What the data for this category looks like
Core data for this category comes from content data, advertising revenue sharing systems, financial systems, and account backends. Four document types are processed daily: incentive rules, settlement details, appeal records, and payment lists. These documents include structures such as loose clause text, structured tables, and short-text appeal entries. Common fields include creator ID, settlement cycle, revenue sharing ratio, and payment amount. Supported units include yuan, calendar days, and number of users. Data updates follow a monthly schedule or single activity cycle. The scale of settlement documents for a single activity varies based on the count of participating creators.
What constraints do these characteristics impose on document parsing and chunking?
The loose clause structure of incentive rules requires chunking logic to balance long paragraphs and nested entries, preventing associated clauses from being split across different chunks. Structured tables in settlement details must retain field alignment relationships, avoiding broken table structures after parsing. Large monthly summary documents require adaptation to timeout and upload limits for large file processing. Documents from multiple systems have inconsistent formats, so custom parsing logic is needed to support different settlement data formats. Mixed short-text appeal records and long-text rule documents require flexible chunk length adjustment to cover all content types.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the average content length of settlement details and appeal records, avoiding excessively long or short single chunks |
chunkSplitter | Mixed newline and chunk length | Balances the splitting needs of loose clauses and structured tables, preventing incorrect splitting of long paragraphs |
customPdfParse | Enabled | Retains the field structure of settlement tables, avoiding structured data being parsed as plain text |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of monthly settlement summary PDFs over 10MB, avoiding timeout errors |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports uploading complete monthly settlement summary files in one go, no need for split uploads |
chunkSettingMode | Manual mode | Adjusts chunking rules for different document types, adapting to differences between incentive rules and settlement details |
Three Common Configuration Mistakes
- A
timeout of 360000ms exceedederror occurs when uploading monthly settlement PDFs over 10MB. The cause is failure to adjust thePARSE_FILE_TIMEOUT_SECONDSparameter; the default timeout duration is insufficient for large file parsing. - Custom parsing JS methods do not take effect. The cause is failure to correctly bind custom functions in the
customPdfParseconfiguration, or using a FastGPT version lower than v4.17.0, which does not support custom parsing. - Ultra-long settlement detail paragraphs are truncated and lost after chunking. The cause is only splitting documents by a single delimiter, without performing secondary splitting on segments exceeding the
chunkSizelimit.
How to Confirm Configuration is Complete
- Upload a test settlement detail PDF, review the parsed chunk results, confirm that table fields are not split chaotically and key data is not lost.
- Adjust
PARSE_FILE_TIMEOUT_SECONDSto 600 seconds, then upload a monthly settlement summary file over 10MB, confirm that no timeout errors occur during parsing. - After enabling
customPdfParse, upload a document containing appeal records, confirm that the custom parsing logic extracts core fields such as appellant and appeal time as expected. - Test the
chunkSizeconfiguration set to 800 characters, confirm that long paragraphs are correctly split into segments matching the length, with no content truncation.
The values provided here are common starting points, and should be evaluated against the user'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.