Reference only; verify against the target FastGPT release and deployment documentation.
What this category of data looks like
Data for this category typically originates from daily work outputs of urban management law enforcement bureaus, subdistrict comprehensive law enforcement teams, environmental sanitation centers, and grid inspectors. Data sources include urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, and public complaint tickets. Updates occur daily. Daily documents cover inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. These documents typically include fields such as case number, law enforcement subject, on-site description, rectification requirements, and penalty results. Some documents include multimedia content.
Constraints on model access and configuration
This category has scattered data sources and includes multimodal content such as text and images. The daily update requirement means model access must adapt to a high-frequency data refresh rhythm. Different document types have widely varying fields, so recall and parsing logic adapted to non-standard text must be configured. The presence of on-site photos and other multimedia content requires the model to support visual information processing. Law enforcement scenarios have strict accuracy requirements for answers. Improper configuration may lead to missing evidence or judgment bias, which disrupts case disposal workflows.
How to configure the settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context requirements of multi-document splicing for this category, avoiding loss of key case information due to context truncation |
maxTokens | 2000–3000 token | Balances answer length and the completeness of policy basis citations in law enforcement scenarios, avoiding overly long output that affects on-site use |
vision | Enabled | Supports parsing of multimedia content such as on-site photos and scanned copies of law enforcement documents, matching the multimodal data characteristics of the category |
temperature | 0.2–0.4 | Reduces randomness, ensures the accuracy of policy basis citations, and avoids judgment bias in law enforcement scenarios |
AIPROXY_API_ENDPOINT | Fill in the actual interface address according to the deployment environment | Adapts to model call requirements for local or private deployments, ensuring data does not cross compliance boundaries |
AIPROXY_API_TOKEN | Configure a dedicated access key | Ensures the security of model calls, preventing unauthorized access to law enforcement-related data |
Three common misconfigurations
- Attempting to select a specialized model fails to target the desired model: The cause is that the deployment address of the corresponding model is not bound in the
AIPROXY_API_ENDPOINTconfiguration, or the key permission does not grant access to the corresponding model. - A
build/model/main.go:79error occurs when starting the OneAPI container: The cause is that the configuredAIPROXY_API_TOKENis invalid or the interface address cannot be accessed normally. Check the correctness of the key and endpoint configuration. - The deployed rerank model does not take effect: The cause is that the associated rerank call logic is not configured, or the interface address of the rerank model is not correctly filled in the corresponding configuration item, resulting in failure to call the rerank service to improve recall accuracy.
How to verify successful configuration
- Upload a single law enforcement document and on-site photo, test whether the model can parse both text and image content, and verify that the
visionconfiguration is effective. - Send a policy basis Q&A request, check whether the context length of the returned result matches the preset
maxContextrange, with no obvious truncation. - View the model call log, confirm that the request address and key match the configured
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKEN, with no failed call records. - Conduct a multi-round Q&A test, observe whether the randomness of the answer falls within the range set by
temperature, and avoid overly divergent content.
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.