What This Category’s Data Looks Like
Data for this category comes from product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, tool management systems, MES systems, and measuring instrument data terminals. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. The system triggers data updates with each work order, completion of each workpiece, sampling inspection link, or tool change. Fields include tool life, dimensional deviation values, processing parameters, and more. Units include millimeters, minutes, revolutions per minute, and others.
What Constraints Do These Characteristics Impose on Deployment and Upgrade
This category’s data includes large-volume CNC programs and 3D models. Update frequency triggers with work orders, workpieces, or tool changes. Deployments must adapt to large-file storage and high-concurrency read/write requirements. Multi-source heterogeneous data access requires the storage system to support multi-format file uploads. The database connection pool must match the concurrent write rhythm of work order-level operations. Real-time data caching for tools and measuring instruments requires cache service configurations adapted to high-frequency read/write scenarios. Additionally, differences in machine tool collection formats across workshops require reserving flexible data source adaptation space during deployment.
How to Configure Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
STORAGE_S3_ENDPOINT | MinIO intranet domain name:9000 | Adapts to storage requirements for large files such as CNC programs and 3D models; standard service endpoint when FastGPT includes the built-in MinIO image |
REDIS_URL | redis://:custom password@intranet IP:6379/0 | Caches patrol inspection records and tool status data to reduce high-frequency read/write pressure on the database |
DB_MAX_LINK | 20–30 concurrent connections | Matches the multi-data write rhythm generated per work order, avoiding write failures caused by exhausted connection pools |
MONGODB_URI | mongodb://username:password@intranet IP:27017/fastgpt?authSource=admin | Stores structured documents such as process routes and operation cards, adapting to multi-field query requirements |
FILE_TOKEN_KEY | Calibrated based on actual testing | Encrypts stored file access tokens to prevent unauthorized access to sensitive processing documents |
Three Common Misconfigurations
- Symptom: Processing data import times out during private deployment. Server monitoring shows consistently high CPU or memory utilization. Cause: Insufficient server resources not configured based on work order concurrency. No reserved memory space adapted to high-frequency data read/write operations.
- Symptom: Timeout or download stall occurs when pulling the fastgpt-minio image. Status code shows connection timeout. Cause: No domestic mirror source configured, or intranet environment cannot access public image repositories.
- Symptom: No image dataset add option appears in the knowledge base management page, or collection tag function cannot save normally. Cause: Not upgraded to FastGPT v4.17.0, or
STORAGE_S3_ENDPOINTand database connection parameters not configured correctly.
How to Confirm Configuration is Complete
- Access the configured
FE_DOMAINfrontend domain name. Check if the knowledge base management page includes an image dataset upload entry. - Run the
docker pull fastgpt-minio:v4.17.0command. Confirm the image pulls without timeout or stall. - Submit a knowledge base document with a custom tag. Confirm the tag can be saved and retrieved normally.
- View the database connection monitoring panel. Confirm the concurrent connection count does not exceed the configured
DB_MAX_LINKthreshold.
The values provided are common starting points and should be measured against the reader's own samples.