What Data for This Category Looks Like
Data sources for this category include steel coil labels and quality certificates, press and feeder operating data, mold management systems, workshop MES systems, coordinate measuring machine or fixture inspection data. Daily documents cover stamping work instructions, material utilization rate sheets, mold inspection sheets, first and last piece records, dimensional inspection reports, and mold change records. Data updates occur per coil of material, per production batch, or each time a mold is changed. Fields include material grade, thickness, press pressure, mold clearance, blank holder force parameters, dimensional deviation values, and more. There is no unified fixed format, and configurations are adjusted based on the actual management processes of each workshop.
Constraints on Model Access and Configuration
Data for this category updates frequently and comes from scattered sources. Model access and configuration must support high-frequency incremental synchronization and multi-source data field mapping. Daily document formats are not uniform. Configure parsing rules adapted to heterogeneous documents. Single audit tasks often link multiple cross-system documents, such as mold change records and dimensional inspection reports. Limit per-round context carrying capacity to avoid exceeding the model's processing limits. Some data is structured numerical values from directly connected equipment. Configure matching rules for structured data extraction to adapt to field differences across different device outputs.
How to Set Configurations
These configurations are developed based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Single audit tasks often link multiple cross-system workshop documents, requiring sufficient context carrying capacity |
maxTokens | 2000–3000 token | Inspection and process record audit outputs require detailed analysis, reserving sufficient generation space |
quoteMaxToken | 1500 token | Limit the total length of referenced documents to avoid context overload that impairs model processing |
temperature | 0.1–0.3 | Audit tasks require rigorous conclusions, reducing random generation probability to ensure result consistency |
| Workflow/API tool selection | auto | Call tools as needed to process structured equipment data, adapting to multi-source data verification requirements |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapt access paths for local or remote models to ensure normal calls |
Three Common Misconfigurations
- When input contains content from multiple cross-system stamping workshop documents, the model returns a fixed error format with no valid analysis content. Cause: The
maxContextparameter is not configured correctly, leading to insufficient context carrying capacity that triggers an exception. - Hybrid retrieval response time exceeds the acceptable range for standard audit scenarios. Cause:
quoteMaxTokenis not reasonably limited, leading to excessive documents loaded into the context that increase processing load. - After deploying a local model, the first call works normally but an error occurs when calling the model with other input content. Cause: The value range of
maxContextis not verified, exceeding the context limit supported by the model.
How to Confirm a Successful Configuration
- Submit a test request containing multiple stamping workshop documents, check that the context loads normally without truncation.
- Call the model to generate inspection audit conclusions, confirm that the output meets the rigor requirements of professional audits.
- Verify the connectivity of the model service address, confirm that there are no call failures or timeouts.
- Check the extraction results of structured equipment data, confirm that field mapping matches the workshop's data format.
The values provided are common starting points and should be measured against the reader's own samples.