What the Data for This Category Looks Like
Data for this category originates from registered and filed formulas, raw material safety documentation, weighing and emulsifying equipment, ERP/MES systems, laboratory test records, and packaging material version libraries of skincare, haircare, makeup, and special cosmetics manufacturers. Daily documents include formula process sheets, weighing and feeding logs, emulsification and filling records, site cleaning and clearance records, semi-finished and finished product inspection reports, and label review forms. Data is updated per production batch and each line change. Fields cover formula ID, restricted substance content of raw materials, production parameter thresholds, inspection pass indicators, and associated filing numbers.
Constraints Imposed by These Characteristics on Workflow Orchestration
Data for this category is updated per production batch and line change. Workflows must be triggered and bound to matching production batch nodes to prevent cross-batch data mixing. Multi-source data requires configuring multiple workflow nodes to pull data from different systems, and adaptation to varying formats of inspection reports and process records is required. Formulas and labels are subject to filing regulations. Unlike pharmaceutical regulatory frameworks focused on therapeutic effects, workflows must include a filing information verification step to confirm parameters align with filed content. Production parameters such as emulsification temperature and homogenization time must be bound to real-time data collected by equipment. Parameter threshold triggers for verification must be set. Errors may lead to product removal and recall, so workflows must include mandatory verification in the review step. Unsuccessful verification blocks subsequent processes.
Configuration Settings
The following configurations are developed for FastGPT v4.17.0.
| Configuration Item | Recommended Approach | Basis for This Approach |
|---|---|---|
| Trigger Mode | Triggered by production batch nodes | Matches the data update characteristics of this category to avoid mixing data across batches |
| Variable Configuration | Bind variables to filed formula ID and production batch number | Used to associate batch and filing information of multi-source data to ensure verification accuracy |
| Node ID Configuration | Bind nodeId to equipment data collection nodes and laboratory inspection nodes | Distinguishes business data from different sources to avoid node confusion |
| Timeout Threshold | 600 seconds | Adapts to data pull delays from ERP/MES and laboratory systems, calibrated based on actual measurements |
| Single Pull Count | Top 10 entries | Adapts to the bulk pull requirement for laboratory inspection reports, calibrated based on actual measurements |
| Exception Interception Configuration | Block the workflow when parameters exceed thresholds | Corresponds to the verification requirement for production parameters, intercepting abnormal processes in a timely manner |
Three Common Mistakes
- Symptom: An error
localStorage is nooccurs when running the codelocalStorage.getItem('userId'). Cause: The FastGPT workflow runtime environment has no browser context, so it cannot call the browser-built-in localStorage object. - Symptom: After obtaining an image URL in the workflow, the image cannot be displayed in the chat interface. Cause: The image rendering node is not configured, or the image URL is not bound to the correct output field.
- Symptom: When calling API - Start Session - Request Workflow, the returned result has no thinking process field. Cause: The thinking process output is not enabled in the large model node of the workflow, or the thinking process is not bound to the API output link.
How to Verify the Configuration Is Correct
- Trigger test: Manually trigger the workflow for the corresponding production batch, check whether only relevant documents and data for that batch are pulled.
- Variable verification: View the fields bound to
variables, confirm that the filed formula ID and production batch number match the test batch. - Node configuration check: Check the equipment and laboratory nodes bound to
nodeId, confirm that the data sources match actual business needs. - Abnormal process test: Simulate a scenario where parameters exceed thresholds, check whether the workflow triggers verification and blocks the process.
The values provided are common starting points and should be evaluated against the reader's own samples.