What the data for this category looks like
Data for refractory materials intelligent due diligence reports primarily comes from industry association public quality inspection bulletins, factory quality inspection documents from production enterprises, and kiln operation condition monitoring data. Update frequencies vary by source:
- Factory quality inspection documents update in real time with each production batch
- Industry quarterly bulletins update once per quarter
- Condition monitoring data is pushed minute-by-minute
The document structure includes fields such as batch number, core chemical components (Al₂O₃, SiO₂, etc.), bulk density, compressive strength, maximum service temperature, manufacturer, and inspection date. All units follow international standard conventions: bulk density uses g/cm³, compressive strength uses MPa, and service temperature uses ℃.
What constraints these characteristics impose on the tool calling and plugins workflow
Heterogeneous data from multiple sources requires tool calling nodes to support multiple data source adapters. Configure database plugins to connect structured quality inspection data from production enterprises, and HTTP toolkits to connect industry public bulletins.
Minute-by-minute updated condition data requires reasonable timeout settings for tool calls, to avoid workflow interruptions caused by data pull delays.
Batch number as the unique identifier field requires precise matching of field names during tool calls, to avoid data loss caused by field mapping errors.
Chemical and physical parameters with multiple coexisting units requires built-in unit conversion logic in plugins, to avoid generating incorrect due diligence data due to unit mismatches.
How to configure the settings
This configuration applies to FastGPT v4.9.3 and above.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
tool_call_timeout | 300–600 seconds | The average time to pull multi-source data for refractory materials ranges from 2 to 10 minutes. This interval covers pull delays for most scenarios |
http_tool_request_method | Configure as needed with GET/POST | Most industry bulletin data sources use GET requests, while internal enterprise database interfaces mostly use POST requests |
tool_call_output_skip | Enable as needed | Enable when only tool call results need to be output and AI replies hidden, to meet the output requirements of some due diligence reports |
model_tool_choice | auto | Adapts to the tool calling logic of Qwen series models, automatically identifying tools that need to be called |
plugin_field_mapping_mode | Precise field name matching | Field names such as batch number and chemical components for refractory materials are fixed. Strict matching avoids data mapping errors |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Phenomenon: The tool calling node outputs AI natural language replies after execution, and the configuration to return only tool results does not take effect. Cause: The
tool_call_output_skipconfiguration item is not enabled, and AI reply output is retained by default. - Phenomenon: Unable to pull quality inspection data after configuring the Oracle database plugin, with the log returning the
ORA-00904: invalid column nameerror. Cause: Non-standard field names are used during field mapping, and the actual fields on refractory material quality inspection documents are not matched. - Phenomenon: Tool calling fails when calling the Qwen Next model, returning a
400 Bad Requeststatus code. Cause: Themodel_tool_choiceparameter is not configured correctly, or the schema format for tool calling is not declared in the request.
How to confirm the configuration is complete
- Perform a single-batch refractory material data pull test, and check if the fields returned by the tool call are completely consistent with the preset quality inspection document fields.
- After setting
tool_call_output_skipto enabled, run the test again, and confirm that the node returns only structured data with no AI natural language replies. - Connect to the Qwen Next model, send a test request, and check if the tool calling schema is correctly recognized by the model.
- View the tool calling log, confirm that no timeout errors are triggered, and that data pull time matches the configured expectations.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.