What the data for this category looks like
Data sources include real-time collection systems for smelting processes via PLC/DCS, quality inspection laboratory systems, and warehouse management systems. Data update frequency for individual processes ranges from every 10 seconds to every hour. The documents are structured ledgers containing fields such as furnace number, process number, collection timestamp, furnace temperature, molten iron composition, energy consumption value, and output volume. Units include degrees Celsius, milligrams per liter, tons, kilowatt-hours, and others. Each ledger entry typically ranges from 50 to 200 characters. Ledger fields vary slightly across different processes.
What constraints these characteristics impose on the tool calling and plugins workflow
High-frequency real-time collected data requires tool calling to match the data update rhythm, to avoid generating reports using expired ledgers. The multi-field and mixed-unit structure requires plugins to have built-in field mapping rules and unit conversion logic to ensure consistency of summarized data. The differentiated format of ledgers for individual processes requires plugins to support custom ledger matching templates, adapting to the ledger structures of different links such as steelmaking, ironmaking, and steel rolling. For batch processing of large-scale ledger data, the number of ledgers called in a single request must be limited to prevent timeouts or memory overflow.
How to set the configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
toolChoice | Set to auto | Adapt to multi-tool calling scenarios, automatically select tools matching ledger processing |
functionCall | Enable | Support structured calling of ledger data interfaces, ensuring the format of summary results complies with requirements |
| Tool call timeout | 300-600 seconds | Match the batch query response time of steel smelting ledgers, calibrated based on actual measurements |
| Single-call ledger limit | First 100 records | Limit the volume of data passed in a single call to avoid resource overload |
| Unit conversion switch | Enable | Adapt to mixed units of temperature, energy consumption, and output volume in ledgers, unify output format |
| Template matching mode | Match by process type | Adapt to ledger structures of different smelting links, improve summary accuracy |
Three common errors
- When calling a tool, the response returns
{"message": "Parameter format error"}, but the application configuration test runs normally. The cause is that the API key permission range in the production environment is not configured to access the corresponding ledger data source, which is inconsistent with the permission configuration in the test environment. - After upgrading the version, ledger parsing tools related to model calling cannot function normally. The cause is that the model mapping configuration relied on by the plugin was not updated synchronously, resulting in failure to match the correct interface during tool calling.
- In a Linux deployment environment, the speed of generating reports by calling a local large model is too slow. The cause is that the plugin is not configured with batch data sharding logic, and the volume of ledger data passed in a single call exceeds the processing limit of the local model.
How to confirm that the configuration is correct
- Manually trigger a tool call, check whether the returned ledger data fields match the mapping rules configured in the system, and whether units have been uniformly converted.
- Check the tool call logs to confirm that the timeout threshold is not triggered and the number of ledgers called in a single request meets the preset limit.
- Compare the return results of the application configuration test and the online API call to confirm that their output formats and data content are consistent.
- View the plugin's template matching logs to confirm that ledgers of different process types can be correctly matched to the corresponding summary templates.
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 2026-09-28.