What This Category of Data Looks Like
Data for this category comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information public disclosure system, and departmental supervision databases. The system updates this data daily. Daily documents include on-site inspection records, inquiry records, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and service receipts. Core fields focus on illegal fact determination, penalty basis, and decision results. Some documents contain unstructured voice text transcribed from law enforcement recorders. The length and structure of individual documents vary significantly.
Constraints on Model Access and Configuration
Decentralized data sources and unstructured voice transcriptions require model access to support multi-source data integration and unstructured content parsing. Diverse document structures with clear core fields require configuring context extraction rules adapted to different document types. Daily updated data volumes require adapting batch processing logic to avoid interface call timeouts. Traceability requirements for penalty case files require ensuring model outputs can be linked to original evidence documents. Serious potential consequences of errors also require configuring strict result verification steps to prevent issues such as penalty revocation.
How to Configure Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context length requirements of splicing multiple law enforcement case files, avoiding truncation of core penalty basis and evidence information |
maxTokens | 4000 token | Controls the length of penalty review opinions generated per round, ensuring outputs comply with case file document format requirements |
quoteMaxToken | 2000 token | Limits the length of referenced original document fragments, ensuring referenced content is precise and non-redundant |
toolChoice | auto | Automatically selects whether to call tools to process voice transcriptions from law enforcement recorders, adapting to multiple input types |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring application of penalty clauses and discretionary benchmarks complies with law enforcement regulations |
AIPROXY_API_ENDPOINT | Fill in the dedicated endpoint provided by the official source | Adapts to the interface call path for internal network environments of law enforcement agencies, ensuring data transmission security |
AIPROXY_API_TOKEN | Fill in the valid token calibrated through actual testing | Verifies interface call permissions, ensuring only authorized services can access model resources |
Three Common Configuration Errors
- Symptom: Single-round review generation results are repetitive and fail to cover all case file evidence. Identical requests initiated within one consecutive minute return completely consistent results. Cause: Improper
temperatureparameter value, causing the model's generation logic to become fixed, without adjusting outputs based on the unique illegal facts of the current case file. - Symptom: Interface call fails after configuration is completed, returning an error message about permission verification failure. Cause: Incorrect
AIPROXY_API_TOKENconfiguration. The dedicated valid token corresponding to the law enforcement agency was not used, resulting in interface access being blocked. - Symptom: Footage from law enforcement recorders is not correctly parsed, and the review results do not mention relevant evidence. Cause: The
visionconfiguration item was not enabled, and visual recognition capabilities for processing non-text law enforcement evidence were not activated.
How to Confirm Configurations Are Correct
- Upload a typical case file containing on-site inspection records and a penalty decision, trigger model review, and check whether the generated results link to core evidence fragments from the original document.
- Initiate two identical case file review requests consecutively within a one-minute interval, compare whether there is a reasonable difference between the two output results, to verify that the
temperatureparameter configuration is effective. - View the interface call log, confirm that there are no spelling errors in the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations, and that the interface call path is correct. - Test a case file containing transcribed text from a law enforcement recorder, confirm that the model can correctly parse the transcribed content and include it in the review logic.
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.