What this category's data looks like
Data for this category originates from production and business systems of professional heat treatment facilities, and heat treatment workshops within automotive and bearing component manufacturing plants. Daily documents include heat treatment process cards, furnace loading diagrams, furnace temperature curves, hardness and metallographic reports, equipment inspection forms, and exception handling records. Fields covered include material grades, heating, holding, and cooling parameters, furnace loading positions, furnace temperature logs, test results, and more. Units include temperature (℃), time (hours), hardness (HV), and others. Data is updated per furnace charge and per batch, and directly ties to technical parameters and quotation bases used for customer bidding.
What constraints these characteristics impose on model access and configuration
Data is updated in batches per furnace charge, with most fields being technical parameters. This requires model access to support small-batch, high-frequency data calls. Scenarios involving mixed input of multiple document types, including text and image documents, require configuring parameters adapted to multimodal input. Data linked to customer technical requirements and bidding quotations requires model output to strictly match specified fields and units to avoid parameter deviations. Timeliness requirements for furnace charge data mean configuration items must support fast updates to the context window, while limiting the number of tokens processed per session to prevent exceeding model load limits. The rigor of bidding scenarios requires configuring a stable error retry mechanism to avoid errors in quotations or documents caused by abnormal data from a single furnace charge.
How to set the configuration
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total content length of multiple documents per furnace charge (process cards, furnace loading diagrams, furnace temperature curves, etc.) to avoid context overflow |
maxTokens | 2000–3000 token | Limits the output length of a single bidding document and quotation, matching internal enterprise document typesetting specifications |
quoteMaxToken | 1500 token | Limits the total number of tokens for heat treatment-related documents referenced by the knowledge base, ensuring referenced content focuses on process parameters and test results |
temperature | 0.1–0.3 | Reduces output randomness, ensures accuracy of bidding parameters, and avoids values that do not comply with process specifications |
vision | Enabled | Supports parsing image documents such as furnace loading diagrams and furnace temperature curves, extracting key parameters including temperature and furnace loading positions |
AIPROXY_API_ENDPOINT | Fill in according to the deployment environment | Matches the proxy address for FastGPT v4.17.0, ensuring normal operation of the model call link |
Three Common Misconfigurations
- Incorrect configuration of
AIPROXY_API_TOKEN, resulting in a 403 status code (no body) error. The cause is that the proxy token does not match the verification requirements of the model service provider, or the token is not correctly configured in the FastGPT channel settings. - The conversation model prompt does not adapt to space recognition, causing heat treatment process parameters (such as holding time) to be incorrectly split. The cause is that the prompt does not explicitly require retaining spaces in the text, or the model configuration does not support space recognition.
- Attempting to access a model version that does not appear in the channel list, making configuration impossible. The cause is that FastGPT channel configuration only supports included model types, and unsupported models are not added via custom interface methods.
How to Confirm Successful Configuration
- Test a call combining heat treatment documents for a single furnace charge, and verify that the model's returned output length complies with the configured
maxTokenslimit. - Submit a test case including image documents such as furnace loading diagrams and furnace temperature curves, confirm that the model can correctly extract parameter information from the images, and verify that the
visionconfiguration is enabled. - View the model call logs for FastGPT v4.17.0, confirm that the request address matches
AIPROXY_API_ENDPOINT, and check that the return status code is 200. - Adjust the
temperatureparameter and compare output results, confirm that output randomness changes as expected with parameter adjustments.
The values provided are common starting points and should be evaluated against the reader's own samples.