What the data for this category looks like
Skincare, haircare, makeup, and special cosmetics manufacturers generate daily documents including formula work instructions, weighing and feeding records, emulsification or filling records, cleaning and site clearance records, semi-finished and finished product inspection reports, and label review documents. Data comes from registered and filed formulas, raw material safety materials, production equipment logs, ERP/MES systems, laboratory test data, and packaging material version information. Data updates trigger with each production batch or line change. Fields include formula raw materials, production parameters, test results, and packaging material batch numbers. The system must support access to multi-source heterogeneous unstructured and structured data.
What constraints these characteristics impose on deployment and upgrade
Multi-source heterogeneous production document types require deployment support for unstructured data parsing and multi-system data source access. Pre-built parsing rules for different document formats must be included. High-frequency data updates per batch or line change require configuration of a reasonable incremental synchronization mechanism during deployment. This avoids excessive system resource usage from full data pulls. Regulated fields such as formulas and labels require strict permission controls and data validation. Pre-built corresponding data validation logic must be added during deployment to ensure input data meets filing requirements. Most cosmetics manufacturers use internal network production environments. Deployment and upgrades must support offline image loading and internal network model calls to adapt to scenarios without public network access.
Configuration Settings
This guidance is based on FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Adapts to high-frequency database connection requests from multiple production batches, avoiding connection exhaustion |
REDIS_URL | Internal Redis instance address, set connection timeout to 300 seconds | Stores high-frequency updated production batch caches, adapting to fast data reading needs per batch or line change |
STORAGE_S3_ENDPOINT | Internal object storage endpoint | Stores unstructured documents such as production records and inspection reports, adapting to offline storage requirements for internal network deployments |
FILE_TOKEN_KEY | Random string with 12-hour validity period | Controls access validity for production documents, preventing unauthorized access |
ROOT_KEY | Custom high-strength random string | Configures system administrator permissions, strictly controlling access to configuration modification permissions for production data |
Three Common Misconfigurations
- After deploying a rerank model, the model does not appear in the platform. Cause: No model cache path was added to the configuration, or the correct model file directory was not mounted during image loading.
- After specifying a model version in the configuration, actual calls still use the old version. Cause: The database connection pool cache was not updated, or front-end cache was not cleared, so the configuration did not take effect synchronously.
- After internal network deployment, associated services restart continuously. Logs show startup failure. Cause: Dependent packages for the offline image were not imported, or the storage endpoint was configured with a public network address, making it inaccessible on the internal network.
How to Verify Successful Configuration
- Run container status check commands to confirm all associated services exit without errors.
- Access the platform configuration page to verify that configured data source connection information matches internal network system parameters.
- Upload a single production document to trigger the synchronization process, confirming the document can be saved normally to the specified storage path.
- Call the model test interface to verify that the configured model can normally return validation results.
The values provided are common starting points, and should be evaluated against the reader's own samples.