What the data for this category looks like
Data for this category comes from administrative law enforcement case handling systems, law enforcement recorder footage, the national enterprise credit information publicity system, and departmental supervision databases. The update frequency is daily. Commonly processed documents include on-site inspection records, interrogation records, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, delivery receipts, and more. The core focus is on identifying illegal facts and penalty decisions, requiring traceable penalty evidence chains and complete document chains. Fields cover law enforcement subjects, illegal circumstances, evidence materials, discretion basis, penalty content, delivery information, and other related content.
What constraints these characteristics impose on the model access and configuration link
Data sources include unstructured image and text content, so multi-modal input and structured document parsing must be supported. Daily updated batch data requires configuration that supports efficient batch calls to avoid single-task timeouts. Diverse document types must strictly correspond to law enforcement process nodes, so the model must accurately recognize field requirements for different documents. Traceability of the evidence chain requires model output to link back to original data sources, with no arbitrary tampering or omission of key information. Rigor of penalty decisions requires configuration to limit generation randomness to avoid content inconsistent with discretion benchmarks.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Accommodate context information from multiple law enforcement records and evidence lists, preventing key evidence chain content from being truncated |
maxTokens | 2500–3500 token | Cover the complete generation length of a single administrative penalty decision letter, meeting output requirements for document details and discretion explanations |
temperature | 0.2–0.4 | Reduce generation randomness, ensuring matching accuracy between penalty decisions and discretion benchmarks, legal clauses |
toolChoice | auto | Support automatic tool call selection, adapting to evidence retrieval and document generation needs across different law enforcement scenarios |
vision | Enabled | Parse unstructured visual data such as law enforcement recorder footage and scanned on-site inspection records, supplementing the completeness of text information |
AIPROXY_API_TOKEN | Exclusive key assigned by the platform | Ensure interface call security, preventing unauthorized access from affecting law enforcement data processing |
Three common misconfigurations
- Phenomenon: Knowledge base search returns non-standard JSON format, causing failure to parse process guidance steps. Cause: No dedicated output format constraint is configured for structured fields of law enforcement documents, and unstructured remark content is mixed into search results generated by the model.
- Phenomenon: Tools cannot automatically select the ones corresponding to law enforcement links during tool calls, leading to process interruption. Cause:
toolChoiceis not configured as auto, or no trigger rules corresponding to law enforcement scenarios are bound to the tools. - Phenomenon: When using mixed model calls, semantic matching results cannot accurately recognize law enforcement-specific terms, resulting in insufficient matching accuracy. Cause: No suitable semantic matching model is selected for specific fields such as law enforcement subjects and illegal circumstances; general models cannot accurately recognize specific expressions in law enforcement scenarios.
How to confirm the configuration is complete
- Upload a single on-site inspection record and law enforcement recorder footage, verify that the model can parse both text and visual content and generate a complete evidence summary.
- Trigger a batch data processing task, check the interface call logs to confirm there are no timeout errors and processing efficiency meets expectations.
- Generate a simulated administrative penalty decision letter, check that the generated content includes required fields such as law enforcement subject, illegal circumstances, penalty basis, and that the format complies with law enforcement document specifications.
- Check the interface call logs for
AIPROXY_API_ENDPOINTto confirm that all interface requests carry valid keys and there are no unauthorized access prompts.
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.