What data for this category looks like
Data for this category comes primarily from urban management law enforcement platforms, urban operation grid platforms, video surveillance systems, and public complaint work orders. Update frequency is daily. Commonly processed documents include inspection records, case filing approval forms, on-site photos, rectification notices, penalty decisions, and closing reports. Typical data fields include case identifiers, descriptions of violations, links to on-site supporting materials, information about responsible parties, and rectification execution progress. Data covers both text and visual materials.
What constraints do these characteristics impose on model access and configuration
This category’s data includes text and visual materials, and sources are dispersed. This requires model access configuration to support visual content parsing. Daily updated data volume must be handled with incremental synchronization mechanisms. This avoids excessive resource usage from full data pulls. Differences in structure across document types require configuration to adapt to parsing logic for multi-format inputs. Law enforcement review has high accuracy requirements. This limits the randomness of model generation, to avoid insufficient case evidence or law enforcement disputes caused by judgment biases.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category includes visual materials such as on-site photos. Image content must be parsed as a review basis. |
maxContext | 8000–12000 token | Case file review requires integrating multiple related documents. Sufficient context fully associates the evidence chain and avoids truncating key information. |
temperature | 0.2–0.4 | Law enforcement review requires rigor and accuracy. Lower temperature reduces generation bias and improves judgment consistency. |
AIPROXY_API_ENDPOINT | Configure per the official access address of the selected large model | Ensures connectivity for model calls, and complies with FastGPT v4.17.0 access specifications. |
AIPROXY_API_TOKEN | Fill in the authorization key for the corresponding model | Completes identity verification for model access, and ensures normal call permissions. |
maxTokens | 1500–2000 token | Review results must clearly present judgments and supporting basis. Moderate output length meets case file recording requirements. |
Three common configuration errors
- Phenomenon: QA search tests prompt "No available channel for model gpt-4o-mini under current group default". Cause: The
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKENfor this model is not configured in the corresponding group, or the key and access address do not match. - Phenomenon: The re-ranking function cannot select a specified model. Cause: The re-ranking adaptation configuration for the corresponding model is not enabled, or the access permission for the re-ranking model is not enabled in the system.
- Phenomenon: Connection failure prompts appear when accessing the model via a third-party proxy. Cause:
AIPROXY_API_ENDPOINTis configured with an incompatible proxy address, orAIPROXY_API_TOKENis not correctly associated with the proxy's authorization information.
How to confirm configuration is complete
- Upload a typical urban management case file that includes on-site photos and a filing approval form. Trigger a review task, and check if the model correctly parses image content and associates text evidence.
- View the FastGPT model call log. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correctly read, with no authentication-related errors. - Adjust the
temperatureparameter to 0.3. Verify that the output remains rigorous, with no overly divergent vague judgments. - Test integrating context from 3 or more related case files. Confirm that the
maxContextsetting covers all materials, with no key content truncated.
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.