What this category of data looks like
This category of data 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, questioning transcripts, evidence lists, prior administrative penalty notification letters, hearing notification letters, penalty decisions, and delivery receipts. The data structure centers on illegal facts, law enforcement procedures, and penalty decisions. It includes core fields such as case-involved subjects, law enforcement procedures, evidence associations, legal citations, and penalty results. No custom unit fields are present.
What constraints do these data characteristics impose on model access and configuration?
The data characteristics of this category impose clear constraints on model access and configuration. Dispersed data sources that include unstructured imagery require configuration that supports multimodal content parsing. Daily high-frequency updated batch data requires adaptation to stable interface call parameters. Mixed storage of multiple types of law enforcement documents requires configuration that supports accurate recall of specified documents. The document structure centered on penalty decisions requires that model-generated content strictly matches original evidence and legal clauses. Generation freedom and context length must be limited to avoid content inconsistent with law enforcement regulations.
How to set the configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Administrative case files contain multiple associated documents, and the full law enforcement process and evidence information must be accommodated to avoid loss of critical content due to context truncation |
maxTokens | 4000–6000 token | Limit the length of penalty documents generated by the model in a single run, to ensure content complies with standard law enforcement document formats |
quoteMaxToken | 2000–3000 token | Ensure the recalled evidence document content is complete, providing accurate citation basis for the model and supporting the legality of penalty decisions |
temperature | 0.1–0.3 | Administrative penalty case file handling requires strict matching with regulations and original documents. A lower temperature value reduces randomness of generated content and improves accuracy |
vision | Enabled | The data includes unstructured content such as law enforcement recorder footage, and multimodal parsing capability must be configured to extract law enforcement information from the footage |
AIPROXY_API_ENDPOINT | Fill in the standard interface address according to the deployment environment | Adapt to local or cloud FastGPT v4.17.0 deployment environments, ensuring normal model call links |
AIPROXY_API_TOKEN | Fill in the exclusive token generated during deployment | Ensure identity verification security for model calls and prevent unauthorized access |
Three Common Configuration Mistakes
- Interface calls return empty results or fail to recall specified documents. Cause: Associated parameters that support specified document recall are not correctly configured, causing the model to fail to accurately match target law enforcement documents.
- Model-generated content deviates significantly from original law enforcement documents, with incorrect legal clause citations. Cause: The
temperatureparameter is set too high, causing the randomness of generated content to exceed the range required by law enforcement regulations. - Content cannot be parsed when processing law enforcement recorder footage. Cause: The
visionconfiguration item is not enabled, and multimodal parsing capability is not activated, making it impossible to recognize law enforcement information in the footage.
How to Confirm Configuration is Complete
- Submit a test request that includes law enforcement recorder footage, check whether the model can correctly extract law enforcement information from the footage, and confirm that the
visionconfiguration is active. - Upload multiple law enforcement documents, submit a recall request specifying a single document, check whether the returned results only contain content from the target document, and confirm that the specified recall configuration is active.
- Adjust the
temperatureparameter to above 0.5, submit a test request, compare the randomness changes of the generated content, and confirm that the parameter configuration is active. - Fill in
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, then submit a call, check that the interface returns a status code of 200 and no identity verification error messages, and confirm that the interface configuration is active.
The values provided here are common starting points, and should be evaluated 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.