What the data for this category looks like
The data used for quality traceability and defect analysis in welded structural component manufacturing covers documents including welding procedure qualifications, welding procedure specifications, welding consumable issue records, welder qualification records, weld inspection reports, and repair orders. Data sources include design drawings, material quality certificates, welding equipment parameters, consumable warehouse management systems, non-destructive testing systems, and personnel qualification ledgers. The data update frequency is per weld seam, per batch, or per shift. Each record contains fields such as weld number, welding parameters, inspection results, and welder qualifications. Field units are adjusted based on the specific document type.
What constraints these characteristics impose on the "tool calling and plugins" workflow
The multi-document association feature of category data requires tool calling to support cross-data source stitching, such as associating consumable issue records with weld inspection reports. The high data update frequency requires plugins to support real-time or near-real-time data retrieval, avoiding the use of expired welder qualification or consumable information. Rigid qualification and process constraints require tool calling to strictly match preset compliance rules, with no flexible adjustment of filter conditions. The unstructured nature of non-destructive testing data requires plugins to have the ability to parse specific format inspection reports and extract defect information such as cracks and incomplete fusion.
How to set the configurations
| Config Item | Suggested Value | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Automatically select tools suited for welded structural component business, no need to manually specify the calling type |
| Workflow/API function-call configuration | enabled | Enable function calling capabilities for retrieving dedicated business plugins such as welding ledgers and inspection report parsing |
| Tool call timeout | 300-600 seconds | Adapt to the time required for parsing multiple welding documents and cross-data source queries |
| Maximum number of tool calls per single request | First 8 entries | Match the volume of associated data for a single batch of welds or a single repair order, avoiding request overload |
| Plugin data source filter rules | Only match documents related to welding processes, non-destructive testing, and qualification ledgers | Limit the scope of plugin calls to avoid irrelevant data interfering with analysis results |
| Context window length | 8000-12000 token | Carry complete context for multiple welding documents, adapting to the context management rules of FastGPT v4.17.0 |
Three common mistakes
- A request error occurs when calling marker-pdf, with a connection failure prompt. The cause is that when deployed in Docker Desktop, the plugin container port is not mapped to the host machine, causing FastGPT to be unable to access the locally deployed marker-pdf service.
- A gpt-4o-mini call error log appears during workflow runtime. The cause is that the API key or region parameter of the corresponding model was not correctly filled in the FastGPT platform configuration area, causing the call link to be interrupted.
- The knowledge base question-answer pair extraction task gets stuck during training, with no call logs generated. The cause is that the corresponding data parsing plugin was not configured, making it impossible to extract the field information of question-answer pairs, leading to task stagnation.
How to confirm the configuration is correct
- Initiate a simulated weld defect analysis query, check whether there are retrieval records for the corresponding welding data sources in the tool call logs.
- Upload a weld inspection report to the marker-pdf plugin, check whether the parsing result includes core fields such as weld number and non-destructive testing results.
- Initiate a query containing multiple welding documents, confirm that no timeout error occurs and the tool call process completes normally.
- Check the FastGPT platform configuration logs, confirm that the
toolChoiceandfunctionCallparameters are correctly loaded.
The values provided are common starting points and should be measured against the reader's own samples.