This document applies to FastGPT v4.17.0.
What data in this category looks like
Data in this category comes from backup systems, database logs, object storage, and disaster recovery platforms. Regularly produced documents include backup policies, recovery drill reports, disaster recovery switchover plans, and data validation records. Data update frequencies are divided into three categories: daily, monthly, and quarterly, corresponding to the business rhythms of daily inspections, monthly drills, and quarterly full validation. Data fields include backup timestamp, recovery point objective, recovery time objective, data consistency check results, drill execution duration, and others. Units adjust based on specific metrics.
What constraints do these characteristics impose on the tool calling and plugins link
Data sources for this category are scattered. Tool calls must adapt to authentication and interface specifications of multiple data sources including backup systems, database logs, and object storage. Data update rhythms vary widely. Tools must support scheduled trigger configurations for daily, monthly, or quarterly cycles, or on-demand single validation tasks. Daily documents have diverse formats. Plugins must support parsing files with different structures such as text backup policies and tabular recovery drill reports. Core time-consuming steps require tools to set reasonable timeout thresholds to avoid long task interruptions. Automated data consistency check plugins must also be embedded to ensure recovery results meet business requirements.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
toolChoice | auto | Adapts to automatic tool selection for multiple data sources, covering different invocation scenarios such as backup systems and database logs |
| Timeout Threshold | 600–1200 seconds | Matches the execution duration of core time-consuming steps including recovery drills and data validation |
| Plugin Authentication Configuration | Set keys and interface addresses separately by data source type | Adapts to authentication rules for different data sources such as backup systems and object storage |
functionCall | Enable forced mode | Ensures tool calls strictly execute according to preset parameters, avoiding unintended content generation |
| Scheduled Trigger Cycle | Optional daily/monthly/quarterly | Matches different data update frequencies, adapting to needs of daily inspections and quarterly full validation |
| Number of Data Items Returned Per Call | Top 5–10 items | Prevents excessive data returned in a single call from causing timeouts |
Three common misconfigurations
- Phenomenon: The workflow executes tool calls normally in debug mode, but triggers an error when switched to official runtime mode. Cause: The official runtime mode does not have authentication keys for the corresponding data sources configured, or the scheduled trigger cycle does not match the data update rhythm, resulting in failure to obtain valid data source permissions during invocation.
- Phenomenon: Plugin calls return connection failures, with logs showing inability to access the target address. Cause: The intranet addresses of the disaster recovery platform and object storage have not been added to the plugin whitelist, or the container deployment network has not opened ports for the target services.
- Phenomenon: The data consistency check result is empty after tool calls, with no valid invocation records in the logs. Cause: The
functionCallforced mode is not enabled, causing the generated check logic to not correctly bind data source fields, making it impossible to extract valid check data.
How to confirm configurations are properly set
- View the workflow's tool call logs to confirm that each configured plugin has corresponding authentication requests and return records.
- Manually trigger a tool call, and verify that the returned results match the preset backup policies and validation rules.
- Check the scheduled task trigger configuration to confirm that the cycle aligns with the data update frequency.
- Simulate an abnormal scenario where the target data source is disconnected, and verify that the error prompt matches the preset configuration rules.
The values provided here are common starting points and should be evaluated 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 2026-10-19.