What data looks like for this category
Data for this category comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES heat records, mold management systems, and customer drawings and technical specifications. Daily documents include foundry process cards, batching lists, heat records, mold histories, first-piece inspection records, material reports, and nonconforming product review forms. Fields cover alloy batching ratios, melting temperatures, component contents, pouring parameters, internal defect types, and more. Update frequency is per heat, per batch, or daily.
What constraints these characteristics impose on model integration and configuration
This category of data updates frequently. The volume of documents processed in a single task changes dynamically with heat batches. Model context capacity must adapt to input sizes of different scales to avoid content truncation or overflow. Fields and units vary significantly across multi-source heterogeneous documents. Models must accurately recognize technical terms and standardized formats to ensure cross-review accuracy. Integration of locally deployed models and third-party models requires compatibility with different interface formats. Additional tags in model outputs must be handled to avoid interfering with review logic.
How to set configurations
| Configuration Item | Recommended Setting | Rationale for This Setting |
|---|---|---|
maxContext | 8000–12000 token | Adapt to batch import requirements of multi-heat documents, avoid context overflow |
maxTokens | 4000–6000 token | Cover complete review logic for foundry technical documents, prevent truncation of critical parameters |
temperature | 0.1–0.3 | Ensure consistency of professional review conclusions, reduce random deviations |
AIPROXY_API_ENDPOINT | Fill in the actual interface address of the deployed model | Complete access path configuration for custom models |
AIPROXY_API_TOKEN | Fill in the key provided by the model service provider | Complete identity verification for model access |
quoteMaxToken | 2000–3000 token | Retain sufficient original text reference fragments to support cross-verification basis |
Three common configuration errors
- In FastGPT v4.17.0, a 500 error occurs when importing a locally deployed BGE model. The cause is incorrect configuration of
AIPROXY_API_ENDPOINTorAIPROXY_API_TOKEN, leading to failed interface calls. - When calling the DeepSeek model, residual <think> tags in the output interfere with review result readability. The cause is failure to configure preprocessing rules for model outputs to filter extra tags.
- When calling a custom model, the prompt “Only allowed now” appears. The cause is incorrect setup of access parameters for the custom model, leading to platform restrictions on third-party model calls.
How to confirm configuration is complete
- Upload a single foundry process card, confirm no interface errors appear and valid review results are returned.
- Import batch heat record documents, confirm no context overflow prompts are triggered.
- Call the custom model, confirm successful interface calls with no permission or format errors.
- Verify matching accuracy for professional fields in model outputs, with no obvious truncation or format interference.
The values provided here are common starting points, and should be evaluated against relevant samples.