What data for this category looks like
Inspection and audit data for furniture and wood product manufacturing typically comes from order design systems, sheet material and wood batch ledgers, CNC cutting and drilling equipment, ERP/MES systems, and moisture content testing equipment. Data update frequencies are per order, daily, and per material batch. Daily associated documents include order breakdown drawings, cutting lists, edge banding and drilling programs, coating processes, hardware lists, and assembly and packaging records. Data fields include panel dimensions, sheet moisture content, hardware model numbers, order batch numbers, and more. Common units are millimeters, percentages, and batch numbers, and the structure is dynamically adjusted based on custom order requirements.
What constraints these characteristics impose on HTTP interfaces and external systems
Multi-source heterogeneous data sources require interfaces to support multi-field mapping and format verification. This prevents data mismatches between different systems. Business characteristics driven by custom orders require interfaces to support dynamic input of personalized parameters. This adapts to panel specifications and process requirements for different orders. High-frequency updated material and production data requires interfaces to support batch synchronization or real-time calls. This ensures the timeliness of audit data. Abnormal panel data directly affects production and installation. Interfaces need a complete failure handling mechanism to facilitate traceability and correction of abnormal data.
How to set the configuration
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
Authorization | Carry the platform API key with the Bearer prefix | Complies with FastGPT external interface identity verification specifications to ensure call security |
variables | Pass custom key-value pairs such as order ID, sheet batch number, panel dimensions, etc. | Adapts to the transfer of personalized order and material data for furniture manufacturing scenarios |
responseData | Configure structured fields that return audit pass rate, abnormal panel list, and process matching results | Facilitates docking with ERP/MES systems to synchronize production audit status |
| Single request limit | Top 20 records | Matches the conventional panel quantity range of a single batch cutting list |
| Request timeout | 600 seconds | Adapts to the parsing and verification time required for complex order breakdown drawings and coating process documents |
Three common errors
- An API call returns empty values. This is manifested as the interface returning no valid audit results. The cause is that order context parameters required for the associated knowledge base are not passed in
variables, making it impossible to match corresponding order breakdown drawings and cutting list data. - Uploading a CSV template fails to correctly recognize the first column as questions and the second column as answers. This is manifested as incorrect knowledge base question-and-answer matching or no matching results. The cause is that field mapping rules are not configured according to FastGPT requirements, and the roles of the corresponding columns are not specified.
- Interface requests cannot generate parameter logs. This is manifested as no request parameter details in the debug interface. The cause is that the parameter printing function is not enabled in the debug configuration, or the correct debug identifier is not carried in the request.
How to confirm the configuration is complete
- Initiate a test request containing a test order ID. Check whether expected audit result fields are returned in
responseData, and confirm that identity verification has passed. - Upload a test CSV template with the format "first column is questions, second column is answers". Verify that field mapping is correct, and confirm that the knowledge base association function is active.
- Simulate a batch panel data request. Confirm that the single request limit and timeout configuration meet the time consumption requirements of the current business scenario.
- Enable debug mode to initiate a request. Check whether custom parameters passed in
variablesare included in the returned log, and confirm that parameter parsing is correct. This configuration applies to FastGPT v4.17.0.
The values given are common starting points and should be measured against the reader's own samples.