What the data for this category looks like
This category’s data comes from raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Data is updated in real time per production batch and per cell. Some online testing data is generated immediately during the production process. Daily documents include batching records, coating and roller pressing parameter ledgers, winding or lamination process records, liquid injection operation logs, formation and grading data reports, OCV and safety test reports. Each document contains fields such as process parameters, batch identifiers, and test results for the corresponding process.
What constraints do these characteristics impose on the model access and configuration link
The multi-source real-time data and process sensitivity of lithium-ion cell manufacturing impose multiple constraints on the model access and configuration link. High-frequency updated data for each batch and each cell will quickly consume context window resources. The context scale of single-round calls and the volume of data processed in a single operation must be strictly limited. The exclusive process fields relied on by this category, such as slurry, coating, formation and grading, require that embedded models adapt to the professional terminology and data structure of targeted sub-scenarios. At the same time, processing classified data such as safety test reports requires that the configuration link clearly define the permission scope for data calls to avoid sensitive information leakage.
How to set the configuration
The following configurations are formulated based on the interface logic of FastGPT v4.17.0.
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Adapts to the total length of multiple process documents for a single battery batch, avoiding context overflow |
maxTokens | 2000–3000 token | Controls the output length of single-round quotation or document generation, aligning with the paragraph specifications of tender documents |
quoteMaxToken | 500–800 token | Limits the length of a single referenced process data entry, ensuring the readability and accuracy of referenced content |
temperature | 0.2–0.4 | Reduces generation randomness, ensuring consistency of process parameters in tender documents and quotations |
AIPROXY_API_ENDPOINT | Fill in the local or cloud interface address according to the deployment environment | Adapts to the enterprise’s internal model deployment architecture, ensuring the security of data calls |
AIPROXY_API_TOKEN | Enterprise-specific encrypted token | Restricts interface access permissions to prevent unauthorized calls |
Three common configuration mistakes
- Empty content returned after calling the embedded model. This is caused by incorrect configuration of the
AIPROXY_API_ENDPOINTaccess address, or the locally deployed model not having external access permissions enabled. - Inconsistent process parameters appearing in generated quotation sheets. This is caused by the
temperaturevalue falling outside the reasonable range, leading to insufficient parameter stability of generated results. - Structured process records failing to be correctly integrated into tender documents. This is caused by an excessively small
maxContextsetting that cannot fully accommodate the complete process documents for a single batch.
How to confirm the configuration is complete
- Initiate an embedding test for process data of a single battery batch. Verify the field completeness of the embedding result, and confirm that the configured context window can accommodate the target data volume.
- Generate a simulated tender document fragment. Verify the matching degree between the process parameters in the generated content and the preset values, and confirm that the
temperaturevalue meets the scenario requirements. - Call the configured model interface. Check the format and content of the returned result, and confirm that the permission configuration for
AIPROXY_API_TOKENis valid. - Test the access permissions for classified data. Confirm that the configured access rules can isolate unauthorized access to sensitive documents.
The values provided are common starting points and should be measured against the reader's own samples.