What this category of data looks like
This category of data primarily comes from concentrate and auxiliary material analysis reports, production DCS systems, electrolyzer voltage and current monitoring data, laboratory test results, and energy and environmental protection monitoring ledgers. Daily processed documents include batching lists, smelting records, electrolyzer condition records, batch test reports, anode and cathode quality records, and environmental protection operation ledgers. Data update frequency changes with production nodes. It updates in real time after each batch of raw materials, each furnace smelting, or each tank electrolysis operation. Some ledgers are summarized daily. Fields include material component proportions, tank operating parameters, test result values, environmental protection emission indicators, and other similar metrics, with clear unit identifiers.
Constraints for tool calling and plugins from this data's characteristics
Multi-source heterogeneous data sources require plugins to support integration with multiple data sources such as DCS systems, laboratory systems, and monitoring platforms. Pre-configured authentication and data pulling rules must be set for each distinct data source. High-frequency real-time production data requires tool calling to support low-latency responses, to avoid interrupting audit processes due to waiting timeouts. Diverse document types and complex field structures require plugins to have structured parsing capabilities. Plugins must automatically identify exclusive fields for different documents such as batching lists and tank condition records. For audits involving metal recovery rates and environmental protection indicators, tool calling must strictly match data units to prevent audit deviations caused by unit conversion errors. The toolChoice parameter can be adjusted to adapt to multi-tool calling scenarios.
How to configure
The following configurations are formulated based on the FastGPT v4.17.0 version logic.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to multi-tool calling scenarios, automatically match the tools required for inspection and process record audits |
| Workflow/API function-call configuration | Allow forced calling | For laboratory data and tank condition parameters that require precise field matching, force tool calling to complete structured extraction |
| Single tool call timeout | 30–60 seconds | Adapt to the pulling delay of real-time production data, avoid interrupting the audit process due to excessive waiting time |
| Maximum tool return results | Top 10 entries | Filter redundant data, focus on the latest smelting, electrolyzer condition, and test results |
| Context window size | 8000–12000 token | Accommodate multiple batches of production records and environmental protection ledgers, meet long-text audit requirements |
| Plugin authentication configuration | Configure by data source group | Differentiate authentication rules for different data sources such as DCS, laboratory, and environmental protection monitoring, ensure data access security |
Three common mistakes
- Symptom: Tool calling returns an error, with a status code of 401 or authentication failure. Cause: Authentication information is not configured by data source group, and authentication keys of different systems are mixed.
- Symptom: Historical tank condition records in the knowledge base are not called during audits. Cause: The knowledge base is not associated with the tool calling process, or the
toolChoiceparameter is configured to only call a single tool, which does not cover the knowledge base retrieval link. - Symptom: The display name of a custom plugin is inconsistent between the system plugin list and the plugin management page. Cause: The display name is not filled in the plugin configuration, and the system defaults to reading the function name in the code as the display name for the list page.
How to verify your configuration is correct
- Initiate a simulated batching list audit request, check whether the tool successfully pulls the corresponding batch of DCS data and test reports.
- View the plugin management page and the system plugin list to confirm that the display name of the custom plugin has been correctly configured.
- Trigger tool calling, check whether the number of returned results and field matching degree meet the audit requirements.
- Simulate a timeout scenario, confirm that tool calling completes the response within the set timeout period.
The values provided are common starting points and should be measured against the reader's own samples.