Reference only; verify against the target FastGPT release and deployment documentation.
What the data for this category looks like
Typical documents for this category include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, penalty case files, and more. Data sources include health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. Data update frequency is monthly. Daily processed documents contain both standardized structured content and free-text explanations, covering core fields such as institution qualification information, on-site inspection records, rectification requirements, penalty content, and more. No unified fixed format template exists. Some documents include table-style inspection items.
Constraints for the document parsing and chunking link
Document characteristics for this category create multiple constraints for the parsing and chunking process. First, documents mix structured tables and free text. Accurate distinction of content types is required to avoid splitting errors. Second, data update frequency is low, but a single processing batch may include multiple associated documents. Accurate splitting of individual documents is required to avoid cross-file confusion. Third, errors in core fields such as institution license numbers and rectification items will directly lead to law enforcement risks. Chunking must retain contextual associations for key information and must not break complete law enforcement logic. Finally, file formats vary across data sources. Support for different types of document parsing needs is required.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Documents in this category are mostly standardized inspection forms and long-text rectification documents. This range can accommodate complete single inspection items or entire rectification explanations, avoiding splitting key law enforcement information |
chunkSplitter | Split by paragraph + model-based paragraph recognition | Documents in this category include structured table paragraphs and free-text explanations. Model-based paragraph recognition can accurately split different types of content and retain logical associations between paragraphs |
customPdfParse | Enable pdf-marker v2 compatibility mode | Official documents of this category are mostly in PDF format. This mode can accurately extract text and embedded table content, meeting the parsing requirements of standardized law enforcement documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | When uploading a single penalty case file or multiple associated documents combined, parsing takes a long time. This setting reserves sufficient time to complete full text extraction |
chunkSettingMode | Manual configuration | Document formats for this category vary widely. Manual configuration allows flexible adjustment of chunking rules to adapt to different types of files such as inspection forms and notices |
Three Common Mistakes
- Symptom: Calling the
customPdfParserelated interface returns an interface not found error, or table content is missing from the parsed PDF document. Cause: The pdf-marker v2 version is not deployed correctly, or access permissions for the corresponding parsing interface are not enabled. - Symptom: After uploading a CSV-format supervision inspection form, the parsing result only returns partial inspection items, with insufficient field count. Cause: Chunking rules are not adjusted for structured tables. The default chunking logic splits table rows, causing complete inspection items to be split into different chunks.
- Symptom: Only some files are successfully parsed after uploading multiple files, or the number of parsed files does not match the number of uploaded files. Cause: The multi-file upload parameter format is not configured as required, and the content array structure is not correctly nested.
How to Verify Correct Configuration
- Upload a standard medical institution supervision inspection form in PDF format. Check if the parsed text fully includes all inspection items and conclusions, with no obvious text breaks or missing content.
- Call the document parsing interface. Check if the returned chunk results include core fields such as institution name and practice license number, with no key information omitted.
- Adjust the
chunkSizeparameter, then upload a long-text rectification notice. Confirm that complete rectification requirement paragraphs are not split across chunks. - Upload multiple files in different formats (PDF, Excel). Confirm that all files are successfully parsed, with no timeouts or error prompts.
The values provided 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 2026-09-28.