This page is written based on the configuration instructions for FastGPT v4.17.0.
What data for this category looks like
Data for this category comes from online composition analysis systems, DCS distributed control systems, laboratories, kiln and grinding equipment, raw material fuel ledgers, and energy metering devices. Daily generated documents include raw material batching plans, kiln operation records, laboratory reports, quality control notices, equipment inspection records, and energy reports. Data update cycles cover real-time, per-shift, and daily frequencies, covering business links across mining, raw material, calcination, and grinding departments.
What constraints do these characteristics impose on the tool calling and plugins workflow
Multiple scattered data sources and varied update cycles require tool calling to support multiple data acquisition methods such as real-time pulling and scheduled synchronization. Business documents covering multiple fields including composition, temperature, and inspection records require plugins to support parsing structured and unstructured content in different formats. The industry has clear consequences for errors, so tool calling must be configured with fault tolerance mechanisms and complete log retention. The business focuses on thermal stability and raw material homogenization, so tool calling logic must prioritize core needs of clinker calcination and grinding links.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapts to multi-tool calling scenarios for equipment inspection and maintenance Q&A, automatically matches corresponding business tools |
| Workflow/API function-call configuration | enabled | Force-enables the tool calling function to ensure normal triggering of actions such as parameter queries and maintenance step retrieval |
| Maximum single tool call count | Top 3 | Data in cement clinker and grinding links is concentrated; excessive calls increase context processing pressure |
| Log retention period | 7 days | Covers per-shift and daily business cycles, facilitating troubleshooting of tool call failures |
| Maximum context length | 8000–12000 token | Adapts to multi-document context requirements for clinker calcination links, avoiding truncation of critical thermal data |
| Question optimization | Enabled | Optimizes question semantics to match corresponding knowledge base documents, especially for batching adjustment issues caused by raw material and fuel fluctuations |
Three common configuration mistakes
- Symptom:
messages is emptyerror returned when calling openAPI. Cause: The request body is not properly populated with context documents related to equipment inspection, or the specific user inspection question content is not included. - Symptom: Tool call results do not match exclusive equipment data for cement clinker grinding. Cause: Question optimization configuration is not enabled, leading to unoptimized questions that fail to match knowledge base documents for this category, or
functionCallis not set to enabled. - Symptom: No valid log retention after tool call failure. Cause: Log retention period is configured too short to cover business troubleshooting cycles, or the log collection switch is not turned on.
How to verify the configuration is complete
- Navigate to the system configuration page, confirm that the settings for
toolChoiceandfunctionCallmatch the recommended values in the table. - Submit a simulated inspection query, verify that the tool call return content includes exclusive business information such as kiln and grinding equipment parameters and maintenance steps.
- Check the openAPI request body, confirm that the
messagesfield is populated with valid context and user query content, with no null values. - Review the system log configuration, confirm that the log retention period matches the recommended value in the table, and that historical call records can be retrieved normally.
The values provided are common starting points and should be measured against the reader's own samples.