What this category of data looks like
Data for this category comes from multiple business carriers in stamping workshops such as automotive body panels and home appliance sheet metal. Daily documents include stamping work instructions, material utilization sheets, mold inspection logs, first and last piece records, dimensional inspection reports, and mold change records.
Data sources cover steel coil labels and quality certificates, punch and feeder operation data, mold management systems, MES systems, and coordinate measuring machine or fixture inspection data. Updates are triggered once per steel coil, per batch of parts, and per mold change. Document fields include steel grade, thickness, pressure value, wear amount, and dimensional deviation, with corresponding units such as millimeters, kilonewtons, and times.
Constraints imposed by these characteristics on the model access and configuration phase
Multi-source heterogeneous data types require clear field mapping rules during the access phase to avoid confusion between structured equipment data and extracted paper records. High-frequency business update rhythm requires limiting the model’s context window size to avoid loading redundant historical mold change and steel coil records, reducing processing latency. Diverse document types and professional parameters require configuring prompt templates adapted to different scenarios to ensure accuracy of process parameter and anomaly analysis. Severe consequences of errors require additional configuration of verification logic to avoid generating content that does not comply with workshop specifications.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the volume of single-batch stamping data, avoiding loading redundant historical mold change and steel coil records |
maxTokens | 2000–3000 token | Limits the output length of single-round exception reports or process documents, ensuring accuracy of process parameter and anomaly analysis |
temperature | 0.3–0.5 | Balances the rigor of process parameters and flexibility of anomaly analysis, avoiding generation of non-standard values that do not comply with workshop specifications |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools to process equipment data and dimensional inspection reports |
AIPROXY_API_TOKEN | Valid token generated by the proxy platform | Adapts to scenarios where domestic large models are accessed via third-party proxies, avoiding restrictions from direct calls to overseas interfaces |
AIPROXY_API_ENDPOINT | Enter the deployed proxy service address, such as https://your-proxy-domain/v1 | Adapts to deployment architectures where requests are forwarded via proxies, ensuring normal interface calls |
Three common mistakes
- Phenomenon: An error is reported when calling the model after configuring
AIPROXY_API_TOKEN, with an invalid token prompt. Cause: The bound model permissions are not correctly configured on the proxy platform, or extra spaces are included when filling in the token. - Phenomenon: After importing dimensional inspection reports from the stamping workshop, search tests return empty results. Cause:
maxContextis not configured to adapt to multi-source data formats, causing the system to fail to correctly match the associated fields of process documents and exception reports. - Phenomenon: The process parameter values in the generated exception report have excessive deviations. Cause: The
temperaturevalue is set too high, causing the model to generate non-standard parameters that do not comply with workshop specifications.
How to confirm that the configuration is complete
- Upload the quality certificate and material utilization sheet for a single steel coil, test whether the model can correctly extract steel grade, thickness, and material utilization values, and verify that the extracted fields correspond to those in workshop documents.
- Simulate an exception scenario after mold change, input equipment error logs and dimensional inspection data, test whether the model can generate exception reports that comply with workshop formats, and verify that the output contains process parameters consistent with actual data.
- View the model monitoring panel in FastGPT v4.17.0, confirm that the response time of interface calls is within a reasonable range, and there are no frequent invalid token errors.
- Switch between different stamping document types, such as mold inspection logs and first and last piece records, test whether the model can adapt to different field extraction rules, and ensure there is no output with format confusion.
The values provided here are common starting points and should be measured against the reader's own samples.