What this category of data looks like
This category of data covers daily work outputs from manufacturing enterprise IPQC, FQC, quality engineering and laboratory teams. It includes documents such as control plans, inspection records, SPC reports, finished product inspection reports, non-conforming product reviews, release orders, and quality monthly reports. Data sources include MES systems, production testing equipment, laboratory data, process parameter documents, customer standards, and historical defect databases. Updates are collected continuously per production batch and shift frequency. Fields include inspection items, test values, judgment results, defect types, release permission identifiers, and more. There are no unified fixed units. Values adjust dynamically based on testing projects and customer standards.
What constraints these characteristics impose on model access and configuration
Decentralized and frequently updated data sources require model access to support multi-source data synchronous pulling. This avoids inspection judgment deviations caused by data lag. Diverse document types and dynamically changing fields require configuration of context processing rules adapted to unstructured text. This ensures accurate information extraction for different types of quality inspection documents. High correlation between historical defect databases and real-time test data requires limiting single context length. This prevents redundant data from interfering with model inference. Fields related to release permissions involve production compliance. Strict permission verification rules for model calls must be configured. This avoids sensitive information leakage or unauthorized judgment.
How to set the configuration
This configuration is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the combined length of multiple quality inspection reports and SPC reports, preventing loss of key inspection data due to context overflow |
maxTokens | 2000–3000 token | Balances single-round inference speed and output completeness, meeting the requirement for detailed descriptions of non-conforming product review conclusions |
quoteMaxToken | 1500 token | Limits the length of referenced source data, ensuring the model only extracts inspection records and process parameter content directly related to current inspection judgments |
temperature | 0.1–0.3 | Reduces inference randomness, ensuring consistency of finished product release judgments and meeting the rigor requirements of quality control |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address based on the deployment environment | Adapts to the internal corporate network environment, ensuring data is only transmitted within authorized links and complies with production data security specifications |
AIPROXY_API_TOKEN | Generate a dedicated token based on the enterprise permission system | Restricts model call permissions, preventing unauthorized access to sensitive data such as release permissions and customer standards |
Three common misconfigurations
- A
401 Unauthorizederror is returned when calling the model. This occurs becauseAIPROXY_API_TOKENis not configured correctly, or the token permissions do not cover the scope of quality inspection data access. - The defect statistics output by the large model does not match the values in the SPC report. This occurs because the
maxContextvalue is too small, failing to fully reference the original data from multiple batches of inspection records, leading to missing key statistical items during inference. - The model cannot accurately identify referential expressions in non-conforming product reviews. This occurs because problem optimization configuration is not enabled, and referential resolution and question expansion are not completed before data retrieval, resulting in unclear context referential objects.
How to confirm the configuration is complete
- Launch a question-and-answer test targeting a single finished product inspection report, to verify that the model can accurately extract inspection items, test values, and judgment results.
- Call the model interface to view the returned context reference log, confirming that the
quoteMaxTokenrestriction takes effect, and only source data fragments of the specified length are returned. - Simulate a multi-source data synchronization scenario, checking whether the model can pull the latest shift inspection records and SPC report data on time.
- After verifying the
temperatureconfiguration, launch multiple identical non-conforming product review questions, confirming that the output results have no obvious random deviations.
The values provided are common starting points and should be measured against the reader's own samples.