What the Data for This Category Looks Like
Data for this category typically uses VIN as the single-vehicle main line, and includes documents such as production orders, VIN lists, assembly work instructions, tightening data, offline inspection reports, rework records, and consistency and release documents. Teams collect this data from ERP/MES systems, VIN and configuration systems, supplier batch data, tightening tools, EOL inspection lines, and quality traceability systems. Update frequencies cover per vehicle, real-time, and per shift cycles. Data fields include standardized identifiers such as VIN codes, part numbers, torque values, and inspection results.
What Constraints These Characteristics Impose on the Model Access and Configuration Link
This category uses VIN as the single-vehicle main line for large-scale mixed-line production configurations. When setting up model access, ensure the model strictly matches retrieved data to the VIN dimension of each individual vehicle to avoid cross-vehicle configuration confusion. Real-time updated tightening data and offline inspection reports require model response delays to align with production beat limits, so limit context loading duration. A large number of part configuration items require a sufficient context window to store historical data. At the same time, strictly control output accuracy to avoid line stoppage or recall risks caused by mismatched or missed part installations.
How to Set the Configuration
This document is written based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale for This Setting |
|---|---|---|
maxContext | 12000–16000 token | Must accommodate VIN lists, assembly requirements, and historical quotation data for multiple vehicles to ensure full coverage of business scenarios |
maxTokens | 3000–4000 token | Matches the standard output length of a single bid document or quotation sheet, avoiding truncation of critical configuration information |
quoteMaxToken | 800–1200 token | Adapts to the output length of single-segment content such as part lists and quotation details, ensuring complete information |
| Workflow/API tool selection | auto | Automatically calls the document retrieval tool to match part configurations corresponding to VIN, reducing manual intervention errors |
temperature | 0.2–0.4 | Controls output randomness to ensure bid and quotation content is consistent and compliant |
AIPROXY_API_ENDPOINT | Enterprise-exclusive internal proxy address | Ensures data transmission complies with internal network security rules, avoiding production data leaks via external interfaces |
Three Common Misconfigurations
- Phenomenon: After switching the knowledge base vector model, the interface shows a successful switch, but retrieval has no progress and the original model cannot be switched back. Cause: The vector cache of the old model was not cleared, and index conflicts between the old and new models prevent the system from normally loading retrieved data.
- Phenomenon: The
chat:LLM_model_response_emptyfield appears in the logs, and the large model returns empty content after being called. Cause: ThemaxContextsetting is too high, causing the model context to overflow, or thetemperatureparameter exceeds a reasonable range, causing an abnormal exit. - Phenomenon: The same VIN configuration question returns different part lists multiple times. Cause: The
temperatureparameter was not fixed, or the retrieval context was not split by VIN dimension, resulting in matching irrelevant historical data.
How to Confirm the Configuration Is Correct
- Submit sample data containing a test VIN, and check whether the part configuration and tightening parameters returned by the model match the original data exported from the ERP system.
- View the interface call logs to confirm that the
AIPROXY_API_TOKENverification is passed, and there are no timeout or permission error messages. - Submit the same test question three times in a row, and confirm that the output results have no obvious randomness and the configuration information remains consistent.
- Adjust the
maxContextparameter to a reasonable range, and verify that the context loading duration meets the production beat requirements.
The values provided here are common starting points and should be measured against the reader's own samples.