What the data for this category looks like
Data for this category comes from steel coil labels and quality certificates, press and feeder operation data, mold management systems, MES systems, coordinate measuring machine or fixture inspection data. Daily generated documents include stamping work instructions, material utilization rate tables, mold inspection logs, first/last piece records, dimensional inspection reports, and mold change records. Fields cover material grades, sheet thickness, mold numbers, processing parameters, inspection dimensions, mold change times, and more. Updates trigger per coil of material, per production batch, or each time a mold is changed.
What constraints these characteristics impose on model integration and configuration
Data updates occur at high frequency and with frequent triggers. This requires model integration to adapt to high-frequency, small-batch document parsing, to avoid resource waste and timeout issues. Multi-source heterogeneous data formats and field differences require clear document structure mapping rules for different data sources during configuration, to ensure accuracy of cross-document association. The volume of documents corresponding to a single mold change or coil of material is limited but highly correlated. This requires limiting context length to avoid interference from redundant information, while ensuring coverage of all associated production records. The strong binding between molds and production parameters requires the model to accurately match identical mold numbers and material batch information across different documents. Parameter rules adapted to association logic must be configured.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Enter the locally deployed AIProxy service address, for example http://192.168.1.100:8080/v1 | Adapt to local large model integration requirements, comply with the proxy calling specifications of FastGPT v4.17.0 |
AIPROXY_API_TOKEN | Enter the access token generated by the local AIProxy service | Complete identity verification, ensuring the security of the access link |
maxContext | 8000–12000 token | Adapt to the total length of associated documents for a single production batch, avoiding parsing failures caused by context overflow |
maxTokens | 2000–3000 token | Limit the output length of a single review round, adapting to the result output requirements of cross-review of stamping technical documents |
temperature | 0.1–0.3 | Reduce output randomness, ensuring consistency and accuracy of technical document review results |
| Workflow/API tool selection | none | No need to call external tools, focus on cross-review and matching of existing documents |
Three Common Misconfigurations
- A
cannot read propertieserror occurs during model testing. The cause is incorrect configuration ofAIPROXY_API_TOKENor an incorrectly formatted endpoint address, preventing the proxy service from forwarding requests normally. - An exception occurs when selecting model adaptation items. The cause is failure to account for the table and numeric field characteristics of stamping documents, using generic adaptation rules incorrectly, and not matching the text features of the metal forming domain.
- An excessively large value is set for
maxContext, leading to model inference timeout. The cause is failure to consider the actual total length of documents for a single production batch, exceeding the context capacity limit of the local large model.
How to Confirm Successful Configuration
- Navigate to the model management page in FastGPT, click the test button for the configured local large model, and check whether a normal text parsing result is returned, with no connection timeout or permission errors.
- Upload a stamping work instruction and the corresponding batch material utilization rate table, trigger a cross-review task, and check whether identical material grades and production batch information can be associated and matched.
- View the model call log to confirm that the actual call values of parameters such as
maxContextandmaxTokensmatch the configured values, with no abnormal truncation or overflow. - Verify the validity of
AIPROXY_API_TOKENby calling the corresponding proxy interface via the terminal, confirming that a standardized model response format is returned.
The values provided here are common starting points, and should be evaluated against the reader's own samples.