What Data for This Category Looks Like
The data sources for this category include raw material quality certificates, heating furnace systems, press collection data, MES systems, non-destructive testing instruments, and customer forging drawings. Teams perform data updates for each forging piece, production batch, or furnace heat. Daily generated documents include forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Each document has unique fields such as furnace batch number, forging batch, process parameters, and test results. There is no universal fixed statistical format.
Constraints Imposed on Model Access and Configuration
This category’s data is multi-source, heterogeneous, and updated per production batch. It requires the model access context to precisely match all associated documents for the corresponding batch, to avoid mixing cross-batch data. Differing document formats across multiple sources require the model to align input fields and unify formats. Otherwise, audit judgments may have deviations. The high-frequency production data update rhythm requires the model interface’s response delay to align with workshop production processes, preventing blocking of the inspection link. Inspection audits need to verify the correspondence between material furnace batches and forging batches. The model must have the ability to associate multiple document fields, to ensure the accuracy of audit basis.
How to Set Configurations
This configuration is based on the model access process for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Must cover the total content of all relevant documents for a single furnace heat, to match the full audit requirements of a forging batch |
maxTokens | 2000–3000 token | Adapts to the result output length of single-batch inspection audits, to avoid truncating key judgment content |
quoteMaxToken | 1500 token | Retains sufficient original document reference fragments to support traceability verification of audit conclusions |
temperature | 0.1–0.3 | Ensures consistent and rigorous judgment results for inspection audits, reducing random deviations |
AIPROXY_API_ENDPOINT | Intranet access address of the deployment node | Ensures data transmission security in the production environment, avoiding public network exposure of sensitive production data |
AIPROXY_API_TOKEN | Token bound with exclusive call permissions | Restricts only authorized forging workshop systems to call the model interface, preventing unauthorized access |
Three Common Configuration Mistakes
- Phenomenon:
connection refusedortoken encoder erroroccurs when calling the model. Cause:AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or network connectivity between the proxy node and the model service is abnormal. - Phenomenon: Key basis is missing from the model's output audit conclusion. Cause:
maxContextis set too small, failing to cover all associated documents for a single furnace heat, resulting in some necessary process or test data not being included in the context. - Phenomenon: Random differences appear in audit results for forgings of the same batch. Cause:
temperatureis set too high, causing unnecessary fluctuations in the model's judgment logic, which does not meet the rigorous requirements of inspection.
How to Confirm Configuration Is Correct
- Call the model interface, check that the returned response status code is 200. Confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations are valid. - Input a typical document set for a single furnace heat. Check that the context coverage range of the model output matches the
maxContextsetting, with no key document content truncated. - Input multiple sets of test data from the same batch. Compare the consistency of the model's output judgment results, confirm that the
temperaturevalue meets the requirements for rigorous audits. - Upload an ultrasonic testing report image with annotation information. Enable the
visionconfiguration, check that the model can correctly identify and parse the test content in the image.
The values provided here are common starting points, and should be evaluated against the user’s own samples.