What Data for This Category Looks Like
Data for this category comes from enterprise ERP system order and inventory records, BOM lists, process routes, equipment capacity ledgers, staff scheduling rosters, work-in-progress inventory logs, and supplier arrival receipts. Data updates follow three rhythms: daily synchronization of capacity and scheduling, weekly updates of the master production plan, and real-time refreshes when exceptions occur, such as order changes, material shortages, or equipment failures. Daily processed documents include sales orders, material requirement plans, weekly scheduling sheets, production work orders, material shortage lists, and delivery change notices. Common fields include order numbers, material codes, process man-hours, equipment load rates, and work-in-progress quantities. Supported units include pieces, hours, units, and shifts.
What Constraints These Characteristics Impose on Model Access and Configuration
Multiple heterogeneous data sources require model access to support cross-system data pulling and format adaptation. This prevents information loss caused by field mismatches. Uncertain update frequencies require configurations to support incremental synchronization and on-demand trigger refreshes. This ensures the timeliness of scheduling and audit data. Complex document structures and multi-dimensional fields require model context configurations to support long-text processing. They also require setting field mapping rules for different document types. Production planning and scheduling require precise result outputs. Model configurations must balance response speed and output stability. This avoids scheduling suggestion deviations or invalid audit conclusions caused by improper parameter settings.
How to Configure Settings
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Production planning and scheduling data includes multiple related documents. Pass complete content such as orders, scheduling sheets, and material shortage lists to ensure logical connections |
maxTokens | 4000–6000 token | Meet the text length requirements for generating scheduling adjustment suggestions or quality audit conclusions, and avoid truncated output |
temperature | 0.2–0.4 | Production scenarios require precise result outputs. Reduce randomness to ensure consistency of scheduling and audit conclusions |
quoteMaxToken | 1500–2000 token | Limit the length of a single referenced document, and avoid overly long content interfering with the model's judgment of core scheduling logic |
AIPROXY_API_ENDPOINT | Internal private enterprise proxy address | Ensure production-sensitive data is not directly exposed to external networks, and comply with data security requirements of manufacturing enterprises |
| Workflow/API tool selection | auto | Allow the model to independently determine whether to call tools such as scheduling calculations and data verification, and adapt to complex audit and scheduling scenarios |
Three Common Configuration Mistakes
- Phenomenon: The knowledge base can retrieve scheduling sheet and work order data, but no output is generated after passing the data to the large model. Cause: The
maxContextconfiguration value is too small. Retrieved long text exceeds the context limit and is truncated, causing the model to fail to obtain complete input information. - Phenomenon: Scheduling suggestions generated by the model seriously deviate from actual equipment capacity and staff shifts. Cause: The
temperaturevalue is set too high, leading to excessive randomness in model output and deviating from the precision requirements of production planning. - Phenomenon: Model calls return authentication failure or interface exceptions. Cause: The
AIPROXY_API_TOKENis filled incorrectly, or the external model is called directly without configuring the internal proxy, which violates production data security specifications.
How to Confirm Configuration Is Complete
This verification process is performed based on FastGPT v4.17.0.
- Upload a test work order and weekly scheduling sheet, trigger a model call, and check whether the passed document content is fully displayed in the context display panel.
- Adjust the
temperatureparameter and initiate another test request. Compare the two generated audit conclusions to confirm that the result stability meets production scenario requirements. - Fill in an incorrect
AIPROXY_API_TOKENto verify whether an authentication error is triggered, and confirm that the proxy and key configurations are effective. - Simulate an order insertion scenario to initiate a request, and check whether the model can associate existing scheduling data to generate reasonable adjustment suggestions.
The values provided here are common starting points and should be measured against the reader's own samples.