What data looks like for this category
The daily documents for this category include maintenance plans, patrol logs, defect disposal forms, road construction permits, road damage compensation forms, etc. Data typically comes from highway maintenance management systems, bridge and tunnel monitoring systems, road patrol systems, and traffic flow monitoring data, with a daily update frequency. Document structures cover fields such as facility ID, location coordinates, disposal records, and approval status. Field formats vary across different document types, and some semi-structured documents contain free-text descriptions.
What constraints these characteristics impose on model access and configuration
Multi-source heterogeneous data sources require model access configuration to adapt to different data source formats, to avoid field parsing errors. Daily updated data sources need matching synchronization and context length settings to ensure that the latest case materials can be covered during review. Diverse document types require configurations that support flexible text understanding logic, balancing structured field extraction and free-text review. At the same time, review results directly relate to road safety and financial compliance, so configurations need to reduce the randomness of model outputs to ensure consistency of conclusions.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of daily updated multi-source case materials, covering all documents in a single review batch |
maxTokens | 2000–3000 token | Ensures complete output of review results for submission materials and case files, avoiding truncation of key conclusions |
temperature | 0.1–0.3 | Reduces randomness to ensure consistency of review conclusions, matching the strict requirements of road law enforcement |
toolChoice | auto | Adapts to different case file formats, supporting structured data extraction and free-text review |
| Call timeout | 600 seconds | Adapts to the maximum waiting time for single-batch case review, avoiding review failure caused by link interruption |
AIPROXY_API_ENDPOINT | Fill in according to deployment environment | Adapts to local or cloud-based model call links, ensuring compliance of data transmission |
Three common mistakes
- Model icons do not display in workflows, and file uploads prompt network errors: The cause is that the FastGPT version is incompatible with the currently configured model protocol, and the adapted workflow node plugin has not been updated.
- Large model nodes return 500 Gateway forwarding error because service is disconnected: The cause is that the model service connection is interrupted, and link health check and automatic reconnection logic have not been configured.
- Custom model vendor icons fail to load: The cause is that the model icon file has not been placed in the specified static resource directory, or the access address does not correctly point to the icon source.
How to confirm the configuration is complete
- Check the status indicator of the model node to confirm there are no error prompts.
- Upload a single typical case file, trigger the review process, and check whether the output results cover the preset review points.
- Test the model call link, verify that the interface returns a 200 response code with no abnormal errors.
- Check the static resource directory to confirm that the model vendor icons load normally.
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.