What the data for this category looks like
Data for this category comes from manufacturing enterprise MES systems, online inspection equipment, laboratory records, process parameter libraries, customer standard documents, and historical defect databases. Daily produced documents include control plans, patrol inspection records, SPC reports, finished product inspection reports, non-conforming product review forms, release forms, and quality monthly reports. Data is collected continuously alongside production batches and shifts. Each data entry includes fields such as inspection items, defect types, release thresholds, batch identifiers, and inspection values. Units include pieces, ppm, hours, and others.
What constraints these characteristics impose on model access and configuration
Data sources for this category are scattered and updated frequently. This requires the model access link to support batch synchronization and incremental updates of multi-source data. Different types of quality documents have large structural differences. The configuration link must adapt to content parsing for multiple formats including control plans and inspection reports. Data contains specific professional fields and units. Configuration must ensure the model can accurately identify professional content such as inspection items and release thresholds, and avoid unit confusion or judgment deviations. Errors in process quality and finished product release directly link to production line downtime and customer risks. Therefore, the stability of model calls and result confidence must be included in configuration constraints.
How to configure the settings
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to context integration of multiple quality documents and inspection records, avoids truncation of critical standards and inspection data |
maxTokens | 4000 token | Controls the output length of single-round Q&A, ensures complete and focused results for process procedure interpretation and abnormality judgment |
temperature | 0.1–0.3 | Reduces random output, ensures consistency of professional judgments, and meets the rigor requirements of quality control |
AIPROXY_API_ENDPOINT | Fill in official or custom proxy addresses according to deployment environment | Adapts to multi-model access requirements, resolves cross-region call latency issues |
| Workflow/API tool selection | auto | Allows the model to independently choose whether to call tools, adapts to scenarios of extracting inspection items from standard documents and associating historical defects |
quoteMaxToken | 1500 token | Limits the length of referenced documents, ensures context accurately matches the quality judgment needs of the current Q&A |
Three common configuration errors
- The interface displays "No available channel for model gpt-4o-mini under current group default". The cause is that the access key or proxy address for this model has not been configured in the corresponding group, or the channel configuration has not passed connectivity testing.
- An authentication failure error is returned during calls. The cause is that
AIPROXY_API_TOKENhas not been configured correctly, or the key does not match the access address, resulting in failure to complete model calls. - The inspection items output by the model do not match the actual content of the document. The cause is that the value of
maxContextis too small, truncating critical process standard text, which prevents the model from fully matching judgment rules.
How to confirm successful configuration
This verification process is based on FastGPT v4.17.0.
- The model management page is accessed, and the configuration values of
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare verified against actual access details. - A single-round test Q&A is initiated with the input "Please extract the first-piece inspection items in the control plan", and the model output is confirmed to contain professional inspection item content from the document.
- A multi-document context test is triggered, patrol inspection records and finished product inspection reports from the same batch are uploaded, and the model is confirmed to associate the two data types to complete abnormality judgment.
- A test tool is used to verify model response time, and results are confirmed to be returned within the time range allowed by production rhythm.
The values provided are common starting points and should be evaluated against appropriate samples.