What the Data for This Category Looks Like
Core data for urban public transit comes from in-vehicle smart terminals, dispatching command systems, IC card fare collection systems, and maintenance work order systems. Data update rhythms fall into three categories: real-time vehicle location and passenger volume update every second, daily scheduling plans update every early morning, and maintenance work orders and fare transaction records are submitted in real time. The structure of individual data records includes fields such as route number, stop sequence, vehicle status code, work order type, and fare amount. Units include passenger trips, kilometers, minutes, yuan, and some cross-route summary data will include time-period passenger flow statistics.
What Constraints These Characteristics Impose on the "Tool Calling and Plugins" Link
The second-level update data from in-vehicle terminals requires the tool calling timeout threshold to match real-time requirements. An overly long timeout will cause returned data to lag behind real-time dispatching business scenarios. Real-time submitted maintenance and fare data requires plugins to support precise filtering by work order number and time interval, to avoid pulling redundant full historical data. Structured fields such as route number and stop sequence require that standardized parameter formats be passed during tool calling, otherwise data matching failures will occur. Some non-standard fields in cross-route summary data require plugins to have built-in basic field mapping rules to adapt to output differences across different dispatching systems.
How to Set Up Configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | auto | Adapt to multi-tool calling scenarios, automatically match tools related to public transit business |
functionCall | Enable batch calling mode | Support pulling route, work order, and fare business data simultaneously |
| Single tool call timeout | 60 seconds | Match the real-time requirement of second-level update data from in-vehicle terminals |
| Tool call retry count | 2 times | Handle temporary access exceptions caused by fluctuations in the dispatching system |
| Plugin data filtering rules | Filter by route number + time interval | Avoid pulling redundant full historical data |
| Workflow bound parameters | Bind two workflows: maintenance work orders and real-time dispatching | Adapt to different business scenarios for internal office work |
Three Common Mistakes
- Symptom: Tool connection fails with an error prompt indicating connection failure, and logs show unauthorized access or incorrect parameter format. Navicat can connect to the database normally. Cause: The access whitelist for the public transit dispatching system is not configured, or special characters in the passed database account password are not escaped.
- Symptom: Only the default workflow is triggered when calling a tool, and the target workflow cannot be specified to perform corresponding tasks. Cause: The unique identifier of the corresponding workflow is not bound in the
toolChoiceparameter, causing the system to fail to identify the call target. - Symptom: Public transit data returned by tool calling has missing fields or chaotic formatting, and cannot be used for internal office analysis. Cause: The parameter format required by
functionCallis not followed, and non-standard filtering conditions are passed.
How to Confirm the Configuration Is Complete
- Initiate a tool call with a preset route number, and verify that the route number in the returned data matches the passed parameter.
- Trigger the bound multiple workflows, and verify that each workflow returns results corresponding to its business module.
- View the tool call logs to confirm that no connection timeout or format error reports appear.
- Test the batch calling function, and verify that the returned data includes route, work order, and fare business information.
The values provided here are common starting points, and should be evaluated against the sample data relevant to the specific deployment.
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.