What Data for This Category Looks Like
The service subjects for this category are medical insurance fund supervision offices, handling institutions, and flight inspection teams. Regularly processed documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decision letters, and rectification reports. Data sources cover 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. Documents include structured fields and unstructured text. Some documents use a mixed format of scanned files and electronically exported data.
What Constraints These Characteristics Impose on Document Parsing and Chunking
Monthly updated bulk data requires parsing processes to have stable batch processing capabilities, to avoid timeout interruptions during single parsing runs. Mixed scenarios with multiple document types require chunking logic to adapt to different structures. The logic must retain the hierarchical title association of audit notices, and accurately extract colloquial suspicious point expressions from interview records. Mixed formats of scanned rectification reports and traceability code documents require adaptation to non-standard PDF parsing logic. Retroactive requirements for post-event audits require chunking to retain context association. This ensures that individual chunks can independently be linked to specific suspicious violations, while avoiding redundant chunking that increases subsequent processing time.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the paragraph length of audit-related documents, covering complete expressions of single suspicious violation points or handling opinions |
chunkSplitter | Split by paragraph + title | Matches the hierarchical structure of audit documents, retaining the association between titles and main text of notices and work papers |
customPdfParse | Enabled | Processes scanned rectification reports and exported documents of drug and consumable traceability codes, improving parsing accuracy for non-standard PDFs |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Adapts to multiple large-volume PDF audit files in a single special inspection batch, avoiding upload interruptions |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Matches the duration requirements of monthly batch parsing, avoiding timeout during large file parsing |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types, such as interview records and exported settlement data tables |
Three Common Misconfigurations
- An "offset out of range" error occurs when uploading large PDF files to 90% progress. The cause is failure to adjust the
UPLOAD_FILE_MAX_SIZEconfiguration, or failure to enablecustomPdfParseto handle internal offset issues in scanned PDFs. - The chunking results of files uploaded via the create file collection API are inconsistent with those uploaded directly via the platform. The cause is failure to unify the
chunkSplitterandchunkSizeparameters; the default chunking rules for API calls are not synchronized with the interface configuration.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Verify Successful Configuration
- Upload a single special audit PDF larger than 100 MB, check whether the upload progress completes fully, with no offset-related errors.
- Upload the same interview record document via the interface and API separately, compare whether the start and end positions of the chunking results are consistent.
- View the parsing log, confirm that the
PARSE_FILE_TIMEOUT_SECONDSconfiguration does not trigger timeout interruption prompts. - After enabling
customPdfParse, upload a scanned rectification report, check whether suspicious marked content in the document is extracted.
The values provided are common starting points and should be evaluated 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.