What This Category of Data Looks Like
The ledger data for metal material processing comes from three primary sources: workshop industrial control collection systems, physical and chemical testing laboratory systems, and warehouse management systems. Data is generated and updated per production batch. Full data is synchronized once a single production batch is completed. A single batch ledger document uses a structured table format. It includes fields such as furnace number, batch number, rolling temperature, thickness tolerance, tensile strength, and yield strength. Units for these fields are ℃, mm, MPa, and others respectively. Single batch data volume varies based on processing complexity. Reference only; verify against the target FastGPT release and deployment documentation.
Constraints Imposed on Tool Calling and Plugins
The structured batch data of metal material processing creates several key constraints for tool calling and plugin workflows: Tool calling must accurately match unique identifier fields like furnace number and batch number to avoid mixing data across batches. The batch-based update rhythm requires plugin triggers to bind to production batch nodes. Tools pull data only after a batch is completed. Fields with dedicated units require tool calls to include built-in unit verification logic. This ensures testing data units match ledger field units. Multi-source data aggregation requires plugins to support data splicing across industrial control, testing, and warehouse systems.
Configuration Settings
The following table lists recommended configuration values and their supporting rationales:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Tool calling trigger condition | Bound to production batch completion node | Matches the characteristic that data updates per production batch |
| Single tool call data limit | Top 200 records | Adapts to typical single batch processing data volume |
toolChoice | auto | Supports automatic matching of multi-source data processing tools |
functionCall | Enable structured mode | Adapts to precise calling of ledger structured fields |
| Unit verification switch | Enabled | Matches the requirement that ledger fields have dedicated units |
| Plugin timeout threshold | 600 seconds | Adapts to data pull delays across industrial control and testing systems |
| Knowledge base association scope | Bound to current production batch ledger | Avoids interference from cross-batch data |
| Image parsing tool switch | Enabled | Supports access to jpg format metallographic test report images |
Three Common Configuration Mistakes
- Phenomenon: The knowledge base recalls cross-batch data during tool calling. This causes irrelevant content to appear in generated reports. Cause: The knowledge base association scope bound to the current production batch is not configured. The recall range is not limited to the single batch ledger.
- Phenomenon: Tool calling has no response after deploying the MCP server. A connection failure status code is returned. Cause: The listening port of the MCP server is not opened to the corresponding production system network segment. Processing data collected from the workshop cannot be pulled normally.
- Phenomenon: After uploading a jpg format metallographic test report, tool calling fails to parse the image content. An unsupported format error is returned. Cause: The image parsing tool switch is not enabled. The image parsing plugin for the corresponding format is not configured.
How to Verify Correct Configuration
- Trigger a complete workflow. Check the conversation token consumption log. Confirm compliance with FastGPT's token calculation rules. Ensure the statistical range of input and output tokens matches expectations.
- Trigger a simulated production batch completion node. Check if tool calling starts automatically. Confirm only current batch ledger data is pulled.
- Check the tool calling log. Confirm the automatic selection logic of the
toolChoiceparameter matches current data processing requirements. - Upload a simulated jpg format metallographic test report. Check if the plugin automatically parses the image content and extracts corresponding fields.
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.