What Data for This Category Looks Like
This category's data originates from medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. The update frequency is monthly. Daily processed documents include audit notices, inspection work drafts, suspicious item lists, interview records, decision letters, and rectification reports. Most documents follow fixed formats, with fields such as document number, inspected entity, violation type, and associated business identifier. Content mainly consists of text and numbered entries, with no complex multi-dimensional numerical units.
Constraints for Document Parsing and Chunking
Monthly batch-updated document volumes are large. Systems must adapt to batch parsing timeout and size limits to avoid single-batch processing timeouts or oversized files. Document types are diverse and closely tied to business logic. When chunking, retain context integrity for same-type documents to prevent splitting key associated audit process content. Some documents are scanned copies or semi-structured text exported from settlement data. Parsing modes must be adapted to ensure no fields are missed. Violation determination relies on precise identifiers within documents. Parsing errors directly impact subsequent audit conclusions. Chunk boundary rationality must be strictly controlled.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Basis for This Setting |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to single-section business information volume of medical insurance audit documents, avoids splitting key associated audit process content |
chunkSplitter | Split by semantic segments | Retains continuous business entries in inspection work drafts and suspicious item lists, ensures chunking logic matches business associations |
customPdfParse | Enable Marker parsing mode | Adapts to scanned paper audit materials and drug/consumable traceability code documents, improves extraction accuracy of unstructured text |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Covers total size of monthly batch-imported audit files, prevents single-batch files from being blocked by the system |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to parsing time of large settlement data exported PDFs, prevents timeout from interrupting the parsing process |
chunkSettingMode | Manual configuration | Adjusts chunking parameters for different document types, matches the fixed format structure of audit documents |
Three Common Configuration Mistakes
- Issue: Single audit file takes longer than expected to parse. Cause: Marker parsing mode for
customPdfParseis not enabled, orPARSE_FILE_TIMEOUT_SECONDSis not adjusted to fit large settlement data exported files. - Issue: Violation type field is extracted as empty from scanned interview records. Cause:
customPdfParseis not configured as enabled, leading to insufficient parsing accuracy for unstructured scanned text. - Issue: File parsing tool cannot be invoked in conversation scenarios. Cause: File parsing permission is not enabled in application configuration, or
UPLOAD_FILE_MAX_SIZEis set lower than the size of the currently imported file.
How to Confirm Configuration Is Correct
- Upload a scanned audit notice under 100 MB, check if parsing completes within 10 minutes.
- Import a suspicious item list document, verify that parsed chunks retain the association between suspicious item numbers and violation types.
- View the switch status of
customPdfParseon the application configuration page, confirm Marker parsing mode is enabled. - Upload a single file, check that parsed chunk length falls within the 800–1200 character range.
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.