What This Category of Data Looks Like
Tunnel alert filtering data comes from temperature sensors, smoke sensors, stress sensors, video surveillance devices deployed along tunnel routes, and tunnel electromechanical control systems. Data update frequency ranges from seconds to minutes. Each alert document includes fields such as alert trigger time, associated device ID, alert type, deviation threshold, measuring point longitude and latitude, and original alert message. The temperature alert field uses degrees Celsius as its unit. The stress alert field uses megapascals as its unit. The device ID is a 16-digit hexadecimal string. The original alert message is JSON format raw data reported by the device.
What Constraints These Characteristics Impose on Tool Calling and Plugin Workflows
The second-to-minute update frequency requires tool calling response latency to align with the alert processing window. Otherwise, alert filtering delays will occur, failing to meet real-time patrol requirements. Mixed multi-type alert data includes numerical deviations, string device IDs, and JSON format original messages. This requires plugins to complete field format verification before calling, to prevent call failures caused by type mismatches. Original alert message structures vary across different device vendors. Plugins must support dynamic parsing of messages in different formats to complete standardized filtering. For scenarios with batch-reported alerts, tool calling concurrency configuration must adapt to centralized data streams, to avoid request backlogs.
How to Configure Settings
The following configurations are developed for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool calling timeout | 300 seconds | Matches the second-to-minute update frequency of tunnel alerts, prevents valid alerts from being dropped due to timeout |
| Plugin request retry count | 2 times | Balances call success rate during network fluctuations and latency requirements for real-time processing |
toolChoice | auto | Allows the system to automatically match tools adapted to multi-type tunnel alerts, covering different alert scenarios |
functionCall | Only specify the tunnel alert filtering plugin | Force calls to preset dedicated tools, avoids interference with filtering processes from unrelated function calls |
| Field verification rules | Verify device ID format and alert value unit | Adapts to the fixed format of tunnel alert fields, filters invalid abnormal alert data |
| Plugin data parsing template | Calibrate based on actual measurements | Adapts to differences in original alert message structures across device vendors, completes standardized conversion |
Three Common Misconfigurations
- Symptom: API call returns
400 Bad Requestwith promptchatId invalidor lost session context. Cause: A stable chatId parameter is not passed with each call. This prevents the system from associating alert processing context and interrupts the filtering workflow. - Symptom: Custom Python plugin executes without returning results, and logs show field parsing failures. Cause: Parsing rules adapted to tunnel alert messages are not configured. Core fields such as device ID and alert threshold cannot be identified.
- Symptom: The number of alert filtering results returned after tool calling does not match expectations. Cause: A reasonable per-call alert batch limit is not set. This causes some batches of alert data to remain unprocessed.
How to Confirm Proper Configuration
- Initiate a tool calling request for a single tunnel alert, and verify whether the returned results include filtered valid alert entries.
- View the FastGPT application’s call logs, and confirm that the chatId parameter for each request is correctly recorded without loss.
- Trigger a simulated batch alert reporting request, and verify that the tool calling concurrency matches the configured upper limit.
- Check the custom Python plugin’s running logs, and confirm that tunnel alert fields are correctly parsed without format errors.
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.