What data for this category includes
Data for this category comes from customer drawings and special characteristic lists, ERP/MES systems, measuring tools, equipment process parameters, and supplier quality certificates. Daily documents include control plans, process flowcharts, work instructions, first article and patrol inspection records, SPC reports, PPAP materials, and 8D reports. Data updates run per production batch and shift. Some equipment parameters are collected continuously. Data fields include batch identifiers, dimensional tolerances, torque parameters, inspection results, exception records, and other items. Some fields use standard units such as millimeters and newton-meters.
Constraints imposed on HTTP Interfaces and External Systems
The multi-source and high-frequency update nature of this category’s data requires HTTP interfaces to support pulling data from multiple systems and real-time synchronization. The batched data structure of high-volume production requires interface requests to carry precise identifiers such as batch numbers and product models. This prevents mixing of audit data from different batches. The large volume of continuously collected equipment parameter data requires interfaces to support segmented reception or paginated returns. This prevents timeouts caused by overload during single transmission. The strict constraints imposed by vehicle customers require interface requests to carry compliant identity verification parameters. This ensures data access meets customer quality system requirements. Some documents such as PPAP materials have long length. Interface-returned long text must support segmented processing. This adapts to the single-segment information carrying capacity of downstream audit workflows.
Configuration Settings
The following configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Carry a token in the format Bearer {oneapi_key} | Adapts to oneapi identity verification requirements, matches the verification method that works for Postman calls |
variables | Bind batch number and product model as request parameters | Matches the business characteristic of batch-managed data for this category, ensuring requests point to audit data for the corresponding production batch |
responseData | Enable long text segmented parsing | Adapts to processing requirements for long documents such as PPAP reports and 8D reports |
| Timeout period | 600 seconds | Covers longer-duration interface requests such as batch pulling of equipment parameters and PPAP material downloads |
| Retry count | 3 times | Addresses interface call failures caused by temporary measuring tool offline status and network fluctuations |
| Chunk length | 800–1200 characters | Adapts to the single-segment information density of automotive component audit documents |
Three Common Configuration Mistakes
- An interface call returns a 401 status code, while Postman calls to the same interface function normally. The cause is incorrect configuration of the identity verification token format, or failure to bind the correct oneapi key to the token parameter.
- Long text returned by the interface is not segmented as required. This causes downstream audit nodes to fail to read information completely. The cause is failure to enable the long text segmented parsing configuration item, or incorrect chunk length settings relative to document information density.
- A self-built model interface cannot be used in the content extraction node, even though toolChoice and functionCall parameters have been configured in config.json. The cause is that FastGPT’s content extraction node has not yet opened access rights for self-built models. Official model interfaces that meet requirements must be used.
How to Verify Configurations Are Correct
- Call a test interface, check that the request header carries a token in the
Bearer {oneapi_key}format, to verify that the identity verification configuration is active. - Import a PPAP material as test data, trigger an interface call, check that the returned results are split according to the 800–1200 character chunk length.
- View system logs, confirm that interface requests carry dynamic variables such as batch number and product model, ensuring data requests point to the correct production batch.
- Check the model configuration page, confirm that the locally deployed chatglm2 model appears in the available list, to verify that the model access configuration is active.
The values provided here are common starting points, and should be evaluated against the reader’s own samples.