What Data for This Category Looks Like
Data for this category primarily comes from order and inventory data, BOM lists, process routes, equipment capacity ledgers, staff shift schedules, work-in-progress inventory, and supplier arrival records from enterprise ERP systems. Daily processed documents include sales orders, master production schedules, material requirements plans, weekly and daily scheduling tables, production work orders, material shortage lists, and delivery change notices. Data update frequency follows business nodes. It is usually synchronized at fixed daily or weekly times, or updated immediately when exceptions occur such as order placement, delivery changes, or material shortages. Fields include order number, material code, capacity value, scheduling period, work order status, and more. There are no unified fixed units, and matching must be done according to corresponding business fields.
Constraints for HTTP Interfaces and External Systems
Data sources for this category are scattered, and update rhythms are flexible. This requires HTTP interfaces to support integration with multiple types of external systems, and adapt to business documents in different formats. Multi-type business documents contain large numbers of structured fields. Interfaces must support batch transfer of data sets with different structures, such as orders, work orders, and scheduling tables. The sudden nature of data updates requires interfaces to support immediate trigger synchronization, while also being compatible with regular scheduling needs for timed pulling. Data abnormalities directly affect production scheduling accuracy. Interfaces must provide clear status feedback and exception capture capabilities to avoid production disruptions caused by data transmission issues.
The following configuration is based on FastGPT v4.17.0.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer {API_KEY} | Complies with HTTP interface standard authentication specifications and adapts to FastGPT's interface authentication logic |
variables | Populate according to business fields, such as {"order_id": "xxx", "shift": "morning shift"} | Used to pass exclusive business parameters for production planning to FastGPT, improving audit accuracy |
responseData | ["audit_result", "suggestion", "update_time"] | Only returns core fields required for inspection and audit, reducing data transmission overhead |
| Request timeout | 30–60 seconds | Production planning data is usually large, so sufficient processing time must be reserved to avoid request interruption due to timeout |
| Retry count | 2–3 times | Addresses network fluctuations or temporary unavailability of external systems, ensuring data synchronization reliability |
| Batch request limit | First 20 work orders | Avoids excessive interface load caused by too large a single request data volume, adapting to the conventional processing scale of shop scheduling |
Three Common Misconfigurations
- Symptom: Business fields passed when calling the
/api/v1/chat/completionsinterface are empty. Cause: Exclusive business parameters for production planning are not correctly configured in thevariablesparameter, so FastGPT cannot match the corresponding audit rules. - Symptom: A cross-domain error or style abnormality is returned after the front end calls the interface directly. Cause: The interface request is not forwarded via a backend proxy, exposing the API key directly or triggering browser cross-domain restrictions.
- Symptom: The audit result returned by the interface does not include scheduling adjustment suggestions. Cause: Core constraint data such as equipment capacity and staff shifts are not passed in the request, so FastGPT cannot generate targeted audit suggestions.
How to Confirm Configuration Is Complete
- Check that the format of the
Authorizationparameter conforms to the Bearer token specification, and confirm that the API key is not leaked or configured incorrectly. - Send a test request to verify that the business fields in the
variablesparameter can be correctly recognized by FastGPT and used in the audit logic. - Simulate network fluctuations or external system delay scenarios to confirm that the interface retry mechanism triggers as configured.
- View the
responseDatafield returned by the interface to confirm that only preset core information such as audit results and suggestions is included, with no redundant data.
The values provided here are common starting points and should be evaluated against the reader's own samples.