What the data for this category looks like
Data sources for this category include raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring systems, formation and grading cabinets, and laboratory testing equipment. Data update cadence follows these rules: Raw material batch data is updated synchronously with delivery. Records for production steps such as batching and coating are generated in real time with each batch of cell production. Formation and grading data, OCV, and safety test reports for individual cells are updated immediately after testing is complete. Core documents include batching records, coating roller pressing parameters, winding or stacking records, liquid injection records, formation and grading data, and OCV test reports. Each document contains process parameters, test values, and batch identifiers for its corresponding production step.
What constraints do these characteristics impose on the tool calling and plugins workflow
Multi-source heterogeneous data sources require tool calling to support cross-system data pulling, and adaptation to the interface formats of different devices. Real-time data updated per individual cell or batch requires tool calling to support precise data location using batch numbers or individual cell IDs, to avoid mixing test results from different cells. Electrochemical systems have strict requirements for parameter accuracy; tool calling input parameters must strictly match the units and value ranges of process fields. Large volumes of single-batch test data increase context load, so tool return fields and data volume must be limited to prevent exceeding the model's processing limits.
Configuration Settings
The following configurations are formulated for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Adapt to multi-step audit requirements, automatically call tools such as raw material query, parameter verification, and data pulling |
| Workflow/API function-call configuration | Enabled | Force structured output of tool input parameters to ensure parameter formats match business data requirements |
| Single tool call limit | Top 3 entries | Match the scale of core audit data for a single cell batch, reducing invalid data transmission |
| Context retention duration | 3600 seconds | Cover the typical cycle of formation and grading tests, avoiding interference from expired data on audit processes |
| Field filtering rule | Return only business-required fields | Focus on core audit parameters such as solid content, areal density, and internal resistance, improving calling efficiency |
Three Common Misconfigurations
- Tool interface calls return empty results, with input parameter fields not passed in structured format. This occurs because the
functionCallconfiguration is not enabled, preventing the tool from parsing non-standard input parameter content. - Calls to deployed local models return empty content, with the tool context containing unfiltered redundant test data. This occurs because no field filtering rules are set, and excessive historical data exceeds the model's context capacity, preventing the model from generating valid responses.
- File parsing interface calls succeed but return no parsing results, with no correct trigger conditions bound. This occurs because tool calling is not associated with document upload actions, causing parsing tasks to not be scheduled and executed normally.
How to Confirm Configurations Are Correctly Set
- Initiate a simulated tool call to verify that the returned input parameter format meets preset structured requirements.
- Review tool call logs to confirm that only core field data required for the business is returned.
- Test calls initiated using batch numbers or individual cell IDs to confirm that production and test data for the corresponding process steps can be accurately pulled.
- Input abnormal parameters that do not meet format requirements to confirm that the tool triggers verification and returns clear error prompts.
The values provided are common starting points and should be measured against the reader's own samples.