What this category's data looks like
Daily documents for this category include sales orders, master production schedules, material requirement plans, weekly production scheduling sheets, work orders, material shortage lists, and delivery change notices. Data sources include ERP orders and inventory, bills of materials (BOMs) and process routes, equipment capacity, staff shifts, work in process, and supplier deliveries. Update frequency is daily, weekly, or triggered when orders or exceptions occur. Data fields include order numbers, material codes, capacity man-hours, shift headcount, material shortage quantities, and others. Units include pieces, hours, days, and others.
What constraints do these characteristics impose on the model access and configuration link
The data sources for this category are scattered and update rhythms are unstable. The model access link must support cross-source data calls and incremental synchronization across multiple systems. The structure of daily documents varies greatly, with a mix of long texts and short fields, requiring the configuration link to adapt to context truncation rules for different documents. Scenarios where updates are triggered by exceptions require configuration of real-time response call logic to avoid delays in scheduling adjustments. At the same time, the data involves constrained values such as production capacity and materials, requiring the model access link to correctly identify and process field information with units to ensure the accuracy of responses.
How to set the configuration
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the long context requirements of production planning documents, covering multiple types of long text data such as orders and scheduling sheets |
maxTokens | 2000–3000 token | Meets the response length requirements of equipment inspection and maintenance Q&A, covering content such as process instructions and troubleshooting steps |
quoteMaxToken | 1500–2000 token | Limits the length of quoted production document fragments to avoid introducing irrelevant redundant scheduling data |
temperature | 0.2–0.4 | Reduces random output to ensure consistency of scheduling suggestions and troubleshooting plans |
AIPROXY_API_ENDPOINT | Fill in the deployed proxy address | Adapts to cross-domain access requirements for multi-data source docking |
AIPROXY_API_TOKEN | Fill in the actually generated key | Ensures the security of interface calls |
Three common mistakes
- The symptom is
undefined No available channel for model gpt-4o in the default group. The cause is that no model channel adapted to production planning data has been configured in the corresponding group, or the API parameters of the channel configuration do not match the requirements for production data calls. - The symptom is that when creating an application, the model cannot be selected for configuration result rearrangement. The cause is that the call permission for the rearrangement model has not been correctly configured in the configuration file, or the parameters of the rearrangement model do not match the field format of production documents.
- The symptom is that FastGPT reports an error indicating no available embedding channel. The cause is that the bce-embedding channel configured in oneapi has not been bound to the model group for the production planning scenario, or the access permission of the channel has not been enabled.
How to confirm the configuration is complete
- Call the test interface, enter questions related to production scheduling, and check whether the returned results include correct work order decomposition and equipment capacity matching content.
- View the channel management page, confirm that the model channel status for the corresponding production scenario is available, with no error prompts.
- Upload test documents such as sales orders and scheduling sheets, and check whether the model can correctly extract field information such as material requirements and material shortage lists.
- Trigger an exception update scenario, such as simulating an order insertion, and check whether the model can respond in real time and generate adjusted scheduling suggestions.
The values provided are common starting points and should be measured against the reader's own samples.