This document follows the configuration rules of FastGPT v4.17.0. It targets the party building learning Q&A scenario for medical institution supervision and public health inspection. It outlines specific configurations, constraints, and processing solutions for the document parsing and chunking workflow.
What Data for This Category Looks Like
Typical data sources for this category include health supervision information systems, medical institution electronic registration systems, complaint reporting platforms, and law enforcement inspection records. Processed documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data updates occur monthly. Document structures include structured fields (such as institution name, license number, inspection date) and unstructured explanatory content (such as rectification requirements, penalty items). Fields have no unified fixed units. Some fields use string encoding, while others use date formats.
Constraints Imposed by These Characteristics on the Document Parsing and Chunking Workflow
Multi-source, multi-format data characteristics for this category require the parsing and chunking workflow to support both structured and unstructured content. It must avoid splitting key business fields. Monthly update frequency means real-time parsing is not required. Parsing stability must be maintained to prevent failures caused by format inconsistencies. Business errors directly affect medical institution practice risks. The parsing workflow must fully extract all key information, including qualification numbers and rectification requirements. Missing this information will reduce the accuracy of subsequent party building learning Q&A. Format differences across document types require flexible chunking rules. These rules must adapt to the structural characteristics of different documents.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | This category’s documents contain both structured fields and unstructured explanations. This range preserves complete context for a single inspection record or rectification notice. It avoids splitting key qualification information |
chunkSplitter | Split by paragraph + title | Documents in this category have clear title hierarchies for inspection items, rectification requirements, and other content. Splitting by title preserves business logic integrity. It meets the context association needs of party building learning Q&A |
customPdfParse | Enabled | PDF documents for this category include scanned copies and structured export files. Enabling custom parsing supports content extraction across multiple PDF types. It covers OCR recognition requirements for scanned documents |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Penalty case files for this category may include multi-page law enforcement records and attachments. This upper limit covers complete document packages for single batch uploads |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | Parsing large penalty case files or multi-page scanned documents takes longer. This duration avoids timeout interruptions. It ensures complete content extraction |
chunkSettingMode | Manual configuration | Documents in this category have large format differences. Manual configuration allows flexible adjustment of chunking rules. It adapts to different types of supervision documents |
Three Common Misconfigurations
- After uploading a scanned supervision inspection form, the parsing result lacks inspection item fields. The cause is that the
customPdfParseconfiguration is not enabled. The default parsing cannot recognize OCR text in scanned copies. This leads to failure to extract some content. - After chunking, a single block contains multiple unrelated penalty case entries. The cause is that
chunkSizeis set too small. It splits originally linked law enforcement processes and penalty basis into separate blocks. This breaks business context logic. - When uploading a large rectification notice, the parsing task times out and fails. The cause is that
PARSE_FILE_TIMEOUT_SECONDSis set lower than actual parsing time. It does not adapt to the size characteristics of documents in this category.
How to Verify Configurations Are Correct
- Upload a single standardized supervision inspection form. Confirm the parsing result includes all preset business fields. Adjust
chunkSizeto fit the current document’s range. - Upload a multi-page scanned penalty case file. Verify parsing time does not exceed the preset
PARSE_FILE_TIMEOUT_SECONDSduration. Confirm all page content is fully extracted. - Upload documents of different formats (PDF export version, scanned version). Verify the
customPdfParseconfiguration supports parsing for all file types. No content should be missing. - Test chunked text blocks. Confirm each block has coherent business logic. No splitting across unrelated entries occurs. This ensures context accuracy for party building learning Q&A.
The values provided are common starting points. They must be evaluated against the target samples in actual use.
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.