What this category of data looks like
This category of data comes from business systems and production equipment of foundries and die casting workshops. It includes documents such as casting process cards, batching lists, heat records, mold history records, first article inspection records, and material test reports. Data sources cover ERP material master data, melting furnace thermometers, spectrometers, MES heat records, mold management systems, and customer technical drawings. Updates occur per heat, per batch, or daily. Fields include alloy batching ratios, melting temperatures, pouring parameters, mold cycle counts, defect types, and corresponding heat and mold cycle information.
What constraints these characteristics impose on model access and configuration
This category of data has high update frequency and is triggered by production batches. When accessing the model, ensure each call retrieves the latest heat, batching, and mold data. This avoids generating bid documents and quotations using expired parameters. Professional fields such as alloy composition and defect traceability information require accurate model identification. Therefore, configure appropriate context length and output token limits. Data formats vary across multiple sources, so clearly define parameter rules for tool calls. Also, support isolating call permissions for different bid projects or workshops using multiple API keys, to prevent configuration conflicts from overwriting existing settings.
How to set the configuration
| Configuration Item | Recommended Setting Range | Rationale |
|---|---|---|
maxTokens | 8000–12000 token | Matches the total length of professional foundry documents and single-heat data, to avoid truncating critical batching, temperature, and defect traceability information |
temperature | 0.2–0.4 | Reduces generation randomness, ensures consistency of quotations and process parameters, and meets the rigor requirements of bid documents |
| Workflow/API tool selection | auto | Automatically triggers tool calls to retrieve real-time heat records, mold history records, and other data, supporting accurate generation of quotations and documents |
AIPROXY_API_TOKEN | Configure independently per business scenario | Distinguishes call permissions for different bid projects or production workshops, preventing keys from different applications from overwriting each other |
AIPROXY_API_ENDPOINT | Specify per region or service provider | Matches the deployment region of data sources, reduces call latency, and ensures stability of real-time data acquisition |
Three common configuration mistakes
- Phenomenon: Frequent 429 Request rate increased too quickly error returns when calling the model. Cause: No reasonable rate limit set for real-time data calls in foundry production, or call requests not split by heat batches.
- Phenomenon: After deleting the body parameter when configuring the large model, the parameter still exists when re-entering the configuration page. Cause: Configuration changes not saved, or the platform has a default forced loading logic for body parameters, and full configuration submission not completed.
- Phenomenon: Creating a new model configuration overwrites the key setting of the original same API model. Cause: No independent
AIPROXY_API_TOKENparameter configured for different business scenarios, and the platform reuses key configurations with the same API identifier by default.
How to confirm the configuration is correct
- Based on FastGPT v4.17.0, initiate a simulated single-heat data call request, and check whether the returned result contains correct core fields such as alloy batching ratios and melting temperatures.
- Configure two independent business scenario keys, initiate calls separately, confirm that the key identifier associated with the returned result matches the configuration, and no overwriting occurs.
- View call logs, confirm that the
maxTokensparameter takes effect, and no critical content is truncated. - Test deleting the body parameter, save the configuration, re-enter the page to confirm that the parameter has been cleared, with no default residue.
- Simulate a model request failure scenario, check whether the node error capture process can be triggered correctly, and ensure that the exception handling logic operates normally.
The values provided here are common starting points, and should be evaluated against the reader's own samples.