What data for this category looks like
Data sources for this category include BOM and coordinate files provided by customers, Gerber files, and production data generated by MES systems, mount machines, SPI/AOI inspection equipment, and reflow oven temperature recorders. The system updates data per work order, each line change, and in real time. Daily documents include line change sheets, stencil records, first article confirmation reports, maintenance records, and traceability labels. Core fields include part number, reference designator, component batch number, furnace temperature parameters, and defect type. Coordinate data uses millimeters as its unit. Furnace temperature parameters use degrees Celsius as their unit.
Constraints on model access and configuration
Multi-source heterogeneous real-time data requires access configuration to adapt to different document and device data formats. This prevents format parsing errors. High-frequency updated work order data requires control of context length. This stops models from timing out when loading excessive redundant data. The need for precise matching of core fields requires limiting the scope of model-generated content in configuration. This avoids generalized analysis. Structured production data requires adaptation to the model's tool calling capabilities. This completes audit steps such as defect analysis and alternative material verification.
How to configure settings
The following configurations are written for FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single work order document data such as BOM and SPI reports is large. This range covers the complete context of multiple documents required for full audit, avoiding truncation of core fields. |
maxTokens | 4000–6000 token | The audit process generates defect analysis and alternative material verification results. Limiting output length ensures results are concise and complete. |
quoteMaxToken | 2000 token | Original document fragments such as BOM and furnace temperature records serve as audit basis. Controlling reference fragment length prevents context overflow. |
| Workflow/API tool selection | auto | The audit process calls tools such as document parsing and defect matching. Automatic tool selection adapts to multi-step audit workflows. |
temperature | 0.1–0.3 | The audit requires precise matching of core fields such as part number and reference designator. A lower temperature reduces randomness in generated content, improving matching accuracy. |
AIPROXY_API_ENDPOINT | Address of the TEI service deployed on an in-house computing node | Data for this category requires local processing to ensure real-time performance. Using an in-house computing node reduces interface call latency. |
Three common configuration mistakes
- Symptom: The model rearrangement result returns
false, and no valid matching content appears during normal retrieval. Cause: No appropriate context truncation threshold is configured for structured BOM data of the SMT category. Core fields such as part number and reference designator get truncated, so the rearrangement model cannot recognize matching features. - Symptom: After calling a tool, only XML/JSON code blocks are returned, and no visual charts are rendered. Cause: The
visionconfiguration item is not enabled, or no chart rendering trigger logic is configured in the tool call chain. Structured data cannot be converted to visual results. - Symptom: A
Cannot read properties of null (reading 'q')error occurs during multi-model collaborative calls. Cause: ThetoolChoiceparameter is not configured correctly during multi-model collaboration. Parameter transfer between models is interrupted, and a null value triggers a read error.
How to verify correct configuration
- Upload the BOM and SPI inspection report for a single work order. Test whether the model can accurately extract core fields such as part number and reference designator, and verify that extracted results match the original documents.
- Trigger a rearrangement test. Confirm that the returned
falsestatus only appears when there is no matching data, and valid matching items return during normal retrieval. - Call the multi-model collaboration function. Check whether structured analysis results and visual content generate normally, with no error messages.
- Test interface calls. Confirm that the
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are correct, with no access permission or address errors.
The values provided are common starting points and should be measured against the reader's own samples.