What the data for this category looks like
Production data for SMT mounting and electronic assembly comes from sources including MES systems, mounting machines, SPI/AOI inspection equipment, reflow oven temperature recorders, and more. Daily documents include BOM and coordinate files, line change lists, stencil and furnace temperature records, first article confirmation sheets, SPI/AOI inspection reports, maintenance records, traceability labels, and others. Fields include part numbers, reference designators, component lot numbers, furnace temperature parameters, inspection defect items, and more. Units include millimeters, degrees Celsius, lot numbers, and others. Data updates occur in real time per work order and each line change; some inspection data updates immediately upon completion of inspection.
Constraints on model access and configuration
Data sources for this category are scattered and updated in real time. This requires model access to support parsing of multi-format documents, including structured tables and image-based inspection reports. High-frequency updates across multiple work orders will rapidly expand context scale. The context length passed to the model must be strictly controlled to avoid exceeding the model's maximum supported capacity.
Ledger data accuracy is directly tied to production error prevention and traceability. Fields generated by the model must exactly match preset business fields such as part numbers and reference designators. No missing fields or incorrect mapping are allowed.
Differences in parameter adaptation between local and online large models also require targeted adjustments for the document scale of SMT scenarios.
Recommended configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context requirements for splicing multiple documents per work order, preventing call failures from context overflow |
maxTokens | 4000 token | Controls the generation length of single-round ledger reports, matching conventional output scales for production documents |
temperature | 0.1–0.3 | Reduces random generation probability, ensuring accuracy of key business fields such as part numbers and reference designators in ledgers |
vision | Enabled | Parses image data from SPI/AOI inspection reports, extracting visual information like solder joint defects and component offset |
AIPROXY_API_ENDPOINT | Fill in based on deployment environment | Adapts to access paths for local or online large models, ensuring interface calls are reachable |
AIPROXY_API_TOKEN | Fill in using the value assigned by the platform | Ensures secure identity verification for model interface calls |
Three common misconfigurations
- Symptom: Returns a
400 Bad Requesterror when calling a local large model. Cause: ThemaxContextparameter was not adjusted for the local large model, and the passed context length exceeded the model's supported maximum context threshold. - Symptom: SPI/AOI inspection defect data fields are empty in generated ledgers. Cause: The
visionparameter was not enabled, so image content from inspection reports cannot be parsed, leading to loss of key business information. - Symptom: No response for an extended period after interface calls, triggering a timeout. Cause:
AIPROXY_API_ENDPOINTwas configured incorrectly, pointing to an incorrect model service address, preventing the request from establishing a normal connection.
How to confirm successful configuration
- Upload a test SMT work order BOM and SPI inspection report, trigger the ledger generation process, and verify that core fields including part numbers, reference designators, and component lot numbers are correctly extracted.
- Check the FastGPT v4.17.0 interface call log to confirm
AIPROXY_API_TOKENverification passes, with no 401 permission errors. - Adjust the
temperatureparameter to 0.2 and 0.3, compare the two generated ledger contents, and confirm differences fall within acceptable business ranges. - Upload multiple different types of SPI/AOI image reports, verify that the model correctly identifies defect data in images and writes it to corresponding ledger fields.
The values provided here are common starting points and should be evaluated against the reader's own samples.