What Data Looks Like for This Category
Data for this category originates from formulation and process libraries, raw material batch ledgers, reactor DCS systems, weighing record systems, LIMS laboratory information management systems, and warehouse systems. Data updates per production batch and each critical process step. It includes documents such as batch production instructions, feeding review sheets, reaction process records, cleaning records, finished product inspection reports, and deviation and change orders. Fields cover raw material batch numbers, reaction temperatures, feeding quantities, cleaning validation results, and more. Units include kilograms, degrees Celsius, hours, and others. Multi-product batch switching requires distinguishing formulation and cleaning records for different batches.
Constraints Imposed on Tool Calling and Plugins
The multi-product, batch-switching production mode requires binding tool calling to the context of the current production batch, and only calling raw material, reaction, and cleaning data for the corresponding batch. Data updates frequently and has fine granularity, so tool calling triggers must match process nodes. This avoids incomplete data caused by early or late calls. Formulation confidentiality requires restricting document access permissions for tool calling by role, only opening non-confidential fields required for the audit process. Cross-contamination prevention requirements mandate that tool calling automatically associates the cleaning validation results of the previous batch to complete compliance checks.
Configuration Settings
The following configurations are based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
| Workflow/API tool selection | "auto" | Automatically matches tools required for the current audit step, adapts to multi-step audit processes |
| Tool Trigger Nodes | Trigger at three nodes: feeding completion, reaction temperature stabilization for 900 seconds, and cleaning validation completion | Matches critical process steps of each batch in this category to ensure complete called data |
| Context Window Limit | Only include document data within 12000 characters of the current batch | Avoids cross-batch data interference, keeps context length within system bearing requirements |
| Tool Call Timeout | 600 seconds | Adapts to the cycle of reaction steps in this category, reserves sufficient time for tool calls and data retrieval |
| Document Access Permission Scope | Only open feeding, inspection, and cleaning fields for the current batch | Meets formulation confidentiality requirements, limits access scope for non-essential data |
Three Common Misconfigurations
- Symptom: Tool call returns an empty response, and logs show no matching data. Cause: The context is not restricted to only documents from the current batch, and cross-batch historical data is called, causing the tool to fail to recognize the current audit parameters.
- Symptom: Custom environment variables cannot be read in the workflow, causing tool call failure. Cause: The workflow's environment variable call permission is not enabled in system configuration, or the variable reference format is not used correctly.
- Symptom: The application test works with a specific model, but an error is returned via API call. Cause: The current batch context parameters are not passed correctly in the API request, or the model's API key environment variable is not configured.
How to Confirm Proper Configuration
- Trigger the feeding review node for the current batch, check if the tool automatically calls the corresponding data query tool and returns the raw material batch information for the current batch.
- View system logs to confirm that the tool call context only includes documents from the current batch, with no data from other batches included.
- Call the API test interface, pass the parameters for the current batch, and check if tool call results are returned normally, with no missing fields or timeout errors.
- Add a print environment variable node to the workflow, run it, and confirm that custom configured parameters can be read correctly.
The values provided are common starting points and should be measured against the reader's own samples.