What Data Looks Like for This Category
Daily documents for this category include forging process specifications, cutting lists, furnace heating records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data sources cover raw material quality certificates, furnace heating systems, press collection data, MES, non-destructive testing instruments, and customer forging drawings. Data updates occur per piece, per batch, or per furnace run. Document fields include forging ratio, fiber direction, furnace batch number, forging weight, detected defect type, and others. Corresponding units include tons, degrees Celsius, millimeters, and additional standard industrial units.
Constraints on Model Access and Configuration
Multi-source heterogeneous data sources create differences in document formats and field naming. This requires model access configurations to support parsing multiple document types, including ultrasonic testing reports with images. The high-frequency update business rhythm requires configurations to adapt to the limited length of single-batch documents, avoiding context overflow. The corresponding relationship between furnace batch numbers and forging batches must be strictly verified, which demands high accuracy in model output. Multi-modal input support is also needed to process image-based testing reports. Differences in fields across documents require configurations to accurately match the core information extraction dimensions required for the business. This document is written for FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled | This category requires review of documents with images such as ultrasonic testing reports, so visual input capability must be supported |
maxContext | 8000–12000 token | Adapt to the total length of single-batch forging-related documents, avoiding exceeding model context limits |
maxTokens | 2000–3000 token | Control the length of single-round review output, ensuring all core review points are covered without redundancy |
quoteMaxToken | 1500 token | Only reference core fragments from documents, reducing interference from irrelevant content on model review |
temperature | 0.1–0.3 | Reduce the probability of model hallucinations, ensuring result accuracy for business scenarios such as batch verification and defect identification |
AIPROXY_API_ENDPOINT | Fill in according to the deployed model service address | Adapt to various model services deployed locally or in the cloud |
Three Common Configuration Errors
- Phenomenon: After accessing a visual model, ultrasonic testing report images cannot be parsed, and the interface prompts that visual input is not supported. Cause: The
visionconfiguration item was not enabled correctly, or a model with visual capability was not selected. - Phenomenon: Single-batch review tasks time out, returning a 504 status code. Cause: The
maxContextsetting exceeds the response threshold of the model service, and parameters were not adjusted based on the high-frequency update document length of this category. - Phenomenon: Unable to accurately count token consumption for single-batch reviews, with results deviating significantly from actual call volume. Cause: The
quoteMaxTokenlimit for reference fragment length was not set, leading to deviations in the model's token calculation logic.
How to Verify a Successful Configuration
- Upload a test document package containing ultrasonic testing report images, trigger the review process, and confirm correct extraction of defect annotation information from images, verifying that the
visionconfiguration is effective. - Review model call logs, confirm that actual token usage falls within the range set by
maxContextandmaxTokens, verifying that context and output length limits are active. - Manually input test data where furnace batch numbers do not match forging batches, confirm that review results accurately identify mismatches, verifying the constraint effect of the
temperatureparameter. - Test connectivity to the locally deployed model service, confirm that the address configured in
AIPROXY_API_ENDPOINTis accessible without authentication errors.
The values provided are common starting points and should be evaluated against relevant samples.