Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category comes from administrative law enforcement case handling systems, law enforcement recorder footage of law enforcement agencies including market supervision, ecological environment, transportation, emergency management, and urban management, as well as the national enterprise credit information publicity system and departmental supervision databases. Commonly processed documents include on-site inspection records, interrogation records, evidence lists, administrative penalty prior notices, hearing notices, penalty decisions, service receipts, etc., covering both structured forms and unstructured documents. The data update frequency is daily. Core fields focus on illegal facts, evidence information, penalty basis, discretionary results, and service status, with no additional custom statistical units.
What constraints do these characteristics impose on the "document parsing and chunking" link
The mixed structured and unstructured nature of law enforcement documents requires the parsing stage to accurately identify both form fields and the logical structure of document paragraphs. Data from multiple sources has format differences, so unified parsing adaptation rules are needed to avoid parsing deviations from files exported by different systems. Daily high-frequency updated data requires the parsing process to be stable, preventing task timeouts or blockages. Core evidence documents must retain complete context; chunking must not split content that links evidence chains and penalty decisions. Non-text data such as law enforcement recorder footage requires additional parsing rule adaptations and the establishment of associated indexes with text data.
How to Set Configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the typical paragraph length of law enforcement documents, avoiding splitting content that links penalty decisions and evidence |
chunkSplitter | Chunk by paragraph + semantic meaning | Retains the contextual logic of law enforcement records, avoiding splitting continuous interrogation or statement content |
customPdfParse | Enabled | Adapts to PDF documents with form formats exported from law enforcement systems, accurately extracting structured fields and embedded image evidence |
UPLOAD_FILE_MAX_SIZE | 1000 MB | Accommodates single-batch law enforcement recorder footage archive files and multiple associated case documents |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Adapts to the parsing time of large-volume law enforcement data, preventing task interruptions mid-process |
chunkSettingMode | Manual configuration | Adjusts chunking rules for different types of law enforcement documents, adapting to structural differences between on-site records and penalty decisions |
Three Common Misconfigurations
- When using the
chunkmode to call the pushdata API to upload case documents, the interface continuously shows indexing with no progress updates. The cause is that thechunkSizeconfiguration exceeds the system's adaptation range, or the chunking logic does not adapt to the long paragraph structure of law enforcement documents, leading to parsing task blockage. - When uploading a PDF case containing law enforcement scene photos, no image content appears in the parsing result. The cause is that the
customPdfParseconfiguration is not enabled, and the default PDF parsing logic does not extract embedded image evidence. - When uploading HTML documents related to law enforcement generated by Javadoc, the knowledge base has no parsed content. The cause is that no parsing adaptation rules are configured for the corresponding document type, and the default parsing logic cannot recognize structured law enforcement data in the HTML.
How to Verify Proper Configuration
- Upload a single typical law enforcement document, check if the parsed chunks retain complete evidence-related paragraphs, and verify that the chunk length matches the preset configuration range.
- Upload a PDF case containing embedded images, confirm that the parsing result includes image content, and verify that the
customPdfParseconfiguration takes effect. - Call the pushdata API to upload test data, check if the task status is completed within a reasonable time, with no persistent indexing status.
- Upload law enforcement documents in different formats, confirm that the parsing logic adapts to the mixed structure of forms and documents.
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.