What the Data for This Category Looks Like
Business data for this category originates primarily from internal state-owned asset supervision reports, asset disposal agreements, property right registration documents, business approval slips, and asset inventory ledgers. Update cycles fall into two categories: fixed and ad-hoc. Monthly operating reports and quarterly inventory reports follow fixed update schedules. Major asset transaction and ownership change files are generated ad-hoc based on business milestones. Document types include structured Excel ledgers, long-text PDF approval reports, Word process descriptions, and a small number of scanned PDF files. Fields include asset number, book value, affiliated unit, approval document number, and others. Units include ten thousand yuan, square meters, equity ratio, and others.
Constraints Imposed on Document Parsing and Chunking by These Characteristics
High-frequency fixed-cycle reports require efficient parsing. Chunking rules must adapt to table structures to avoid splitting fields apart. Long-text approval reports have clear approval process paragraphs and headings. Chunking must retain contextual coherence to support business guidance logic. Irregular large-value asset files may have large individual file sizes. Parsing capacity and timeout settings must be adjusted accordingly. Scanned property right documents require OCR support. Parsing configurations must cover non-editable PDF parsing needs.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical paragraph length of asset disposal reports, while retaining contextual association for structured ledger fields |
chunkSplitter | Split by paragraph + heading | Matches clear approval step headings and independent paragraphs in business documents, avoiding splitting that breaks business process logic |
customPdfParse | Enabled | Supports OCR parsing of scanned property right documents, retaining text hierarchy and layout information |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to the maximum individual file size for batch asset inventory Excel files, preventing upload interception |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Reserves sufficient parsing time for complex multi-page approval reports, avoiding timeout errors |
chunkSettingMode | Manual configuration | Supports adjusting chunking parameters separately for structured tables and long texts, adapting to multiple types of business documents |
Three Common Configuration Errors
- Phenomenon: After uploading an Excel-format asset ledger, the parsing result has missing fields or broken table structures. Cause: Custom parsing rules were not enabled for structured documents. The default chunking logic breaks the cell and row association of tables.
- Phenomenon: Timeout errors occur when parsing large multi-page asset disposal PDFs, and the task status shows failure. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. The default short timeout setting cannot complete full parsing of complex documents. - Phenomenon: When searching for keywords related to business processes, the returned chunks only contain isolated text and cannot be associated with the metadata of the corresponding approval step. Cause: A reasonable context overlap ratio was not set during chunking, resulting in lost association information between chunks and preventing complete process guidance.
How to Verify the Configuration Is Correct
- Upload a typical asset disposal PDF document. Check that the parsed text retains the heading hierarchy and paragraph logic of approval steps.
- Upload a structured Excel asset ledger. Confirm that the parsed chunks contain complete row and column field information with no split or broken fields.
- Test uploading a batch asset inventory file under 1000 MB. Confirm that the parsing task starts and completes normally with no upload interception prompts.
- Search for a specific asset number. Check that the returned chunks include preset metadata fields such as affiliated unit and book value.
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.