What data looks like for this category
The data for this category comes from product digital models and GD&T specifications, process route documents, coordinate measuring machine data, on-site trial feedback, and metrology system records. Daily documents include workholding fixture requirement specifications, positioning datum schemes, workholding fixture design drawings, inspection gauge operation instructions, acceptance reports, calibration records, and change histories. Data updates are performed per project delivery, each product change, and regular calibration. Fields include positioning datum parameters, tolerance values, calibration cycles, version numbers, and others. Units involve millimeters, microns, days, and similar measurements.
Constraints imposed by these characteristics on the deployment and upgrade process
The data characteristics of this category impose multiple constraints on the deployment and upgrade workflow. Multi-source engineering data requires support for parsing multiple formats such as CAD drawings and metrology reports. Corresponding format parsing plugins must be configured during deployment. The non-fixed update rhythm requires the system to support incremental data synchronization and version management, to avoid excessive server resource usage from full updates. Fields related to precise tolerances must ensure storage and retrieval precision, to prevent data distortion that reduces the accuracy of equipment point inspection and maintenance Q&A. Daily documents have a relatively high degree of structuring, so preconfigured field extraction rules are needed to ensure accurate recall of relevant content during Q&A.
Configuration guidelines
These configurations apply to FastGPT v4.17.0:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 1000–1500 seconds | Adapts to the long connection requirements of multi-source engineering data for this category, avoids frequent reconnections that reduce document parsing efficiency |
MONGODB_URI | Use an independent replica set connection string | Stores multiple types of engineering documents and calibration records; an independent replica set ensures stable data read and write performance |
REDIS_URL | Configure a memory cache instance adapted to a 2-core 8G server | Caches frequently called fixture drawing metadata and calibration cycle data, reduces database load |
STORAGE_S3_ENDPOINT | Use an object storage endpoint that supports large file uploads | Meets storage requirements for large-volume engineering files such as CAD drawings |
FILE_TOKEN_KEY | Use an encryption key length calibrated through actual testing | Ensures access security for sensitive engineering files such as fixture drawings |
ROOT_KEY | Management key for internal deployment only | Restricts unauthorized system configuration modifications, aligns with security requirements for engineering data |
Three common configuration mistakes
- Symptom: Continuous container restarts, with database connection timeouts shown in logs. Cause: The
DB_MAX_LINKparameter was not adjusted based on the category's data volume, and insufficient long connection configuration leads to frequent reconnection failures. - Symptom: Fixture drawings cannot be recalled in Q&A after upload. Cause:
STORAGE_S3_ENDPOINTwas not configured, or storage permissions are insufficient, resulting in failed file storage and access. - Symptom: Gauge calibration data is missing from Q&A responses. Cause: Incremental synchronization rules were not configured based on project change frequency, and old data was not updated in time, resulting in empty fields.
How to verify successful configuration
- Log in to the FastGPT management backend, check the database connection status, and confirm that the connection string configured for
MONGODB_URIcan establish a normal session. - Upload a fixture drawing, verify that the file can be stored normally and its corresponding metadata can be retrieved in the knowledge base.
- Submit a test Q&A about gauge calibration cycles, confirm that the returned results include the latest calibration record data.
- Check system logs to confirm there are no frequent container restarts or connection timeout error messages.
The values provided are common starting points and should be measured against the reader's own samples.