Cross-review of technical documents for heat treatment: Model Access and Configuration

FastGPT v4.17.0

What the data for this category looks like

Core data for heat treatment scenarios comes from material grade libraries, customer technical specification documents, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers and metallographic testing equipment, and workshop MES systems. Data update frequency is per furnace run, per batch. A single furnace run includes documents such as heat treatment process cards, charging layout diagrams, furnace temperature curve records, hardness and metallographic test reports, equipment inspection forms, and exception handling documents. Document fields include parameters with clear units: heating temperature (℃), holding time (h), furnace temperature sampling time (s), hardness value (HV/HRC), etc. Some documents contain visual chart content.

What constraints these characteristics impose on the "model access and configuration" link

Single furnace run data is updated in real time and contains multi-source heterogeneous content. This requires model access configuration to adapt to short-cycle, multi-document context aggregation requirements, and avoid truncation of key process parameters due to context length limits. The presence of visual documents requires configuration to support image parsing capabilities, ensuring that non-text data such as charging diagrams and furnace temperature curves can be correctly identified. Fields with units require the model to accurately match parameter units, so configuration must balance the model’s recognition accuracy for professional units. The need for multi-source data association verification requires configuration to clearly define the priority of recall and inference, to avoid data confusion.

How to set the configuration

Three common mistakes

• Phenomenon: After configuration, the model only recalls knowledge base content, and directly refuses to answer when there is no matching content. Cause: Accidentally set toolChoice to knowledge base recall only mode, and did not enable generalized inference permissions, corresponding to the community-reported issue where the model directly refuses to answer when the knowledge base has no content.
• Phenomenon: The model returns an interface error when called, unable to normally initiate inference or failed to call tools. Cause: Failed to correctly configure AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN, or failed to verify the input format supported by the model, corresponding to the channel configuration error issue mentioned in the community.
• Phenomenon: Content truncation occurs after single furnace run documents are uploaded, and key process parameters are lost. Cause: Did not adjust the maxContext value according to the total length of single furnace run documents, resulting in forced context truncation.

How to confirm the configuration is complete

• Upload the complete document set for a single furnace run, submit a query containing process parameters and test data, and verify that the model can recall all relevant content without truncation or omission.
• Submit a query containing charging diagrams and furnace temperature curves, and verify that the model can correctly parse visual content and associate it with text data.
• Call the specified model channel, submit a test request, and confirm that the interface returns normally without errors.
• Adjust the temperature parameter to a reasonable range, submit a process query containing units, and verify that the output parameter units match the source documents.

The values provided are common starting points and should be measured against the reader's own samples.