What this category's data looks like
This category's data covers the registration review for the qualification examination of professional and technical personnel, involving three types of materials: registration forms, academic credentials, and work experience certificates. The sources are candidate submissions and employer receipts, and updates advance with registration batches. The registration form contains name, ID number, examination subjects, date, and signature; the academic credential contains school name, major, graduation date, and certificate number; the work experience certificate contains position title, start and end dates, employer seal, and signature. After materials enter parsing and training tasks, each is persisted as a visible data row and bound to a unique data identifier, flowing through the three states of parsed, indexing, and indexed; data pending indexing does not participate in recall. Multilingual materials and terminology processing require the same term to be consistent throughout the entire text and the translated name to be traceable back to the source text, ultimately forming a unified terminology glossary and comparison draft for external release.
Configuration and implementation checks
Organize this material set by application batch and candidate identifier. Keep the school, major and graduation date together in academic credentials, and retain the employment dates and issuing employer in traceable context for work experience certificates. Parsed data rows support source lookup; compare translated names and employment periods with the originals.
FastGPT v4.17.2 persists data rows after parsing, with index states of parsed, indexing and indexed. When a row becomes visible, check whether indexing has also finished. Use chunkSize and chunkSplitter to inspect chunk boundaries and trainingType to identify the processing mode. Retain a source file and its chunking results before making an adjustment.
| Object | Check | Verification action |
|---|---|---|
| Application form | Candidate identifier, examination subject and application batch correspond | Locate the original record by candidate identifier, then compare parsed values and file sources |
| Academic credential | School, major and graduation date correspond between original and translation | Open the data row containing the major and check that a chunk boundary has not separated the name from its explanation |
| Work experience certificate | Employment dates, employer and seal can be traced to the source | Compare date formats, translated job titles and issuing employers in both versions |
| Terminology table | School, major and job title names use consistent translations | Select three actual terms and record their language versions, source files and corresponding data rows |
Three common errors
- Symptom: a school or major translation is retrieved for the wrong candidate identifier. Check the connection between the original credential and application form, then inspect the files and data rows actually matched.
- Symptom: the start and end of an employment period fall into different chunks. Inspect the boundary against the complete certificate, then compare results before and after changing
chunkSizeorchunkSplitter. - Symptom: a newly submitted credential is visible in the list but is not retrieved. Inspect its index state and training task error, then repeat the same query using the major name from the original document.
How to verify the setup
Use one candidate’s application form, academic credential and work experience certificate for the trial. Confirm that parsing results can be traced by file and candidate identifier. Query the translated school name, major and employment dates separately, and record the matched passages.
- Compare names and dates with the originals. Job titles and major names in the translation must refer to the same original entries.
- Retain the
trainingType, chunking settings, data row identifiers and index states. When changing models, check the rebuild task scope and execution results for indexed data separately.