What this category of data looks like
Internal office and employee services data for metal material processing mainly comes from production MES systems, equipment operation logs, quality inspection reports, and employee attendance and approval systems. Production process data is updated in real time or every hour. Equipment logs and quality inspection reports are updated when batches are completed. Employee attendance and office approval data is updated daily. Data documents include fields such as batch number, material grade, processing temperature, hardness value (HV), defect type, employee ID, job title, and approval node status. Most field units use industrial standard units such as degrees Celsius, megapascals, and hours.
What constraints do these characteristics impose on tool calling and plugins
The strong association between data and batches in metal material processing requires tool calls to accurately bind the batch number parameter, preventing invalid data from being pulled across batches. Real-time updated production process data requires tool call request response times to adapt to the delay requirements of industrial sites. Industrial-specific fields such as material grades and hardness values require plugins to support custom field mapping rules to adapt to non-standard business terminology. Employee service plugins must be associated with jobs and processing tasks, requiring tool calls to complete cross-system permission and data verification to prevent unauthorized access to production data.
How to configure settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
functionCall | Mandatory enabled | Adapt to structured business data calls for metal material processing, avoid interference from unstructured replies |
toolChoice | Fixed to specified plugin | Lock target plugins for fixed business scenarios such as batch query and quality inspection data pulling |
| Single call data volume | Top 3 entries | Avoid response timeouts caused by pulling too much industrial data in a single call |
| Request timeout period | 600 seconds | Adapt to delay conditions for industrial site data pulling, cover the complete query cycle for batch data |
| Custom field mapping | Match by material grade, batch number | Adapt to exclusive business fields for metal material processing, compatible with existing system data formats |
| Maximum context length | 800–1200 characters | Adapt to conversation scenarios with many industrial terms, avoid context overflow |
Three common configuration mistakes
- Phenomenon: Unintended inclusion of non-business knowledge base data during tool calls. Cause:
functionCallmode is not enabled, and general conversation logic automatically recalls irrelevant knowledge base content. - Phenomenon: API calls return a 514 status code. Cause: The
toolChoiceparameter is not configured to lock the target plugin, or the parameter format is incorrect, resulting in request blocking. - Phenomenon: Missing business fields in plugin call responses. Cause: Custom field mapping rules are not configured, making it impossible to match exclusive business fields such as material grades and batch numbers.
How to confirm configuration is complete
- Initiate a tool call request, verify that returned results only include specified business fields and no irrelevant knowledge base content, to confirm
functionCallmode is active. - Review API call logs, confirm the returned status code is 200 and the
toolChoiceparameter matches the target plugin, to confirm plugin locking configuration is active. - Cross-check plugin returned fields against custom mapping rules, confirm exclusive fields such as material grades and batch numbers are fully returned, to confirm field mapping configuration is active.
- Initiate multiple batch call requests, verify response times meet preset thresholds, to confirm timeout period configuration is reasonable.
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.