What this category of data looks like
Daily data for urban sanitation and urban management law enforcement cases comes from urban management law enforcement platforms, urban operation grid platforms, video surveillance, and public complaint work orders, with a daily update frequency. Typical documents include inspection records, case filing approval forms, on-site photos, rectification orders, penalty decisions, and case closure reports. Common data fields include case number, violation type, responsible entity, rectification requirements, disposal progress, and mostly consist of structured and unstructured content related to on-site handling.
Constraints imposed by these characteristics on model access and configuration
This category of data has scattered sources and includes multimodal content. Model access must support multimodal recognition and multi-source data association. Daily updated inspection and work order data require automatic synchronization processes to ensure knowledge base content timeliness. Documents related to on-site evidence collection require the model to accurately identify law enforcement details in images, preventing insufficient case evidence due to missing information. Party building learning Q&A must strictly align with law enforcement regulations. Model outputs must be linked to specific law enforcement documents to reduce law enforcement dispute risks.
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 on-site photos and video surveillance footage, requiring support for multimodal content recognition |
temperature | 0.2–0.4 | Party building learning Q&A requires strict policy statement accuracy, avoiding content generated that deviates from regulations |
maxContext | 8000–12000 token | Requires association with multiple law enforcement documents such as inspection records and penalty decisions to ensure context coherence |
AIPROXY_API_ENDPOINT | Corresponding exclusive access address for urban management systems | Adapts to the network isolation and permission control requirements of urban management departments |
quoteMaxToken | 2000–3000 characters | Requires retaining sufficient original text of law enforcement documents as reference basis, avoiding missing evidence |
toolChoice | auto | Requires automatic invocation of knowledge base retrieval tools to match party building policies and law enforcement basis |
Three Common Configuration Mistakes
- Phenomenon: When calling gpt-4o, the system returns "No available channels under the current default group". Cause: No exclusive model channel is bound to the current scenario group, or the API key for the corresponding model is not configured
- Phenomenon: Retrieval results only return vaguely matched policy summaries, without referencing specific law enforcement documents. Cause: The model is not restricted to answering only using retrieved knowledge base content, or the mandatory reference configuration is not enabled
- Phenomenon: The model fails to recognize relevant content after uploading on-site photos. Cause: The
visionconfiguration is not enabled, or a model version that does not support multimodality is used
How to Verify Successful Configuration
- Input party building-related law enforcement case questions for testing, and check if the returned content includes the original text of law enforcement documents from the specified knowledge base
- Upload on-site inspection photos, and confirm that the model can recognize law enforcement logos or violation scenarios in the photos
- View the model call log, and confirm that the actual values of configuration parameters such as
maxContextandtemperaturematch the preset values - Simulate importing that day's inspection records and complaint work orders, and check if the data synchronization and retrieval processes are triggered normally
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.