What this category of data 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. Daily documents cover forging process specifications, cutting lists, heating furnace records, forging process cards, mold life records, heat treatment entrustment slips, and ultrasonic testing reports. Data is updated per piece, per batch, or per furnace run. Fields include forging batch number, heating temperature (℃), forming pressure (kN), mold usage count, detected defect type, and other items. Some documents include structured parameters and unstructured testing images.
Constraints imposed on model access and configuration
Multi-source heterogeneous data sources and update rhythms based on furnace runs and batches require model access to support cross-system data association and matching. Daily documents include structured parameters and unstructured testing images, so parsing logic adapted to multi-format content must be configured. Fields include numeric data such as temperature and pressure, and enumeration items such as defect types. Clear field mapping rules must be established to ensure the accuracy of ledger summary. The volume of high-frequency updated single-batch data is limited, so the context scale for single processing must be restricted to avoid exceeding the model's carrying limit.
How to set configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
vision | Enabled, only bound to ultrasonic testing report images | Ultrasonic testing reports for this category include unstructured defect images, so visual capabilities must be enabled to parse relevant information |
maxContext | 8000–12000 token | The volume of ledger data associated with a single furnace run is moderate. This range covers associated documents for complete batches and avoids context overflow |
maxTokens | 2000–3000 token | The output length of a single production ledger summary report must be adapted to the content scale to prevent output truncation |
quoteMaxToken | 500–800 token | Restrict the length of individual document fragments referenced to ensure context cleanliness and data association accuracy |
temperature | 0.1–0.3 | Production ledger summary requires consistent data accuracy, so excessively high randomness parameters should not be used |
AIPROXY_API_ENDPOINT | Fill in the domestically compliant proxy interface address | Complies with data localization requirements and avoids compliance risks from cross-border data transmission |
AIPROXY_API_TOKEN | Fill in the authentication token for the corresponding proxy interface | Completes identity verification for model invocation |
Three Common Misconfigurations
- Issue: The model cannot parse images in ultrasonic testing reports, or the parsing result is empty. Cause: The
visionconfiguration item is not enabled, or the document type bound to the image is not specified, so the visual capability is not triggered. - Issue: Model invocation returns an authentication failure prompt, or the interface returns a 401/403 status code. Cause: The
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTconfiguration is incorrect, and the authentication information and interface address of the currently used model service are not matched. - Issue: Some fields are missing or unit labels are incorrect in the generated production ledger. Cause: The
maxContextconfiguration range is too small, so some associated process parameters and testing data are not included in the context, and the model cannot extract information completely.
How to confirm the configuration is successful
- Upload a forging process card that includes ultrasonic testing images, and check whether the model can recognize and extract defect descriptions from the images.
- Invoke the model to generate a ledger summary for a single furnace run, and verify whether the output content includes all configured field information.
- View the model invocation logs to confirm that the parameter values of configuration items such as
visionandAIPROXY_API_TOKENmatch the preset values. - Test the associated summary of multi-batch data to confirm that the model can correctly match the same-batch forging data from different sources.
The values provided are common starting points and should be measured against the reader's own samples.