What data for this category looks like
This category’s data originates from centralized treasury payment systems, unified budget management systems, electronic invoice platforms, and contract management systems, and is updated daily. Commonly processed documents include payment applications, fund appropriation plans, invoices, contracts, acceptance receipts, reimbursement approval forms, and payment vouchers. Most documents mix structured fields and attachments, containing fixed fields such as budget indicators, economic classifications, payment amounts, and approval workflow nodes. Some documents use multi-column tables for detailed data.
Constraints on document parsing and chunking
Multiple system sources create disparate document formats, requiring adaptation to both structured tables and plain text documents to avoid field loss from single parsing rules. Daily batch-updated documents demand stable parsing efficiency to prevent timeouts that disrupt daily audit workflows. Single payments linked to multiple documents require precise chunking to preserve approval chain context, ensuring coherent audit logic. Improper segmentation of multi-column table detailed data breaks field correspondence, so each group of associated fields must stay intact after chunking.
How to set configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of multi-field associated fiscal documents, avoiding splitting the association between budget indicators and corresponding payment amounts |
chunkSplitter | Split by table rows | For multi-column detailed tables, preserves complete fields for each row of bills or payment details, preventing cross-row information loss |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Matches parsing needs for daily batch-updated documents, preventing timeouts when parsing large contracts or multi-page payment vouchers |
customPdfParse | Enable table recognition mode | For scanned documents or structured PDF invoices, accurately extract fields such as economic classifications and amounts within tables |
UPLOAD_FILE_MAX_SIZE | 50 MB | Adapts to batch-imported annual payment voucher packages, preventing upload failures caused by overly large single files |
Three common configuration errors
- Symptom: After automatic chunking of multi-column structured table documents, cross-row field associations are lost, and a single chunk contains unrelated multi-row column data. Cause: The table row split basis was not specified, and the default paragraph splitting rule was used.
- Symptom: When batch importing documents updated on the current day, the parsing task loads all previously uploaded documents, resulting in excessively long parsing time. Cause: The global historical document reuse parsing switch was not turned off; the system processes all uploaded files in combination by default.
- Symptom: After uploading xlsx-format reimbursement details, embedded formulas or hidden columns are incorrectly parsed as text content, resulting in empty fields. Cause: The custom table parsing mode was not enabled, and rules to skip hidden columns were not configured.
How to confirm correct configuration
- Upload a single multi-column excel reimbursement detail file, check whether each chunk corresponds to an independent row without mixed cross-row fields.
- Upload a batch document package, view the parsing task log to confirm that only the currently uploaded files are parsed, with no historical files involved.
- Upload a PDF invoice with tables, check whether the extracted fields include core content such as economic classifications and payment amounts, with no missing fields.
- Test uploading a large payment voucher package under 50 MB, confirm that there are no timeout errors in the upload and parsing process.
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.