consistent with the records in the talent cultivation program, and the version number is synchronized | Check the file records and data rows separately, confirm that the identities of the file records and the data rows are distinguishable, and compare versions and signatures |
| Job competency analysis table | The job name, competency items, corresponding courses, and survey date can be traced back to the original text, and the expert signature is complete | Check whether the data rows covered by the rebuild task cover only data that has already completed indexing, and confirm that trainingType matches the processing mode |
| Data row | It is in one of the three states parsed, indexing, and indexed and is consistent with the interface display; data from failed training can be edited or deleted | Check the chunking results corresponding to chunkSize and chunkSplitter, and confirm that data pending indexing does not participate in recall |
Three common errors
- Symptom: newly uploaded materials cannot be found. Cause: the data row is in the parsed or indexing state, and data pending indexing is naturally isolated at the retrieval layer and is not recalled; successful parsing does not mean retrieval will definitely hit.
- Symptom: the name, quantity, and date of the same record fail to match across two materials. Cause: the talent cultivation program and the course standard were not revised in sync, versions are not synchronized, and field comparison lacks basis items that can be traced back to the original text.
- Symptom: empty data rows remain after training fails. Cause: interrupted training leaves orphan data, materials missing signatures or receipts have not completed ingestion, the initial training covers data pending indexing and data being indexed, and rebuilds target only data that has already completed indexing.
How to verify the setup
This function's acceptance: upload one material and observe the data row status transitions and the coverage scope of the rebuild task. Confirm that the data row transitions from parsed to indexing to indexed are consistent with the interface display, that the rebuild task covers only data that has already completed indexing, and that the initial training covers data pending indexing and data being indexed; the two cannot be interchanged.
Business test: ask a basis question that spans materials and see whether the answer provides a traceable source. For example, ask whether the class hours of a certain course are consistent between the talent cultivation program and the course standard, and check whether the answer can be traced back to the original text and indicates the material name, version, and signature date.
- Open the data row to check the status, confirm that the three states parsed, indexing, and indexed are consistent with the interface display, and check that the identities of the file records and the data rows are distinguishable.
- Check the chunking and training configuration corresponding to
chunkSize,chunkSplitter, andtrainingType, and see whether the data rows covered by the rebuild task cover only data that has already completed indexing.