What this category of data looks like
Data for this category originates from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. Commonly processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data is updated on a monthly basis. Core fields include institution qualification information, on-site inspection items, rectification requirements, and penalty results. Most documents use a mixed format of standardized tables and paragraphs. Some physical paper documents require initial electronic conversion.
Constraints on document parsing and chunking
Monthly updated batch documents require the parsing process to support batch processing. The mixed structure of standardized tables and paragraphs requires adaptive multi-format parsing logic. Law enforcement records and penalty case files contain long paragraphs of inspection details. Overly long content may exceed per-chunk length limits, so reasonable splitting is needed to avoid information fragmentation. Data formats vary significantly across different sources. Parsing rule mismatches that cause field omissions must be prevented. Key compliance information in rectification notices and supervision opinions must be accurately located. Chunking must retain contextual associations to prevent key content from being split across multiple chunks.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapt to the paragraph and table block lengths of documents in this category, avoid splitting key compliance information |
chunkSplitter | Standardized separators + paragraph merging | Match the mixed document structure, prioritize retaining contextual associations between inspection items and rectification requirements |
customPdfParse | Enabled | Optimize parsing accuracy for tables and standardized content in PDF documents converted from paper scans |
UPLOAD_FILE_MAX_SIZE | 100 MB | Adapt to the single-file size of law enforcement case files and supervision inspection forms submitted in monthly batches |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Prevent task failure due to parsing timeout when processing long penalty case files |
chunkSettingMode | Manual configuration | Flexibly adjust chunking rules and separators for document types from different sources |
Three common configuration mistakes
- Symptom: After uploading a scanned PDF document in FastGPT v4.17.0, the parsing result shows missing table fields or empty content. Cause: The
customPdfParseparameter is not enabled, and the default parsing rules cannot adapt to the format of scanned and converted documents. - Symptom: In post-parsing chunking results, consecutive on-site inspection items are split across multiple chunks, making contextual association impossible. Cause: The
chunkSizesetting is too small, and thechunkSplitterrule is not adjusted to merge similar paragraphs. - Symptom: When attempting to export parsed ledger data to Excel format, complete business fields cannot be obtained. Cause: No custom parsing script is configured, and chunked content is not reorganized by dimensions such as institution qualifications, inspection items, and rectification requirements.
How to confirm proper configuration
- A standardized supervision inspection form PDF is uploaded, and the table fields in the parsing result are checked for completeness with no missing items.
- The
chunkSizeparameter is adjusted, then a long penalty case file with detailed paragraphs is uploaded. Chunked content is checked to retain contextual associations for the same inspection logic. - A single law enforcement record document under 100 MB is uploaded, and the parsing task is confirmed to complete within 600 seconds with no timeout errors.
- A custom parsing script is configured, then the parsing result is verified to split fields by dimensions such as institution qualifications, inspection items, and rectification requirements.
The values listed above are common starting points and should be assessed against samples from the relevant use cases.
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.