What data for this category looks like
Data for this category comes from ERP material master data, melting furnaces and thermometers, spectrometers, MES furnace heat records, mold management systems, customer drawings and technical specifications. Daily documents include foundry process cards, batching lists, furnace heat records, mold histories, first article inspection records, material test reports, and nonconforming product review forms. Data updates occur per furnace heat, per batch, or daily, including fields such as alloy batching values, temperature parameters, defect types, furnace heat numbers, mold times, and more. There is no unified fixed format, and data is dynamically generated based on production nodes.
What constraints do these characteristics impose on the model access and configuration link
Data sources for this category are scattered, and update frequency is high. This requires the model access link to support rapid synchronization of multi-source heterogeneous data. Daily documents have no unified format, so configuration must adapt to unstructured content such as process cards and nonconforming product review forms. Production-related parameters include professional terminology and specific units. This requires the model to accurately identify and associate corresponding business logic. Frequent furnace heat-level updates also require context configuration to adapt to dynamically added business data, avoiding judgment errors caused by expired data.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of foundry process documents, covering full-process data for a single furnace heat |
maxTokens | 2000–3000 token | Meets the output length requirements for process parameter interpretation and defect traceability, avoiding truncation of key conclusions |
quoteMaxToken | 1500–2000 characters | Ensures complete recall of source data such as process cards and furnace heat records, supporting the accuracy of Q&A basis |
| Workflow/API tool selection | auto | Adapts to tool call requirements such as batching verification and parameter interpretation, automatically triggering corresponding functions |
vision | Enabled | Supports parsing of image materials such as customer drawings and metallographic photos, covering multimodal Q&A scenarios |
temperature | 0.1–0.3 | Reduces random output, ensuring consistency and accuracy of process parameter judgments |
Three common configuration errors
- Setting
toolChoiceto auto results in no tool call response. This occurs because the tool call adaptation rules for the model are not matched, or the model itself does not support structured output formats. - A 400 error is returned when connecting a custom API gateway. This is because the request parameter format does not meet docking requirements, or the API endpoint and token configuration do not match.
- The model list is not updated after modifying the docker-deployed configuration file. This is because the FastGPT container was not restarted, or the configuration file write path is incorrect.
How to confirm successful configuration
- Submit a test query involving alloy batching verification, check whether the model can recall the batching list data for the corresponding furnace heat and generate a conclusion.
- Upload a customer drawing or metallographic photo, verify whether the model can correctly parse the image content and associate it with process parameters.
- View the model call logs in the FastGPT backend, confirm that configuration parameters such as
maxContextandtoolChoiceare correctly passed. - Restart the FastGPT v4.17.0 deployment container, check whether the configured target model appears in the model list.
The values provided are common starting points and should be evaluated against the reader's own samples.