What the data for this category looks like
The data for this category comes from raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing systems. Daily generated documents cover batching records, coating and rolling parameter ledgers, winding or lamination operation records, liquid injection parameter logs, formation and grading data reports, and OCV and safety test reports. Data is updated in real time per batch and per cell dimension. Document fields include slurry ratio parameters, coating areal density values, rolling gap values, dew point temperature values, formation and grading voltage values, internal resistance detection values, and more.
What constraints these characteristics impose on model access and configuration
The data characteristics of this category impose multiple constraints on model access and configuration. Real-time data updated per batch and per cell dimension requires limiting the model’s context length. Excessive context length causes processing delays, which fail to meet production real-time requirements. Heterogeneous data formats from multiple device sources require adding data format verification rules during configuration. This prevents access to non-standard production logs. Fine-grained professional production fields require the model to accurately match corresponding parameters. Reasonable context recall rules must be configured to prevent cross-batch data confusion. Frequently updated data also requires configuring an appropriate context refresh mechanism. This ensures the model uses the latest production document data when called.
How to set configurations
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
AIPROXY_API_ENDPOINT | Internal FastGPT proxy interface address of the enterprise | Adapt to internal production data access permissions for lithium-ion cell manufacturing scenarios, ensuring secure data transmission |
maxContext | 8000–12000 token | Match the total token scale of production-related documents for a single batch, avoid context overflow that causes call failures |
maxTokens | 2000–3000 token | Cover the complete output length required for cross-review of professional production parameters, meet multi-dimensional document comparison needs |
quoteMaxToken | 1500 token | Limit the reference length of a single core production document, ensure review content is focused and does not exceed the model's processing limit |
temperature | 0.1–0.3 | Maintain the rigor of production parameter review, avoid generating non-standard results that do not comply with process specifications |
| Workflow/API tool selection | none | This scenario only requires cross-review of documents, no external tool calls are needed |
Three common misconfigurations
- Phenomenon: After calling a third-party large model, the output still contains thinking content wrapped in
<think>tags. Cause: The model's thinking output switch was not configured correctly, or the switch configuration was not synchronized to the proxy interface. - Phenomenon: After accessing the internal production data interface, a prompt indicating unauthorized access appears. Cause: The IP address of the production data source was not added to the access whitelist of the FastGPT proxy, or the
AIPROXY_API_TOKENconfiguration was incorrect. - Phenomenon: The cross-review results returned by the model do not correctly associate parameters from the corresponding production documents. Cause: The
maxContextvalue is too small, causing core production documents to not be fully included in the context, making parameter matching impossible.
How to verify proper configuration
- Call the test interface, enter the cross-review requirement for a single batch of production documents, check whether the output contains
<think>tags, and confirm that the thinking output switch configuration meets expectations. - View the model call log, confirm that the verification status of
AIPROXY_API_TOKENis passed, and check whether the interface address matches the internally deployed FastGPT proxy address of the enterprise. - Enter a query containing specific production parameters, verify whether the reference content returned by the model includes production documents from the corresponding batch, and confirm that the context recall range complies with the configuration requirements.
- Adjust the
temperatureparameter, observe the consistency of the output results, and confirm that the parameter value adapts to the rigor requirements of the scenario.
The values provided here are common starting points, and should be measured against the reader’s own samples.