Data Characteristics for This Category
Data for this category typically originates from risk control systems, audit records, user behavior logs, and report evidence. Daily processed documents include ban records, appeal materials, review opinions, and user notifications. Most documents use a structure combining structured fields and unstructured text, with fields such as account ID, violation type, timestamp, appeal content, and audit result. Update frequency is usually daily or real-time.
Constraints on Document Parsing and Chunking
This category’s data mixes structured fields and unstructured appeal text, and includes account-level unique identifiers. The document parsing step must accurately distinguish between structured fields and free-form text. Real-time or daily update frequencies require the parsing process to support efficient batch processing, to avoid delays impacting appeal review progress. Content density varies widely across different document types, so different chunking granularities must be supported to ensure core information such as violation evidence and appeal reasons is not split or omitted. Associated fields such as account ID must be retained as metadata alongside chunks, to prevent information gaps during subsequent review association.
Configuration Settings
The following configurations are based on the document parsing and chunking logic of FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Most documents in this category are medium-length violation records and appeal texts. This range balances information completeness and retrieval accuracy |
chunkSplitter | Paragraph-based chunking + model-based paragraph recognition | Semantic boundaries of appeal materials and audit records are mostly defined by paragraphs. Model-based recognition corrects manual typesetting errors |
customPdfParse | Enabled | Report evidence for this category is often submitted in PDF format. Custom parsing supports structured forms and handwritten annotation content |
UPLOAD_FILE_MAX_SIZE | 100 MB | Most report evidence for this category are PDF or screenshot files. This upper limit covers the size of nearly all single evidence files |
PARSE_FILE_TIMEOUT_SECONDS | 600–900 seconds | Sufficient time must be reserved for parsing and chunking when processing batch PDF appeal materials |
chunkSettingMode | Custom configuration | Document structures vary widely across this category. Chunking parameters must be adjusted separately for ban records and appeal materials |
Three Common Misconfigurations
- The code implementation for the
/v2/parse/fileAPI cannot be located. This occurs ifcustomPdfParseis not enabled, or if the matching version of parsing logic is not used. - The account ID field is missing from chunk results. This occurs if metadata retention configuration is not enabled, so structured fields are not carried alongside chunks.
- The chat API returns
parameter format errorwhen multiple appeal materials are submitted. This occurs if the request body format for multi-file parameter passing is not correctly configured.
How to Verify Correct Configuration
- A single ban record PDF is uploaded, the chunk preview interface is reviewed, and structured fields such as account ID and violation type are confirmed to be retained alongside chunks.
- The batch parsing API is called, 3 appeal materials are submitted, and the number of returned chunk results is checked to match expectations.
- Parsing logs are reviewed, timeout errors are confirmed to be absent, and the
PARSE_FILE_TIMEOUT_SECONDSparameter is confirmed to be applied. - The document library pull API is called, and uploaded ban records and appeal materials are verified to be retrievable normally.
The values listed are common starting points, and should be evaluated against the applicable 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.