Document Parsing and Chunking for College Admissions and Application Placement: Compare Documents and Locate Differences

What this category's data looks like

Materials in the general higher education institution admissions enrollment and filing stage come from three types of ledgers and receipts: enrollment plans, filing rules, and admission rosters. Enrollment plans are updated by batch and subject category, and contain major names, planned quantities, subject selection requirements, and dates; filing rules are published by province and batch, and contain filing ratios, tie-breaking sorting, signature fields, and version numbers; admission rosters are written back by the admission review group, and contain candidate names, admitted majors, admitted quantities, dates, and receipts. When materials enter this function, enrollment plans and filing rules are parsed and chunked first as baseline ledgers, while admission rosters are parsed and chunked later as the object to be verified; each is persisted to the database as data rows and bound to a unique data identifier, so that retrieval and subsequent processing can compare names, quantities, dates, and signatures against the same object.

Configuration and implementation checks

This step is executed by parsing and training tasks (server-side tasks), and outputs data rows and index statuses (parsed, indexing, indexed). The discrepancy list is then completed by retrieval and subsequent processing. After parsing is complete, parsed chunks are immediately persisted to the database as visible data rows and bound to a unique data identifier; the index transitions through the three states parsed to indexing to indexed, and data pending indexing does not participate in recall.

Three common errors

• Symptom: a newly uploaded admission roster cannot be found. Cause: the data row is in parsed or indexing, data pending indexing is isolated at the retrieval layer, and successful parsing does not equal a retrieval hit.
• Symptom: the admitted quantity and date for the same candidate do not match between the roster and the ledger. Cause: the versions of the two materials are not synchronized, one of them is still the old version, and the fields were not updated with the version.
• Symptom: filing rule materials lack signatures or receipts, and review cannot pass. Cause: the signature fields and receipts were not completed in the material circulation step, and although the data row is already indexed, the business requirements are incomplete.

How to verify the setup

This function's acceptance: upload one material, observe the data row status transitions and the coverage scope of the rebuild task, confirm the transition from parsed to indexing to indexed, and check that the rebuild covers only data that has completed indexing; the reference limit verifies the tokens of the referenced text, the number of array items is recorded separately, and chunkSize, chunkSplitter, and trainingType are recorded separately.

Business test: use one real record to find a field inconsistent with the ledger, and hand it to the reviewer for confirmation and write-back.

• Check file records and data rows separately, confirm that the identifiers of files and data rows are distinguishable, and record the values of chunkSize and chunkSplitter.
• View the data rows covered by the rebuild task, confirm that only data that has completed indexing is covered, and record trainingType and the index status.