What the data for this category looks like
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 documents include batching records, coating and rolling parameters, winding or lamination records, liquid injection records, formation and grading data, OCV and safety test reports. Data is updated in real time per battery batch and per individual cell. Fields include batch identifiers, process parameter values, detection times, and device codes. Units include grams per square meter, millivolts, degrees Celsius, and others.
What constraints this category imposes on the tool calling and plugins workflow
The real-time, multi-source, batch-wise updated data characteristics of this category create multiple constraints for tool calling and plugins. First, the demand for high-frequency, small-batch real-time data pulling requires plugin calls to support short timeout settings, to avoid impacting audit efficiency. Second, differences in multi-source heterogeneous data formats require plugins to adapt to interface specifications of different devices and systems, and unify data structures to match audit logic. Third, the strong binding between battery cell batches and detection data requires that data returned by tool calls strictly corresponds to specified batches, to avoid data misalignment affecting audit accuracy.
How to set configurations
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | Set to auto | Supports on-demand calling of multi-source detection data, matching the flexible needs of real-time audits |
| Function call limit | 3 times per round | Matches multi-stage data calls for single-batch cell audits, avoiding context overflow |
| Plugin timeout threshold | 60 seconds | Adapts to short-cycle data pulling for real-time dew point, coating testing, and similar scenarios, avoiding long waits that impact audit efficiency |
| Context window size | 8000–12000 token | Accommodates multiple sets of test records and process parameters for a single battery batch, avoiding truncation of critical information |
| Result recall limit | Top 5 entries | Focuses on core test items for a single batch, filters redundant data, and improves audit accuracy |
| Workflow/API function-call configuration | Enable mandatory validation | Ensures detection data returned by tool calls is strictly bound to battery cell batches, avoiding data misalignment |
Three common mistakes
- Symptom: A 514 status code is returned during tool calling. Cause: The plugin access whitelist is not configured, or the Docker port deployed on Alibaba Cloud does not open corresponding API call permissions.
- Symptom: Non-specified business data is accidentally introduced during tool calling. Cause: The global knowledge base association switch is not turned off, causing irrelevant information to be mixed into the tool call context.
- Symptom: Detection data returned by tool calls does not match the target battery cell batch. Cause: Parameter validation is not enabled, and the batch number parameter is not bound, leading to data misalignment.
How to confirm the configuration is correct
- On the background tool configuration page of FastGPT v4.17.0, verify that the
toolChoiceparameter is set toauto, and check the trigger records in the plugin call logs. - Import test data for a single battery batch, submit an audit request, and confirm that the detection data returned by the tool call completely matches the imported batch information.
- Simulate a high-frequency calling scenario, check that the response time of plugin calls does not exceed the preset threshold, and no timeout errors occur.
- View the context window logs, confirm that all process records for a single batch audit are fully loaded, with no truncation.
The values provided here are common starting points, and should be evaluated against the reader's own samples.