Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
This category of data typically comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, and citizen complaint work orders. The update frequency is daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification notices, penalty decisions, and case closure reports. Fields cover inspection time, location, problem description, case filing number, responsible entity, rectification requirements, and more. Some documents include multimedia files.
Constraints on model access and configuration
Daily updated batch data requires model access to support incremental synchronization and batch scheduling, to avoid excessive resource consumption from full-volume data pulling. Multiple document types include both text and multimedia materials, so adapted visual and text processing parameters must be configured to ensure evidence materials such as on-site photos can be correctly parsed. The diversity of fields and specialized terminology requires configuring domain-specific prompt words during model access to improve understanding accuracy for law enforcement scenarios. The timeliness requirement for on-site disposal must match the time window of the law enforcement process, to avoid model response timeouts affecting case processing flow.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context requirements for multi-round case flow, avoiding truncation of key process information such as case filing, rectification, and case closure |
maxTokens | 4000 token | Matches the average length of a single case document, ensuring long texts such as penalty decisions and case closure reports can be fully generated |
vision | Enabled | Supports parsing multimedia evidence materials such as on-site photos and surveillance screenshots, meeting model call requirements for on-site law enforcement links |
toolChoice | auto | Automatically triggers tool calls, matching scenarios such as querying law enforcement basis and locating responsible entities during case processing |
temperature | 0.3–0.5 | Reduces random output, ensuring standardization and accuracy of law enforcement document generation |
AIPROXY_API_TOKEN | Fill in the key allocated by the service provider | Completes identity verification to ensure the security of interface calls |
Three common configuration mistakes
- An interface error occurs when calling o1 series models, with a prompt indicating parameter incompatibility. The cause is that no specialized system prompt word is configured for the urban management law enforcement scenario, causing the model to fail to recognize specialized law enforcement terminology and triggering model-side verification failure.
- The AI does not trigger tool calls during the tool call phase, and answers directly on its own. The cause is that
toolChoiceis configured to a fixed non-automatic mode, or the requirement to call law enforcement-related tools is not clearly stated in the system prompt word. - A communication error occurs after access, returning a timeout status code. The cause is that
maxContextis set too large, causing the model processing time to exceed the interface timeout threshold, or no reasonable request retry mechanism is configured.
How to confirm successful configuration
- Upload a single inspection record and on-site photo, verify whether the model can correctly extract location and problem description and generate a preliminary case filing suggestion, confirming that the
visionand text parsing functions work normally. - Trigger a tool call test, input "Query the law enforcement basis for illegal occupation of a certain road section", verify whether the model automatically calls the corresponding tool, confirming that the
toolChoiceconfiguration is correct. - Check the interface log to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare configured correctly, with no 401 or 404 errors. - Submit batch case data, verify that the model processing speed meets the daily update scheduling requirements, confirming that
maxContextandmaxTokenssettings do not exceed interface limits.
The values provided are common starting points and should be measured against samples from the target deployment.
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.