This database and operations configuration applies to FastGPT v4.17.0.
What data for this category looks like
Data for this category covers multiple document types: tooling requirement documents, positioning benchmark schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, change histories, and more. Data sources include product digital models and GD&T, process routes, coordinate measuring machine data, on-site trial feedback, and metrology systems. Updates are triggered by project progress, each product change, and regular calibration activities. Fields include tooling number, calibration date, coordinate measuring machine measurement values, change version number, and others. Measurement values use millimeters as the unit, and dates follow the standard Gregorian calendar format.
What constraints these characteristics impose on the database and operations workflow
Multi-source heterogeneous data types require the database to support both unstructured document storage and structured data queries. Irregular updates tied to projects and changes require the database to support flexible version management and incremental synchronization mechanisms. Diverse document structures and field differences require the operations workflow to have dynamic field adaptation capabilities. Data is associated with processing and inspection workflows, and errors can lead to batch dimensional deviations or misjudgments. A complete data backup and verification mechanism is therefore required.
How to set the configuration
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://user:pass@host:27017/fixture_db?authSource=admin | Stores multiple types of unstructured and semi-structured documents such as tooling requirement documents and fixture drawings |
PG_URL | postgresql://user:pass@host:5432/metric_db | Stores structured metrology and process data such as coordinate measuring machine measurement values and calibration records |
DB_MAX_LINK | 10–15 | Adapts to concurrent access demands from multiple departments accessing ledgers and calibration data simultaneously, avoiding database connection exhaustion |
REDIS_URL | redis://:pass@host:6379/0 | Caches frequently accessed gauge parameters and positioning benchmark schemes to reduce database load |
| Data backup trigger rules | Per project completion and monthly regular intervals | Adapts to the update rhythm based on projects and regular calibration activities, ensuring data security |
| Field verification switch | Enabled | Verifies the format of fields such as tooling numbers and measurement values to prevent invalid data from being stored in the database |
Three common misconfigurations
- A connection timeout error occurs after configuring
MONGODB_URI, and the log shows authentication failure. The cause is failing to specify the authentication source of the tooling ledger database as admin, or failing to correctly fill in the username and password in the connection string. - A 403 status code is returned when attempting to create a dataset via API call. The cause is failing to configure access permissions for the corresponding API key in the FastGPT backend, or failing to carry correct authentication parameters in the request header.
- Fields are empty after importing coordinate measuring machine data. The cause is failing to match the mapping relationship between data fields and the ledger template, resulting in structured metrology data failing to be written to the database correctly.
How to confirm the configuration is complete
- Execute the built-in FastGPT database connectivity test tool, input the configured
MONGODB_URIandPG_URL, and verify that a connection successful prompt is returned. - Upload a tooling calibration record document, check for the generation of a corresponding structured entry in the database, and confirm that fields match the uploaded content.
- Initiate a ledger summary request, and confirm that the returned report includes summary information of associated documents such as tooling requirement documents and coordinate measuring machine data.
- Review system logs to confirm the absence of error messages such as database connection exhaustion or cache breakdown.
The values provided here are common starting points and should be evaluated against the reader's own samples.