What the Data for This Category Looks Like
The daily documents for this category primarily include inspection plans, inspection target lists, inspection item lists, on-site inspection records, rectification notices, and inspection result announcements. Data is sourced from the "Double Random, One Public" supervision platform, enterprise credit information public disclosure system, industry supervision systems, and the law enforcement officer directory. The update frequency is monthly. Document structures include structured number fields and text description fields. Some documents contain tabular lists or item lists, with most fields covering supervision target identifiers, law enforcement officer information, inspection items, and rectification requirements.
What Constraints Do These Characteristics Impose on the Document Parsing and Chunking Stage
Batch data updated monthly requires the parsing process to support batch processing and fast response. Documents from multiple sources and formats require adaptation to different parsing rules to avoid discrepancies between structured list and unstructured record parsing. The accuracy of law enforcement documents directly impacts supervision workflows. Parsing and chunking must retain contextual logic to avoid separating inspection items from rectification requirements. Field differences across document types require flexible chunking configurations to adapt to splitting needs for lists, records, announcements, and other document types.
How to Configure the Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the text length of on-site inspection records and rectification notices, avoiding disruption of law enforcement logic |
chunkSplitter | Chunk by paragraph + heading | Matches the hierarchical structure of supervision documents, retaining contextual links between inspection items and corresponding rectification requirements |
customPdfParse | Enabled | Adapts to multi-source PDF format supervision documents, restoring structured content such as tables and lists |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Supports batch uploading of large documents such as monthly updated inspection target lists and inspection result announcements |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large law enforcement record documents, avoiding timeout interruptions for batch tasks |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different document types (lists/records/announcements) to meet differentiated splitting needs |
Three Common Misconfigurations
- No parsing results after uploading supervision documents, with no errors in the backend. Cause: The
customPdfParseconfiguration is not enabled, or theUPLOAD_FILE_MAX_SIZEsetting is smaller than the actual uploaded file size. - Service timeout when parsing large monthly inspection record documents. Cause: The
PARSE_FILE_TIMEOUT_SECONDSsetting is lower than the actual parsing time of the document, and timeout parameters were not adjusted for batch tasks. - Chunking results disrupt law enforcement logic, failing to match inspection items with corresponding rectification requirements. Cause: The
chunkSplitterconfiguration is set to chunk by fixed characters, without splitting by document hierarchy.
How to Verify the Configuration Is Correct
- Upload a single typical document (such as a monthly inspection plan PDF), view the parsed chunk list, and confirm that headings and paragraphs are correctly split with no content omitted.
- Upload a batch inspection target list file larger than 500 MB, confirm that the upload progress is normal with no timeout prompts.
- Call the FastGPT v4.17.0 API to upload a document, verify that the returned parsing task status code is successful, and that the chunked content includes complete law enforcement officer information and inspection items.
- Adjust the
chunkSizeparameter, compare chunk lengths across different values, and confirm that the expected text range is met.
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.