What the data for this category looks like
Core data for this category centers on student status and enrollment eligibility. Daily processed documents include enrollment application forms, student status information sheets, transfer applications, enrollment plans, admission rosters, student status change approval forms, and similar materials. Data sources include the national primary and secondary school student status information management system, local enrollment platforms, household registration and real estate data interfaces, and school academic affairs systems. Update frequency is daily. Documents contain structured tables and unstructured text. Core fields include student name, student ID number, household registration address, enrollment eligibility review status, and similar items. The focus is on student status transfer and enrollment eligibility verification.
What constraints do these characteristics impose on document parsing and chunking?
The data characteristics of this category impose multiple constraints on document parsing and chunking. First, documents contain structured student status tables and unstructured enrollment text. The parsing process must balance accuracy of table field extraction and long text chunking. It must avoid losing core associated information such as student ID numbers and household registration addresses after chunking. Second, daily updated batch data requires the parsing process to be efficient. It must avoid excessive single-file parsing time that delays overall progress. Third, data formats from multiple sources vary. The system must support custom parsing rules to adapt to documents exported from different platforms. Fourth, the accuracy of student status data directly affects student progression and student status registration. The parsing link must avoid field omissions or incorrect identification. This prevents student status issues in subsequent ledger aggregation.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to multi-field documents such as student status information sheets and admission rosters. Prevents loss of core associated information after chunking |
chunkSplitter | Chunk by paragraph + heading | Corresponds to documents with hierarchical structures such as enrollment plans and transfer applications. Preserves contextual associations such as policy clauses and application reasons |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Compatible with hundreds-page admission roster PDFs for batch imports. Prevents large file parsing timeouts that trigger interruptions |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to batch-imported student status archive compressed packages. Meets daily batch processing needs |
customPdfParse | Enable table recognition mode | Accurately extracts structured fields from student status information sheets. Prevents table content from being incorrectly split into scattered text |
Three common misconfigurations
- An error occurs when using
customPdfParseto parse hundreds-page PDF files, but parsing works normally for files with tens of pages. The cause is failure to adjust thePARSE_FILE_TIMEOUT_SECONDSparameter. Large file parsing time exceeds the default threshold and triggers an interruption.
Reference only; verify against the target FastGPT release and deployment documentation.
- Setting chunk repetition rate without associating the uniqueness of student status fields leads to excessive duplicate chunks. This reduces the accuracy of subsequent recall.
How to confirm proper configuration
- Upload a single student status information sheet PDF under 100 pages. Check if the parsed text fully retains core fields such as student ID number and household registration address.
- Upload a batch compressed package of student status archives. Verify that upload progress and parsing time fall within the configuration ranges of
UPLOAD_FILE_MAX_SIZEandPARSE_FILE_TIMEOUT_SECONDS. - Test the post-chunk recall logic. Confirm that split content from the same enrollment plan document can be recalled in batch.
- View parsing logs. Confirm that no abnormal records of empty fields or garbled formatting appear.
The values listed here are common starting points. They should be evaluated against the user’s own sample data.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.