What the data for this category looks like
The data for this category mainly comes from content data platforms, advertising revenue sharing systems, financial accounting systems, and creator account backends. Commonly processed documents include incentive rule details, monthly settlement detail lists, creator appeal records, and batch payment application forms. Data updates follow monthly or single-event cycles. Document fields typically include creator unique identifiers, revenue accounting standards, settlement cycles, appeal statuses, payment amounts, and other relevant items. Units include yuan, person-times, calendar days, and similar units.
Constraints imposed on document parsing and chunking
Incentive rule documents are mostly long-form policy descriptions. Settlement details contain large amounts of structured tabular data. The parsing process must preserve both text and table formats. Monthly or event-driven update cycles lead to wide variation in the scale of documents processed in a single batch. The system must adapt to inputs of different lengths. Financial data has strong inter-field correlation. When chunking, preserve the contextual association of adjacent records to avoid breaking the binding between revenue accounting standards and creators. Batch data in payment lists must also avoid field misalignment caused by chunking, which would disrupt subsequent settlement verification steps.
Configuration Settings
These configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category of documents mostly consists of long text with structured fields. This range balances contextual association and retrieval efficiency |
chunkSplitter | By paragraph + custom delimiter | Daily documents include incentive rule paragraphs and settlement detail tables. Custom delimiters can distinguish policy text from data tables |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Batch settlement detail documents have large file sizes, so sufficient parsing time must be reserved |
customPdfParse | Enable structured table extraction | Settlement details are mostly PDF format tables. Enabling this feature preserves field structures |
chunkSettingMode | Manual configuration | This category of documents has wide structural differences. Manual configuration allows adaptation to different types of document parsing |
Three Common Misconfigurations
- Symptom: Uploading a settlement detail PDF results in an empty parsing result or missing fields. Cause: The structured table extraction function of
customPdfParseis not enabled, causing table content to be discarded as plain text. - Symptom: Parsing tasks time out and fail, returning timeout-related status codes. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter is not adjusted based on document size, and the default short duration configuration is used. - Symptom: Extremely long incentive rule documents are forcibly truncated, resulting in loss of some revenue accounting standard descriptions. Cause: No custom segmentation rules are configured for
chunkSplitter, and only default delimiters are used to process extremely long text.
How to Verify Correct Configuration
- Upload a typical settlement detail PDF document, check if table fields in the parsing result are fully preserved, to confirm the structured extraction function is active.
- Upload a large-volume incentive rule document, verify that the execution duration of the parsing task falls within the preset range, and no timeout errors occur.
- Randomly select a segment of extremely long text, check that the chunking result preserves contextual association, and no content loss occurs due to forced truncation.
- Import batch-formatted appeal record documents, confirm that associated appeal information for the same creator is not split during chunking.
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 2026-10-19.