What the Data for This Category Looks Like
The core data for this category comes from BOMs, Gerber coordinate files, line change lists provided by customers, as well as MES systems, solder paste and component batch ledgers, placement machines, SPI/AOI testing equipment, reflow oven temperature records, and other sources. Daily generated documents include first article confirmation sheets, maintenance records, traceability labels, and more. Data is updated once per work order, with synchronous refreshes during line changes. Critical testing data such as oven temperature and placement defect data is uploaded in real time. Document fields include part numbers, reference designators, component batches, oven temperature curve parameters, defect types, and more. Fields are dynamically adjusted along with the work order process.
Constraints for Model Access and Configuration
The data sources for this category are scattered, and their update rhythms vary significantly. Real-time testing data and static work order documents require separate adaptation to model processing logic. Define the model call priority for different data sources clearly during model access. Single work order data volume is small, but there are many batches. Control the token limit for single processing to avoid exceeding the model’s processing limits. The field structures of different documents differ noticeably. Ensure the model can accurately identify core business fields such as part numbers and reference designators. Configure appropriate context recall parameters to meet this need. At the same time, real-time oven temperature data has high timeliness requirements. Adjust model call response parameters to match the data update rhythm.
Configuration Recommendations
This page is written based on FastGPT v4.17.0. The following are model access configuration recommendations for this category:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Matches the total token scale of multiple documents including BOMs, line change lists, SPI/AOI reports, etc., contained in a single work order, to avoid context overflow |
maxTokens | 2000–3000 token | Controls the output length of single-round inspection replies, and adapts to the text display requirements of process audit results |
quoteMaxToken | 1500 token | Limits the total token proportion of recalled reference documents, ensuring that core business fields are prioritized for model recognition |
| Workflow/API tool selection | auto | Allows the model to automatically select call logic based on inspection tasks, adapting to the cross-checking requirements of multi-source data |
temperature | 0.1–0.3 | Reduces the randomness of replies, ensuring consistency and rigor of inspection results |
AIPROXY_API_ENDPOINT | Fill in the access address of the corresponding model according to the deployment environment | Adapts to local or privately deployed model call scenarios |
AIPROXY_API_TOKEN | Fill in the authorization token of the corresponding model | Completes identity verification for model access |
Three Common Misconfigurations
- Symptom: The model’s generated inspection report includes irrelevant brand declaration text at the end. Cause: The default brand prompt output was not disabled in the system configuration.
- Symptom: Connection timeout errors occur when calling external models. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are not configured correctly, or network access is restricted. - Symptom: Duplicate defect records appear in inspection results. Cause:
quoteMaxTokenis set too large, recalling excessive redundant historical testing data, leading to model confusion.
How to Verify Correct Configuration
- Launch a simulated work order inspection task, check whether irrelevant brand declarations appear at the end of the reply, and confirm that the prompt configuration has taken effect.
- Call the configured model interface, verify that inspection results can be returned normally, and confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENparameters are configured correctly. - Submit a test work order containing multiple documents, check whether the model can accurately identify core fields such as part numbers and reference designators, and confirm that the context recall configuration meets expectations.
- Adjust the
temperatureparameter and launch a test, observe changes in reply consistency, and confirm that the parameter value meets business requirements.
The values given here are common starting points and should be measured against the reader's own samples.