What the Data for This Category Looks Like
The business data for metal material processing mainly comes from production work orders, quality inspection reports, process specifications, and equipment operation logs. Data sources cover the entire process from raw material intake to finished product delivery. Production work orders are generated in real time per processing batch. Quality inspection reports are updated upon completion of testing for each furnace or batch of materials. Process specifications are adjusted as production standards are updated. Individual documents typically include fields such as furnace number, batch number, processing procedure parameters, qualification determination results, and exception handling records. Units include commonly used industrial measurement identifiers such as ton, millimeter, megapascal, and hour.
What Constraints Do These Characteristics Impose on Model Access and Configuration
Multi-source data across the entire process requires model access configuration to adapt to frequently updated work order data, and avoid information lag caused by context refresh delays. Batched quality inspection and work order data require recall rules configured to match fixed fields, to ensure recall results are bound to corresponding furnace numbers and batch numbers. Long process specification documents contain a large number of industrial parameters, so single-round context length must be limited to prevent the model from losing key procedure information due to context overflow. Fields with multiple units require configuration logic that can accurately identify and associate units with parameters, to avoid confusion of cross-batch processing data.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the context carrying requirements of multi-field long documents such as process specifications and long work orders, and avoids losing key information such as batch numbers and procedure parameters due to context overflow |
maxTokens | 2000–3000 token | Matches the single reply length requirement of business process guidance, and ensures that content such as procedure steps and exception handling solutions can be fully output |
quoteMaxToken | 1500 token | Limits the length of recalled and cited document fragments, ensures the relevance of cited content to business process guidance, and avoids overly long fragments interfering with core guidance logic |
temperature | 0.3–0.5 | Balances the accuracy and flexibility of business guidance, ensures that procedure steps output by the model comply with industrial production standards, and reduces the generation of irrelevant content |
AIPROXY_API_ENDPOINT | Interface address corresponding to the locally deployed model | Adapts to locally deployed models, and ensures normal model call links |
AIPROXY_API_TOKEN | Authentication token generated during local deployment | Completes identity verification for model access, and prevents unauthorized calls |
Reference only; verify against the target FastGPT release and deployment documentation.
Three Common Misconfigurations
- After adding a new model configuration, existing model configurations with the same name are removed from the list. This occurs when only a generic model name is used as the configuration identifier, and fields such as furnace number and batch number for metal material processing are not used to distinguish configurations, resulting in the new configuration overwriting the old entry.
- Model output content includes text such as "Citation Marker: [1]". This occurs when the automatic output switch for knowledge base citation markers is not disabled, resulting in recalled document index identifiers being directly appended to generated content.
- A context length overrun error occurs during model calls. This occurs when the
maxContextparameter is not adjusted according to the characteristics of long documents, resulting in single-round context exceeding the maximum length supported by the model.
How to Verify Successful Configuration
- Enter the model configuration list, confirm that all added model configurations carry identifiers corresponding to metal material processing batches or furnace numbers, and no duplicate overwrites exist.
- Initiate a business process guidance test, check that no citation marker text appears in the reply content, and confirm that the knowledge base citation configuration has been adjusted correctly.
- Upload a long process specification document, trigger a model call, check that context loads normally, and no length overrun errors occur.
- Call the locally deployed model, confirm that the interface returns normal results, no authentication failure prompts appear, and verify that the authentication configuration is valid.
The values provided are common starting points and should be evaluated against samples specific to the target deployment context.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.