What data this use case uses
Data sources include ERP production management systems, MES manufacturing execution systems, VIN and vehicle configuration systems, tightening torque tools, EOL offline inspection lines, and quality traceability systems. Update frequency includes real-time assembly data per vehicle, shift-level summary offline reports, and batch-updated supplier part documents. Daily documentation includes production order numbers, VIN codes, assembly work instruction entries, tightening torque values, offline inspection item results, rework records, and consistency release documents. Fields include uniquely identified VIN codes, multi-dimensional assembly parameters, and batch numbers for traceability.
What constraints these characteristics impose on model integration and configuration
Each vehicle corresponds to hundreds of configuration parameters. The model must process large volumes of associated data. Context window length must match the scale of a single batch of final assembly documentation to avoid truncating critical information. Multi-source data comes from different systems with inconsistent formats and units. The model must stably recognize field meanings to prevent parameter drift. Real-time updated tightening data and offline inspection results require low-latency processing. Call parameters must adapt to the response speed requirements of production workstations. In mixed-line production scenarios, the model must accurately match VIN codes to corresponding configurations. Output consistency must be controlled to reduce mismatch risks.
How to set the configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Total data volume of assembly documentation, VIN configurations, tightening data and other related materials for a single vehicle is approximately 8000–12000 token. Full loading is required to avoid truncation of critical information |
temperature | 0.2–0.4 | Cross-review of technical documentation requires strict matching of parameters and requirements. A lower temperature ensures output consistency and reduces mismatch risks |
vision | Enabled | Assembly work instructions include images such as torque charts and workstation diagrams. Image content must be recognized to assist review |
AIPROXY_API_ENDPOINT | Fill in the dedicated access address provided by the service provider | Ensure correct model call routing, avoid cross-region latency or access blocking |
AIPROXY_API_TOKEN | Fill in the key bound to the service provider | Verify call permissions, prevent unauthorized access or incorrect quota judgments |
maxTokens | 2000–3000 token | The length of a single review result must be controlled to adapt to terminal display and response speed requirements of production workstations |
Three common configuration errors
- Symptom: The large language model call displays an overdue payment prompt, but the API account balance is sufficient. Cause:
AIPROXY_API_TOKENorAIPROXY_API_ENDPOINTis not configured correctly. The platform cannot correctly recognize the account quota, triggering a quota limit prompt. - Symptom: An error occurs when running the workflow. Recovery fails even after confirming the model configuration is correct. Cause: The
maxContextparameter is not adjusted for the final assembly scenario. Batch VIN data loading causes context overflow. Automatic truncation by the platform triggers a format error. - Symptom: The review result returned by the knowledge base does not include image content from the work instruction. Cause: The
visionconfiguration is not enabled, and the prompt does not explicitly require associating image references. The model ignores visual elements in the document.
How to confirm successful configuration
- Log in to the FastGPT v4.17.0 backend. Verify that
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENconfigurations match the parameters in the service provider’s backend. - Enter the VIN code of a single vehicle for testing. Confirm that the context length of the returned content aligns with the
maxContextsetting, with no obvious truncation. - Upload an assembly work instruction that includes images. Initiate a review request, and confirm that the returned content includes associated descriptions of the images.
- Configure multiple model channels and set priorities. Initiate a batch test, and confirm that the system prioritizes calling models from high-priority channels.
The values provided here are common starting points and should be evaluated against the reader's own samples.