What This Category of Data Looks Like
Data originates from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. Content is updated daily. Common documents include on-site inspection records, interrogation records, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decision letters, and delivery receipts. Core fields focus on illegal fact determination, penalty basis, document number, and party information. Single document length varies widely, from a few pages of records to dozens of pages of complete case files.
What Constraints Do These Characteristics Impose on Document Parsing and Chunking
Law enforcement case files come from structured case handling system data, unstructured record text, and transcribed footage content. Parsing tools must support multi-format input. The daily updated business rhythm requires parsing processes to have low latency. This avoids delaying case handling progress. Single document length varies widely, from a few pages of records to dozens of pages of complete files. Chunking logic must accommodate both short-text records and long-text decision letters. Document structures center on illegal facts and penalty bases. Chunking must retain key contextual associations. Splitting core evidence chains or penalty decision content may reduce the accuracy of subsequent service consultation and response.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
chunkSize | 800–1200 characters | Adapts to the mixed text length of records and decision letters in law enforcement case files, avoids splitting key evidence sentences or legal clauses |
chunkSplitter | Split by semantic paragraphs | Law enforcement case files contain numerous record question-and-answer pairs and legal clause references. Splitting by semantic paragraphs preserves contextual integrity, improving the accuracy of subsequent consultation and response |
customPdfParse | Enabled | Most law enforcement documents use standard PDF formats. Enabling custom parsing accurately extracts text content, eliminating interference from irrelevant information such as headers and footers |
PARSE_FILE_TIMEOUT_SECONDS | 300–600 seconds | Covers the parsing time required for long case files, prevents parsing failures due to timeout, and adapts to the varying length of law enforcement case files |
chunkSettingMode | Manual configuration | Adjust chunking parameters for different types of law enforcement documents, accommodating differences between short-text records and long-text decision letters |
Three Common Misconfigurations
- Symptom: File parsing API calls return timeout errors, or parsing progress stalls without updates. Cause: The
PARSE_FILE_TIMEOUT_SECONDSparameter was not adjusted. Long case file parsing time exceeds the default threshold. - Symptom: Parsed text retains large amounts of header and footer content, or key evidence fields such as illegal facts and penalty bases are missing. Cause: The
customPdfParseconfiguration was not enabled. The default PDF parser does not adapt to the standard format of law enforcement documents.
Reference only; verify against the target FastGPT release and deployment documentation.
How to Verify Proper Configuration
- Upload a single typical law enforcement document, review the parsed text content, and confirm that core fields match the original document.
- Adjust the
chunkSizeparameter, upload the same document again, and compare chunking results to confirm the chunking logic meets expectations. - Batch upload different types of law enforcement case files, verify that the parsing process completes normally across all document types.
- Review parsing logs to confirm no timeout or format error prompts appear.
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.