What this category of data looks like
This category of data typically originates from urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, and public complaint work orders. The update frequency is daily. Daily documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Common data fields cover case number, violation type, responsible party, rectification execution status, and on-site evidence collection information. Most data uses a format combining structured forms and unstructured text and images.
Constraints for model access and configuration
This category of data mixes structured forms and unstructured text and images, and is often updated in real time across multiple platforms. Model access must therefore support visual recognition capabilities to process on-site photo materials. A reasonable context window must be configured to integrate multi-source work orders, inspection records and other information, to avoid missing key law enforcement information. Daily updated case data has fluctuations, so a single-processing token limit must be set to prevent excessive resource usage. The fixed field format of law enforcement documents requires the model to accurately identify corresponding fields to support business process guidance.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category of data includes unstructured visual materials such as on-site photos. Visual capabilities must be enabled to recognize evidence collection content |
maxContext | 8000–12000 token | Multi-source inspection records, work orders and law enforcement documents need to be integrated. This range covers typical multi-source information integration requirements |
maxTokens | 2000–3000 token | Limit the single response length, adapt to the concise output requirements of law enforcement case guidance, and avoid resource overload |
temperature | 0.3–0.5 | Reduce randomness to ensure that law enforcement process guidance content complies with fixed business specifications |
AIPROXY_API_ENDPOINT | Fill in the model proxy address corresponding to the urban management law enforcement scenario | Adapt the call path of the category-specific model to ensure normal request routing |
quoteMaxToken | 1500 token | Limit the length of referenced document fragments to ensure that extracted law enforcement documents and inspection record information are accurate and not redundant |
Common Misconfigurations
- Issue: The model returns an
invalid tokenerror, or prompts that the model interface cannot be connected. Cause: TheAIPROXY_API_TOKENparameter is not configured correctly, or the proxy address does not match the actual deployment path of the model. - Issue: The model cannot recognize evidence collection content in on-site photos, and the output result is empty or incorrect. Cause: The
visionconfiguration item is not enabled, or the access parameters of the visual model are not configured synchronously. - Issue: Context overflow error occurs when processing case data, and the service restarts repeatedly. Cause: The
maxContextormaxTokensvalues are not limited, causing the total token amount for single processing to exceed the model's carrying limit.
How to Verify a Successful Configuration
- Submit a test call to the configured model, upload a single on-site photo and inspection record, and confirm that the model can correctly recognize the photo content and associate it with inspection information.
- Initiate a simulated case guidance request, and check whether the format and fields of the returned result comply with the fixed specifications of law enforcement documents.
- Initiate multiple batch case data processing requests consecutively, and confirm that the service has no errors and the response is stable.
- Verify the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters to ensure they fully match the deployed model proxy information.
The values listed here are common starting points, and should be validated against the samples used in the specific implementation.
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.