What this category's data looks like
Data sources include material grades, customer technical specifications, temperature control instruments, furnace temperature uniformity testing equipment, hardness testers, metallographic equipment, and manufacturing execution systems. Updates occur once per heat and per batch. Daily documents include heat treatment process cards, furnace loading diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling forms. Process cards contain structured fields such as heating temperature, holding time, and cooling method. Furnace temperature curves are line charts with time axes. Hardness reports mark mechanical performance units such as HV.
What constraints these characteristics impose on model integration and configuration
Multiple document types and high-frequency updates require model integration to adapt to visual content and real-time data synchronization requirements. Professional fields and fixed units require the model to have industry terminology recognition capabilities to prevent parameter parsing errors. Single-heat documents have moderate total length but include multiple sets of associated data. The total context volume must be controlled to fit model input limits. The timeliness of exception handling forms requires that recalled historical data accurately match current batch parameters. Irrelevant content must be prevented from interfering with audit results.
How to set configurations
| Configuration Item | Recommended Approach | Basis for This Setting |
|---|---|---|
vision | Enabled | Required to identify visual document content such as furnace loading diagrams and furnace temperature curves, to meet the image parsing needs of audit scenarios |
maxTokens | 8000–16000 token | Heat treatment-related documents contain multiple sets of furnace temperature, hardness, and metallographic data. Sufficient tokens are needed to carry complete context |
quoteMaxToken | 4000 characters | Single-heat associated documents have moderate total length. This value balances context recall volume and model input limits |
temperature | 0.1–0.3 | Supplier and process quality audits require accurate judgment of parameter compliance, and excessive divergence should be avoided |
AIPROXY_API_ENDPOINT | Fill in local or cloud endpoints according to the deployment environment | Adapts to the model integration specifications of FastGPT v4.17.0, and supports both local deployment and cloud call modes |
Three common configuration errors
- Phenomenon: Online recall test results are not displayed in rearranged order. Cause: Rearrangement configuration was not enabled during the knowledge base indexing step, and no corresponding rearrangement model was associated.
- Phenomenon: After the configuration reference upper limit is adjusted above 3000, context cannot be passed to the large model. Cause: The context carrying capacity upper limit of the FastGPT platform was exceeded, and relevant parameters were not adjusted according to actual document length.
- Phenomenon: Uploaded furnace temperature curve images cannot be correctly recognized. Cause: The
visionconfiguration was not enabled, or no model with visual capabilities was integrated.
How to confirm the configuration is correctly set
- Upload a single heat treatment process card and furnace temperature curve image, initiate a test query, and check whether text parameters and chart trends in the image can be correctly identified.
- Adjust the
maxTokensparameter to different ranges, and verify whether the input context length matches the expected configuration. - Launch multiple rounds of quality audit queries, and check whether the returned results contain correct professional fields and units.
- View the FastGPT v4.17.0 application logs to confirm that there are no errors in the model access endpoint and token configuration.
The values provided are common starting points and should be measured against the reader's own samples.