This document is written based on FastGPT v4.17.0.
What data looks like for this category
Data for this category typically comes from ERP/MES systems, VIN and vehicle configuration systems, tightening tools, End-of-Line (EOL) off-line inspection stations, and quality traceability systems. Daily documents include production orders, VIN lists, assembly work instructions, tightening torque data, off-line inspection reports, rework records, and consistency release documents. Data update frequency follows per individual vehicle: critical torque and inspection data are synced in real time, while batch and configuration information is updated per shift. Core data fields include VIN code, part batch number, torque parameters, inspection results, and rework station information.
What constraints these characteristics impose on model integration and configuration
This category uses mixed-line production with VIN as the single vehicle core identifier. The model must accurately match the hundreds of part configurations and process parameters corresponding to a single vehicle, so the context length must adapt to the scale of all associated data for one vehicle. Real-time synced tightening and inspection data require model call latency to align with production cycle times. Multi-source heterogeneous documents — including text-and-image work instructions and inspection photos — require the model to support visual input and multi-format parsing. Data updates are frequent, and error consequences are severe, so model integration must support a stable retry mechanism and permission control. It must also adapt to the needs of locally deployed large model calls to ensure production data is not leaked.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total context length of hundreds of configurations and associated documents for a single vehicle, avoiding truncation of critical VIN matching information |
maxTokens | 2000–3000 token | Meets the result output length required for single-batch defect analysis, covering complete interpretation of rework records and inspection data |
vision | Enabled | Adapts to the needs of text-and-image parsing for assembly work instructions and defect recognition for off-line inspection photos |
temperature | 0.2–0.4 | Reduces result randomness, ensuring consistency and rigor for quality traceability conclusions |
AIPROXY_API_ENDPOINT | Fill in the dedicated interface address for local deployment or cloud services | Adapts to data security requirements in production environments, avoiding leakage of vehicle production data via external interface calls |
AIPROXY_API_TOKEN | Configure a dedicated permission key | Implements access control to prohibit anonymous access, only allowing authorized production systems to call model services |
Three common misconfigurations
- Symptom: After enabling the
visionconfiguration, the model still only returns text results and cannot parse uploaded inspection photos or work instruction images. Cause: The enable switch for thevisionparameter was not configured correctly, or the bound large model does not support visual input capabilities. - Symptom: Connection timeout or 404 errors occur when calling a locally deployed large model. Cause:
AIPROXY_API_ENDPOINTwas filled in incorrectly, not pointing to the actual running port of the local model, or the correctAIPROXY_API_TOKENfor permission verification was not configured. - Symptom: When importing a VIN list in bulk, the model cannot match the corresponding vehicle's configuration data, returning empty results or incorrect matches. Cause:
maxContextwas set too small, truncating the associated information between VIN and part configurations, preventing the model from completing accurate matching.
How to verify proper configuration
- Upload a single vehicle's VIN list and corresponding tightening torque data, trigger a model call, and check if the returned results include the part configuration matching content corresponding to the VIN.
- Upload an image of an assembly work instruction, verify that the model can correctly recognize the text and annotation information in the image, confirming that the
visionconfiguration is effective. - Call the interface of the locally deployed large model, check if the interface return status code meets expectations, verifying that the API address and key configuration are correct.
- Initiate a model call using an unauthorized key, confirm that the interface returns a permission error, verifying that the access control configuration is effective.
The values provided are common starting points, and should be evaluated against local deployment samples.