What the data for this category looks like
The data for this category comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, 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 progress, penalty results, and case closure time. Some documents include multimedia materials such as on-site photos.
What constraints these characteristics impose on the "model access and configuration" workflow
The daily updated scale of case data requires model access configuration to adapt to the processing capabilities of batch text and multimedia data, to avoid single-call timeouts. The multi-source heterogeneous document structure (text + on-site photos) requires configuration to support visual parsing capabilities, adapting to unstructured material extraction. The inconsistent field structure of cases requires information extraction rules in the configuration to be flexible, covering field requirements for different types of law enforcement cases. The accuracy requirements for on-site evidence materials require configuring appropriate content verification parameters, ensuring that extracted information such as violation types and responsible parties complies with business specifications.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Adapts to the total content length of single-case ledger summary and multi-case report generation, avoiding output truncation |
vision | Enabled | Supports content parsing of multimedia materials such as on-site photos, extracting key visual information for cases |
temperature | 0.3–0.5 | Reduces randomness of generated content, ensuring that content such as responsible party identification and penalty basis for law enforcement cases complies with business rigor requirements |
AIPROXY_API_ENDPOINT | Calibrated based on actual platform testing | Adapts to the network access environment of the urban management law enforcement system, ensuring stable model call links |
AIPROXY_API_TOKEN | Bound to the corresponding model service | Ensures identity legitimacy for model calls, preventing unauthorized access |
Three common configuration errors
- Generated Markdown tables fail to render properly, with displayed content truncated. The cause is insufficient
maxTokensvalue, which cannot accommodate complete ledger tables and report content, resulting in forced output truncation. - Model calls return empty responsible party fields. The cause is that the
visionconfiguration is not enabled, so relevant responsible party information cannot be extracted from on-site photos, leading to missing extraction results. - Model calls time out, returning connection error status codes. The cause is that the access address configured in
AIPROXY_API_ENDPOINTdoes not match the actual network environment, causing requests to fail to reach the model service normally.
How to verify a successful configuration
- Upload a typical case document containing both text and on-site photos, trigger ledger summary and report generation, and check if the generated content covers all required business fields.
- Test consecutive calls for batch case data to confirm that there are no timeouts or connection errors in the model call links.
- Adjust the
temperatureparameter, compare the rigor of generated content, and ensure that content such as penalty basis and rectification requirements complies with business specifications. - Verify the Markdown format of generated content, confirm that tables and text paragraphs can be rendered normally without content truncation.
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.