What the data for this category looks like
This category’s data covers the entire production process of lithium-ion cells. Data sources include raw material batch ledgers, workshop MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory OCV and safety test reports. Daily documents include batching records, coating and rolling parameters, winding or lamination records, liquid injection records, formation and grading data, and OCV and safety test reports. Data updates follow a cadence tied to each material batch and individual cell. Some process parameters, such as workshop dew point and coating areal density, are updated in real time. Fields include material batch numbers, process parameter thresholds, test result values, and more. Exclusive fields from each data source must be matched per process node.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
Lithium-ion cell manufacturing data is mostly multi-source heterogeneous and stored dispersedly by process node. Tool calling must support cross-data source associated queries to enable full-link traceability of a single cell. Real-time parameters such as workshop dew point and coating areal density require short-cycle pulling, so plugin configuration must adapt to frequency limits for high-frequency data requests. Traceability for a single cell requires association with multiple batches of raw materials and multi-stage process data. Tool calling must support nested queries and context splicing. Electrochemical manufacturing processes have extremely high requirements for data accuracy. Parameters returned by tools must match the legal threshold ranges of the corresponding process to avoid introducing abnormal data that interferes with defect analysis results. Formation and grading data are long-term stored time-series data. Tool calling must support filtering queries by batch or time range.
Configuration Settings
This configuration applies to FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Workflow/API tool selection | Force specify tool type | Must limit the scope of tool calling to avoid irrelevant tools interfering with quality traceability and defect analysis workflows |
| Number of items returned per single tool call | Top 10 entries | Prioritize returning core process parameters; too many entries will increase model context load |
| Tool call timeout period | 300–600 seconds | Formation and grading data are time-series data; pulling requires a longer cycle, so a reasonable range is set based on actual measurements |
| Context splicing length | 800–1200 characters | Must retain key process parameters for a single cell; excessive length will exceed model context limits |
| Question optimization switch | Enabled | Quality traceability questions need to be broken down into multi-stage queries; optimization can improve the accuracy of tool calling |
Three Common Misconfigurations
- Empty values returned after nested knowledge base assistant calls in workflows. Cause: Corresponding process data source fields are not matched, or tool calling fails to associate the correct MES and formation and grading data sources.
- Some browsers fail to display images and avatars when calling applications via iframe. Cause: Browser cross-origin resource sharing rules are not adapted, or resource paths are not configured as accessible static addresses.
- AI replies and next-level branch execution are printed simultaneously during tool calling. Cause: No output filtering rules are configured for tool calling, causing the context to retain both tool calling logs and subsequent branch trigger signals.
How to Verify Correct Configuration
- Initiate a traceability query for a single cell, and verify that the data source fields returned by tool calling match the document fields of links such as batching and coating.
- View tool calling logs to confirm that triggers only take effect at corresponding process nodes, with no redundant calling records.
- Simulate abnormal parameter input, and verify that the abnormal prompts returned by tool calling match the legal thresholds of the process.
- Test nested calling scenarios, and confirm that the output of the upper-level tool call only passes necessary parameters to the lower-level workflow, with no redundant content.
The values provided are common starting points and should be measured against the reader's own samples.