What the data for this category looks like
Civil aviation airport internal office data sources include flight dispatch systems, ground crew scheduling management systems, baggage sorting monitoring systems, maintenance equipment logging systems, and others. Data update rhythms are divided into real-time (flight dynamics, stand allocation), daily (ground crew schedules), and periodic (monthly maintenance reports). The structure of a single document includes fields such as flight number, takeoff and landing time, stand number, ground crew ID, equipment code, and others. Field types cover strings, timestamps, and integers. Units mostly use internationally recognized time formats and alphanumeric mixed codes.
What constraints these characteristics impose on the tool calling and plugins link
Tool calling for airport internal office needs to connect to multiple heterogeneous systems. Tools with high real-time requirements such as flight queries and stand scheduling must adapt to short-interval data pulling. The multi-field document structure requires that tool input parameters must accurately match required fields; missing any key information will cause the call to fail. Differences in update rhythms across data sources require plugins to support flexibly configurable refresh cycles to avoid data lag or repeated pulling, and must be compatible with authentication protocols of different systems.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool call timeout | 60–120 seconds | Matches the update delay of real-time airport flight data, prevents task interruptions caused by system response timeouts |
| Plugin data source refresh interval | 5–15 minutes | Adapts to the update rhythms of airport ground crew schedules and maintenance logs, balances data timeliness and system load |
| Parameter verification mode | Strictly match required fields | Avoids call failures caused by missing key information such as flight numbers and stand numbers |
toolChoice | Automatically match corresponding tools | Covers multiple office scenarios including ground crew dispatching, flight queries, maintenance log extraction, and others |
| Context cache duration | 30–60 minutes | Matches the average duration of a single employee's internal office session, reduces repeated parameter input |
Three common errors
- Text extraction tools return the
400 Invalid JSON payload received. Unknown nameerror. The cause is that the passed parameters contain non-standard nested fields from airport maintenance logs, and the request body was not encapsulated in the format required by the tool. - The PgVector plugin cannot retrieve historical flight data after an upgrade. The cause is that the vector storage dimension parameter was not adjusted, so it cannot match the text length of airport flight data.
- No context information was associated when calling the FastGPT workflow API. The cause is that the session identification parameter was not correctly passed in the request, so the workflow cannot associate the employee's previous office requests.
How to confirm the configuration is complete
- Initiate a simulated ground crew dispatching tool call, check that the returned results include correct stand numbers and ground crew scheduling information.
- View the plugin's data source refresh logs, confirm that data is automatically updated at the set 5–15 minute interval.
- Initiate a cross-session tool call request, check that the system can correctly associate context parameters from previous sessions.
- Verify that the
toolChoiceconfiguration takes effect, the system can automatically switch the corresponding tool type based on input content.
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.