Data Profile for This Category
Service subjects include education bureau aid centers, school student affairs offices or aid offices, and financial departments. Daily processed documents include family economic hardship certification forms, aid application forms, scholarship and grant review forms, disbursement lists, and public notice materials. Data sources cover the national student aid management information system, school student status systems, civil affairs low-income and special hardship data, and financial payment systems. Update cycles follow academic semesters. Most documents contain fixed fields such as student ID number, bank card number, hardship level, and disbursement amount. Supported file formats include scanned copies, Excel spreadsheets, and Word documents.
How These Characteristics Impact the Document Parsing and Chunking Link
Documents from multiple systems have significant format differences, so the parsing workflow must support multiple file types and layouts. Semester-based batch processing requirements mean the parsing and chunking workflow must support bulk uploads of large files. The presence of fixed fields requires that chunking preserves the association between fields and their surrounding context, to avoid splitting critical information. Additionally, errors in aid review directly affect student rights, so parsing and chunking accuracy must be maintained, with no field loss or content misalignment.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Aid-related documents contain fixed fields and paragraphs. This range balances content integrity and matching accuracy, preventing lost field associations from overly long chunks, and overly fragmented chunks from overly short lengths |
chunkSplitter | Split by title + paragraph | Most documents in this category have section titles such as family situation and student status review columns. Retaining title prefixes improves context association during RAG retrieval |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Bulk semester materials can reach hundreds of MB per file. This duration covers parsing and OCR processing for large files |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Meets the maximum volume requirements for bulk disbursement lists and scanned public notice materials, preventing upload interruptions |
customPdfParse | Enabled | For unstructured PDFs such as scanned certification forms, enabling this triggers structured parsing that retains original layout and field positions |
chunkSettingMode | Manual configuration | Allows adjustment of chunking rules for different document types, adapting to differences between student status system exported spreadsheets and manually filled forms |
Three Common Misconfigurations
- Uploads parse correctly in local frontend environments, but return 404 errors when uploading after server deployment, and file content cannot be read. The cause is that the static file storage path on the server is not configured with a mapping, so the parsing node cannot access the uploaded file address.
- When using the Qwen3-14B model, the field extraction result after PDF parsing is empty. The cause is that the structured parsing mode of
customPdfParseis not enabled, so the parsing result of unstructured PDFs cannot be associated with model-recognizable fields. - RAG retrieval only matches the main content of chunks, and does not associate auxiliary field information. The cause is that chunking does not retain document metadata or auxiliary fields, only extracts main body paragraphs, which limits the matching scope.
How to Verify Proper Configuration
- Upload a single family economic hardship certification form, and verify that the parsing result includes core fields such as student ID number and family hardship level.
- Adjust
chunkSizeto the 800–1200 character range, upload multiple documents of the same type, and confirm that chunking does not split the association between fields and context. - Enable
customPdfParse, upload a scanned aid application form, and confirm that the OCR parsing result can be properly chunked and extracted. - After deploying to the server, upload bulk semester materials, and confirm that the parsing node has no timeout or path errors, and all files can complete parsing normally.
- Initiate a RAG retrieval test, and confirm that the returned results include auxiliary fields and chapter title information of chunks.
The values provided are common starting points and should be measured against the reader's own samples.
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.