What data for this category looks like
Data sources for this category include ERP orders and inventory, BOMs, process routes, equipment capacity, staff shifts, work-in-progress, and supplier delivery information. Daily processed documents include sales orders, master production plans, material requirements plans, weekly and daily scheduling tables, work orders, material shortage lists, and delivery change notices. Each document contains fields such as order number, material code, capacity value, shift duration, and work order quantity. Units include pieces, hours, days, and others. Data update frequencies include daily, weekly, and real-time updates when orders or exceptions occur.
Constraints imposed by these characteristics on model access and configuration
Multi-source, multi-update-frequency data requires model access to adapt to different synchronization frequencies, to avoid audit result deviations caused by data lag. Diverse document structures and field types require configurations to support recognition of different business field formats, such as numeric capacity data and text-based delivery time descriptions. High-frequency exception updates such as emergency order additions and material shortages require configurations to have rapid response capabilities, while controlling context length to avoid redundant data interfering with audit logic.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | This category needs to cover at least 3 days of scheduling data and associated orders; this range can fully carry the core audit context |
maxTokens | 4000–6000 token | Complete scheduling adjustment suggestions and audit conclusions need to be generated; this range balances output length and content completeness |
quoteMaxToken | 2000–3000 token | Core document fragments such as work orders and material shortage lists need to be referenced; this range retains sufficient original text details |
| Workflow/API tool selection | auto | Tools need to be automatically selected or results generated directly based on audit scenarios, to adapt to different tasks such as scheduling adjustments and material shortage audits |
temperature | 0.2–0.4 | Audit scenarios require rigorous results; this value range avoids generalization deviations and ensures audit conclusions comply with business rules |
AIPROXY_API_ENDPOINT | Exclusive proxy endpoint provided by the platform | Ensures stability and security of model calls, and adapts to access requirements for domestic and international model channels |
AIPROXY_API_TOKEN | Exclusive key bound to the endpoint | Verifies call identity and prevents unauthorized model access |
Three common configuration errors
- Phenomenon: An authorization error is returned when calling the specified model, and the audit task cannot be completed normally. Cause:
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare not configured correctly, or a mismatched proxy channel is used. - Phenomenon: When the corresponding scheduling data is not present in the knowledge base, the model directly refuses to generate inference results. Cause: The context configuration is not adjusted, or reasonable generalization logic is not enabled, and search matching is mistakenly set as a mandatory dependency.
- Phenomenon: A timeout error is returned when calling tools to process scheduling adjustments. Cause: A reasonable
maxTokenslimit is not set, causing tool call duration to exceed system thresholds.
How to confirm configuration is complete
- Confirm that the currently used FastGPT version is v4.17.0, to ensure configuration items match the interface.
- Submit test work order and material shortage list data, and check whether the audit results returned by the model include reference fragments of the corresponding documents.
- Simulate emergency order addition or equipment failure scenarios, and verify whether the model can generate adjusted audit suggestions based on real-time updated capacity and inventory data.
- View the model call log, confirm that the request status codes for
AIPROXY_API_ENDPOINTandAIPROXY_API_TOKENare 200, with no authorization or connection errors.
The values provided are common starting points and should be measured against the reader's own samples.