What the data for this category looks like
Data for this category comes from product drawings and 3D models, CAPP/CAM systems, machine tool collection terminals, tool management systems, MES, and measurement and inspection tool data. Daily documents include process routes, operation cards, tool lists, CNC programs, first-piece inspection records, patrol inspection forms, and measuring tool calibration records. These documents contain fields such as processing parameters, dimensional tolerances, and tool life. Common units are millimeters, revolutions per minute, meters per minute, and others. Data updates are triggered per work order, single-piece machining, sampling inspection, or tool replacement. Update frequency matches production rhythm.
Constraints for Deployment and Upgrade
Data sources for this category are scattered, covering design, production, quality inspection and other systems. During deployment, connection parameters for multiple types of data sources must be configured. Document types include structured process cards, unstructured CNC programs, and others. During upgrade, parsing logic for different formats must be adapted. Data updates are triggered randomly based on production nodes. During deployment, a reasonable index synchronization cycle must be configured to avoid deviations between indexes and actual production data. Errors directly affect production efficiency and workpiece quality. After upgrade, compatible configurations of the original verification link must be retained to prevent interruption of the production process.
Configuration Settings
These configurations are formulated based on FastGPT v4.17.0.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 10–20 connections | Adapt to concurrent data pulling requirements of multiple systems, avoid connection pool exhaustion, and support synchronous verification of various documents such as process and quality inspection |
MONGODB_URI | Cluster connection string containing authentication information | Store massive unstructured CNC programs and quality inspection records, support efficient full-text search |
REDIS_URL | redis://:custom password@localhost:6379/0 | Cache frequently accessed process parameters and verification results, reduce database load, and improve response speed of cross-review |
STORAGE_S3_ENDPOINT | Access endpoint of enterprise self-built object storage | Store large-volume CNC programs and 3D model files, meet storage requirements for large-capacity documents |
ROOT_KEY | Randomly generated 32-bit string | Configure the highest privilege key for system administrators, ensure access security after deployment, and avoid unauthorized modification of verification rules |
Three Common Configuration Mistakes
- After deployment, the
fastgptcontainer restarts continuously with no valid log output. The cause is that the connection parameters forREDIS_URLare not configured correctly, and the cache service fails to start normally. - After upgrading to FastGPT v4.17.0, verification of the original administrator account password fails. The cause is that the original
ROOT_KEYconfiguration is not retained during the upgrade process, and the system resets the highest privilege key. - The text content extraction function cannot read CNC program documents. The cause is that the storage permissions corresponding to
STORAGE_S3_ENDPOINTare not configured, and large-volume program files cannot be accessed.
How to Verify Successful Configuration
- Log in to the data source management page of the FastGPT admin backend, run connection tests for
MONGODB_URIandPG_URL, and confirm the test results pass. - Upload a CNC program document, verify that the file parsing function works normally, and can extract processing parameters and text content from it.
- Initiate a simulated technical document cross-review task, confirm that verification results are normally generated and stored to the specified data source.
- Check the system operation logs, confirm there are no abnormal messages such as database connection timeouts or cache service errors.
The values provided are common starting points and should be measured against the reader's own samples.