What data for this category looks like
Data for this category centers on individual vehicle VINs, and includes production orders, VIN configuration lists, assembly work instructions, tightening torque data, end-of-line inspection reports, rework records, and compliance release documents. Data primarily comes from ERP/MES systems, VIN and vehicle configuration systems, supplier batch management systems, workshop tightening tools, EOL inspection lines, and quality traceability systems. Updates are performed per individual vehicle: critical torque and inspection data are updated in real time, while batch configurations and release documents are synchronized per shift. Document fields include VIN, part numbers, torque values, inspection item thresholds, rework station numbers, and similar, with units mostly newton meters, millimeters, batch numbers, and other standard units.
Constraints imposed on HTTP interfaces and external systems by these characteristics
A VIN-centric data structure requires interfaces to support precise retrieval by VIN. Request parameters must include fixed field validation to prevent invalid retrieval. Real-time updated tightening and inspection data requires interface response latency to match workshop station cycles, to support production rhythms. Heterogeneous data from multiple system sources requires interfaces to support cross-source aggregation, to avoid returning content from only a single system per request. Batch documents synchronized per shift require interfaces to support pagination or sharded uploads, to accommodate large file transfers. Differences in field units across different documents require interfaces to either standardize formats or retain original units for direct use by downstream production systems.
How to configure settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Bearer {pre-configured enterprise-specific API key} | Follow FastGPT official interface authentication specifications to secure interface access |
variables | {"target_vin": "{{input.vin}}", "scene": "total_assembly"} | Adapt to VIN-centric retrieval logic to accurately match exclusive process specifications for the corresponding vehicle model |
responseData | Return the first 2 matching document fragments | The number of documents related to final assembly processes for a single vehicle is limited; excessive results will occupy excessive context space |
| Chunk Length | 800–1200 characters | The content length of individual final assembly work instructions falls within this range, balancing retrieval accuracy and context usage |
| Single Search Limit | Calibrated via actual testing | Adapt to station response cycles of different workshops to avoid exceeding production rhythm requirements |
| Maximum Context Window | 16000 token | Cover the context information required for complete process Q&A for a single vehicle, avoiding truncation of critical content |
Three common configuration mistakes
- Garbled content is returned via interface calls. The cause is failure to correctly specify the
Content-Typerequest header asapplication/json; charset=utf-8, leading to incorrect document encoding parsing. - Online chat results are correct but API call results have significant deviations. The cause is failure to pass the
variablesparameter consistent with online chat in API requests, leading to a mismatch between retrieval scope and context configuration. - Interface authentication fails after a version upgrade. The cause is failure to update the API key in the
Authorizationparameter to a version-compatible key, or failure to follow authentication format requirements of the new version.
How to confirm successful configuration
- Send a test request with a valid VIN parameter, and verify whether the returned results include assembly work instruction content for the corresponding vehicle model.
- Check whether the
responseDatafield of the interface response includes the expected number of matching documents, and adjust configuration items to meet business requirements. - Simulate a real-time workshop call scenario, and verify whether interface response latency meets the time requirements for station operations.
- Compare identical input results from online chat and API calls, and confirm that parameters such as
variablesare passed consistently.
The values given are common starting points and should be measured against the reader's own samples.