What the data for this category looks like
This category's data originates from material grades, customer technical specifications, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and workshop MES systems. Data update frequency is per furnace run or per batch. Daily documents include heat treatment process cards, charging diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling sheets. Core fields include furnace number, batch number, heating temperature, holding time, cooling rate, hardness value, and metallographic structure grade. Units include degrees Celsius, hours, HV Vickers hardness grades, and similar units.
What constraints these characteristics impose on the "tool calling and plugins" link
Multi-source heterogeneous data types (text-based process cards, numerical furnace temperature curves, image-based charging diagrams) require tool calling to support data type routing, with separate processing logic for different formats. High-frequency per-furnace updates require plugins to support real-time pulling of MES data to avoid data lag. The presence of professional fields and units requires tools to have professional parameter parsing capabilities; general text parsing tools cannot be used directly. Abnormal data may lead to failure of the entire furnace's performance, so plugins must include a parameter verification link to intercept unqualified batches in advance.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Auto mode, enable multi-type data routing | Adapt to mixed heat treatment production data including text, numerical values, and images, automatically matching corresponding processing tools |
| Workflow/API function-call configuration | Set priority to 0.8–0.9, timeout threshold to 15–20 seconds | Balance the accuracy and real-time performance of tool calling, matching the response requirements of furnace temperature data collection |
| Plugin trigger timing | Triggered upon submission of single furnace run data | Match the update frequency per furnace run, avoiding repeated calls or data omissions |
| Professional parameter parsing plugin | Bind hardness value and furnace temperature curve fields | Parse heat treatment professional terminology and units, ensuring the accuracy of ledger fields |
| External system API permissions | Only open to specified interfaces of the workshop MES system | Ensure access security of production data, complying with workshop data management specifications |
| FastGPT v4.17.0 | Enable the tool calling native capability of this version | Achieve more stable multi-type data routing and plugin execution effects |
Three common mistakes
- Symptom: The ledger fields returned after
functionCallare empty. Cause: No parsing rules for professional parameters have been configured, so core fields such as heating temperature and holding time cannot be extracted from process card text. - Symptom: API calls return a 403 status code, and MES data cannot be pulled. Cause: No call permissions for external systems have been configured, causing the plugin to be unable to access workshop production data sources.
- Symptom: Multimodal data is not routed correctly, and image charging diagrams are assigned to plain text processing tools. Cause: The automatic data type recognition function of
toolChoicehas not been enabled, and trigger logic has not been split by document type.
How to confirm the configuration is complete
- Initiate a test call simulating single furnace run heat treatment data, check whether
toolChoiceautomatically routes to the corresponding data processing tool. - View the FastGPT plugin execution log, confirm that the parameters returned by
functionCallinclude core fields such as furnace number, heating temperature, and hardness value. - Call the published API interface to verify that the external system can normally obtain the summarized production ledger data.
- Upload test data containing abnormal parameters, check whether the abnormal verification plugin triggers the preset alarm logic.
The values provided here are common starting points and should be measured against the reader's own samples.