What Data for This Category Looks Like
Data for this category originates from raw material quality certificates, heating furnace systems, press data collection systems, MES systems, non-destructive testing instruments, and customer forging drawings. Daily documents include forging process specifications, cutting cards, heating furnace records, forging process cards, mold life records, heat treatment work orders, and ultrasonic testing reports. Data is updated per piece, per batch, or per furnace run. Documents include professional fields such as forging ratio, fiber direction, furnace batch number, and forging dimensions. Units include millimeters, degrees Celsius, tons, and others.
What Constraints These Characteristics Impose on Model Access and Configuration
Data for this category is updated frequently per furnace run and batch, and sources span multiple industrial systems. This requires model access to support pulling associated data by batch to avoid mixing cross-batch data. Multi-source heterogeneous document structures and professional process fields require model access configuration to support understanding of metal forming terminology. The volume of single-furnace-run data updated frequently fluctuates greatly. This requires configuration items to support flexible adjustment of context length to adapt to the token scale of different batches of data. At the same time, exception reports need to quickly associate cross-document data such as raw material quality certificates and non-destructive testing results. This requires configuration to support parameters for multi-document context linkage.
How to Set Configurations
This configuration guide applies to FastGPT v4.17.0. The following are specific configuration recommendations for the forging and die forging scenario:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Fill in the dedicated industrial scenario access address provided by the corresponding large model service provider | Adapt to the data interface permission and format requirements of multiple systems in the forging workshop, and avoid compatibility issues with general-purpose interfaces |
AIPROXY_API_TOKEN | Fill in the dedicated authentication token assigned by the manufacturer | Ensure permission isolation for multi-source production data and prevent unauthorized access to core process data |
maxContext | 8000–12000 token | Adapt to the total token scale of single-furnace-run forging process documents and testing reports, and avoid context content being truncated |
maxTokens | 4000–6000 token | Meet the long-text output requirements when drafting exception reports, and cover complete process rectification and defect analysis content |
temperature | 0.2–0.4 | Control the rigor of process document generation, and avoid arbitrary changes to metal forming professional terms |
quoteMaxToken | 1000–1500 token | Limit the token count of a single referenced document, ensure the accuracy of context association, and reduce interference from irrelevant data |
Three Common Mistakes
- Phenomenon: A "request incompatible" error is returned when calling the large model, and process documents or exception reports cannot be generated. Cause: The model input format adapted for the forging scenario is not configured, and the field structure and professional term requirements of industrial documents are not aligned.
- Phenomenon: No relevant OneAPI configuration entry can be found after deploying with Docker Compose, and internal large model services cannot be accessed. Cause: Environment variable declarations for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not added to the deployment configuration, resulting in missing configuration items. - Phenomenon: The generated exception report text is truncated early, and only the first 4000 tokens of content are output. Cause: The
maxTokensparameter is not adjusted, the default 4000 token upper limit is retained, and the actual requirements of long-text drafting are not met.
How to Confirm Successful Configuration
- View the model access logs to confirm that multi-source industrial data can be pulled and associated by furnace run, and verify the correctness of data access.
- Submit a typical single-furnace-run forging process document, check that the text length of the model output meets expectations, and confirm that the
maxTokensconfiguration is effective. - Call the exception report drafting function, check whether the returned results include professional process fields such as forging ratio and fiber direction, and verify the domain adaptation of the model.
- Check the Docker Compose environment variable configuration file to confirm that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENhave been correctly declared, and verify that the authentication configuration is effective.
The values provided are common starting points and should be evaluated against samples from the actual deployment environment.