What Data for This Category Looks Like
Technical documents for lithium-ion cell manufacturing cover batching records, coating and calendering parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports, and more. Data sources include raw material batch ledgers, production MES systems, slurry and coating testing equipment, workshop dew point monitoring devices, formation and grading cabinets, and laboratory testing equipment. Batch records are generated for each cell production batch, matching the update cadence. Real-time data such as workshop dew point and real-time formation and grading parameters update synchronously with the production flow. Document fields include professional parameters like slurry solid content, coating areal density (g/㎡), calendering gap (μm), dew point temperature (℃), and others. Single records have many fields, with tight cross-document associations.
Constraints for Tool Calling and Plugins
The large number of professional document fields and tight cross-document associations require tool calling to accurately identify and verify field consistency across multiple documents. High-frequency updates to real-time data require tool calling to support low-latency data source connection and querying. The strong association of data with production batches requires tool calling to bind filtering rules for the current review batch, to avoid recalling historical data from unrelated batches. The high sensitivity of electrochemical systems to data deviations requires tool calling to trigger multi-source data cross-verification after returning results, to identify abnormal parameters that may pose safety risks.
Configuration Settings
The following configurations are based on FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | auto | Supports automatic tool call triggering logic for multiple types of professional documents, no manual specification of call nodes required |
| Single Tool Call Timeout | 300 seconds | Matches query response latency for real-time production data, prevents review interruptions caused by device delays |
| Knowledge Base Associated Batch Range | Current Production Batch | Limits recall scope to only the cell batch currently under review, prevents cross-batch data from interfering with review results |
| Context Window Size | 8000–12000 token | Accommodates full content of multiple professional documents, supports field association verification for long texts |
functionCall Parameter Format | Passed per local model adaptation rules | Differentiates parameter transfer requirements for online APIs and locally deployed models, ensures tool call instructions are parsed correctly |
Three Common Configuration Errors
- Duplicate thinking occurs during tool call node debugging. Cause: The optional range of
toolChoiceis not restricted, causing the large language model to repeatedly trigger tool call attempts. - No production batch-related knowledge base content can be queried after enabling tool calling. Cause: No filtering rule for the current production batch is configured for the knowledge base, leading to overly large recall scope or mismatch with target batch data.
- Tool call parameters fail to work correctly when calling a local large language model. Cause: The
functionCallparameter transfer format for local deployment and online APIs is confused, and the interface requirements of the local model are not adapted.
How to Verify Proper Configuration
- Trigger a test review, check the tool call log, confirm only one valid tool call is triggered.
- Manually input the coating areal density parameter for a specific batch, verify that the knowledge base recall results include the corresponding record for that batch.
- Check the fields returned by the tool call, confirm that parameters exclusive to lithium-ion cell manufacturing such as slurry solid content and coating areal density are included.
- Simulate a data deviation scenario, confirm that the tool call triggers the result verification process.
The values provided here are common starting points, and should be evaluated against the reader's own samples.