What this category’s data looks like
Data for this category originates from design drawings, material quality certificates, welding equipment parameters, filler metal inventories, non-destructive testing systems, and personnel qualification ledgers. Daily generated documents include welding procedure qualifications, welding procedure specifications, filler metal issue records, welder qualification records, weld inspection reports, repair orders, and similar items. Data updates follow the rhythm of each individual weld, each batch of materials, or each work shift. Different documents correspond to distinct update trigger conditions. Fields include base metal grade, filler metal model, welder qualification number, weld inspection results, and similar items. There are no unified fixed units; values shift based on specific business parameters.
What constraints these characteristics impose on model access and configuration
Data sources are scattered and highly correlated. Multiple system data sources must be accessed, and model access must support cross-data-source association matching. Update frequency is high and trigger conditions are diverse. Model call rhythms adapted to real-time or batch updates must be configured. Document fields change with welds and production batches. Model input must support dynamic field extraction to avoid issues with fixed template adaptation. Quality-related data requires rigid verification. Model configuration must bind logical constraints for qualification verification and process compliance to ensure outputs meet the quality requirements of welded structural components.
How to set the configuration
This configuration is based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Bid documents for welded structural components include multiple associated procedure qualifications, inspection reports, and similar content. Sufficient context is required to enable association matching |
maxTokens | 4000–6000 token | Outputs for bid documents and quotation preparation must cover complete content including process descriptions, quotation details, compliance statements, and similar items. This matches model output length limits |
temperature | 0.2–0.4 | Qualification verification and compliance checks require stable and accurate outputs to avoid process or qualification judgment errors caused by randomness |
AIPROXY_API_TOKEN | Configure using the exclusive key assigned by the platform | Ensures secure identity verification for model interface calls and prevents unauthorized access |
| Workflow/API tool selection | auto | Tools must be automatically called to complete filler metal matching, welder qualification verification, and similar steps to improve configuration adaptation efficiency |
Three common configuration errors
- Configuring
AIPROXY_API_TOKENresults in an invalid token prompt. The cause is failure to verify the key permission scope of the proxy platform, or the key has expired. - Unable to access the model configuration interface after local deployment. The cause is incorrect configuration of the front-end startup port, or failure to load the corresponding functional module.
- Call timeout occurs after adding an index model. The cause is that the
maxContextconfiguration exceeds the model's supported range, or the data source update frequency does not match the call rhythm.
How to confirm correct configuration
- Call the model interface, input a fragment of a single weld's welding procedure qualification document, and check whether the returned result includes correct qualification verification and process compliance judgment.
- Check the
AIPROXY_API_TOKENconfiguration item to confirm that the binding relationship between the key and the proxy platform is consistent, and there are no character input errors. - Adjust the
maxContextparameter, test whether context loading works normally across different values, with no truncation or loading failures. - Simulate batch submission of multiple associated documents to verify that the response time of model calls meets business expectations.
The values provided are common starting points and should be measured against the reader's own samples.