What Data for This Category Looks Like
Bridge defect identification data has three main sources: 4K high-definition inspection images captured by drones, real-time monitoring data from bridge structural health monitoring sensors, and structured record documents from manual inspections. Drone inspection images are updated quarterly or semi-annually. A single frame can reach several hundred megabytes in size. Sensor data updates at minute-level intervals, and includes numeric fields such as stress and displacement. Manual inspection records include enumerated fields such as defect location (pile number/longitude and latitude), defect type, and severity level. The unit for pile number is meters, and the unit for stress is megapascals. All data is stored as independent files or structured data tables, with no unified packaging format.
Constraints for Tool Calling and Plugins
The large file size of drone inspection images requires the tool calling workflow to support large file upload and transfer. Differences in format across multiple data sources require plugins to adapt to combinations of multiple tools including image recognition, numeric parsing, and text extraction. The high-frequency updates of real-time sensor data require tools to support scheduled pulling or streaming calls. The enumerated field requirements of structured records require plugins to return standardized structured results to support subsequent alarm analysis. A single inspection may generate multiple images, so tool calling must support batch file processing to avoid excessive resource usage per request.
Configuration Settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
| Upload file size limit | 1000–2000 MB | Meets storage and transfer requirements for a single 4K inspection image |
toolChoice | auto | Automatically matches the built-in image recognition and text parsing tool chain |
| Tool call timeout | 120–180 seconds | Covers preprocessing and recognition time for large-volume images |
| Number of files per call | First 10 items | Prevents excessive resource usage per request |
functionCall | Enabled | Supports structured return of defect recognition results and location parameters |
Three Common Configuration Mistakes
- The error "file size limit exceeded" appears during tool calling. This occurs because the upload file limit configuration when starting the container was not adjusted. The default limit is 2 MB, which cannot support high-definition bridge inspection images.
- When calling the upload file tool, only fixed links are supported, and custom variables cannot be passed. This happens because the variable reference switch for tool parameters was not enabled, or the file source variable was not correctly bound.
- The recognition result fields returned by the plugin are empty. This occurs because
functionCallwas not configured to enable structured output, causing non-standard text returned by the tool to be unparseable.
How to Verify Correct Configuration
- Upload a single 1500 MB inspection image, and check whether the tool calling workflow can complete preprocessing normally.
- Test passing the
toolChoiceparameter as auto, and check whether the tool automatically calls the image recognition and text parsing plugins. - After configuring
functionCall, check whether the returned results include structured defect type and location fields. - Access the MinIO storage bucket, and check whether uploaded inspection images and sensor data are saved normally.
The values provided are common starting points, and should be evaluated against specific operational 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.