What the data for this category looks like
Data for this category originates primarily from engineering outputs of tooling departments at automotive, aerospace, and mechanical assembly enterprises, as well as dedicated tooling suppliers. It includes documents such as tooling requirement documents, positioning datum schemes, fixture drawings, gauge operation manuals, acceptance reports, calibration records, and change histories. Data sources cover product digital models and GD&T specifications, process routes, coordinate measuring machine (CMM) inspection data, on-site trial feedback, and metrology systems. Data updates follow no fixed cycle. They are triggered by individual project progress, product changes, and regular calibrations. A single project’s data includes multiple types of heterogeneous files and structured records.
What constraints these characteristics impose on the deployment and upgrade phase
Most data for this category consists of engineering heterogeneous files and structured records. Updates depend on project milestones and change events. During deployment, systems must support unified access to multi-source data and permission isolation. Because the data includes compliance documents such as calibration records and acceptance reports, teams must configure fine-grained access control. Files such as CMM inspection data and fixture drawings have large file sizes. Deployments must use high-capacity storage nodes. During upgrades, systems must maintain compatibility with change histories and calibration records from historical projects to avoid data version conflicts. This configuration applies to FastGPT v4.17.0.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
DB_MAX_LINK | 150–250 connections | Adapts to concurrent access demands for multiple types of data such as tooling documents and calibration records across parallel projects |
STORAGE_S3_ENDPOINT | Configured according to the enterprise’s own metrology system storage nodes | Used to store large engineering files such as CMM data and fixture drawings |
REDIS_URL | Deploy an independent cache node | Ensures fast querying of tooling change histories and real-time calibration status |
FILE_TOKEN_KEY | A key aligned with the enterprise identity system | Controls access permissions for sensitive documents such as tooling requirement documents and acceptance reports |
ROOT_KEY | Divide permission scopes by project group | Isolates tooling data and audit records from different suppliers |
PG_URL | Configured according to the enterprise’s structured data cluster | Stores structured records such as tooling requirement documents and positioning datum schemes |
Three common mistakes
- Issue: Application access returns a 401 unauthorized error, and tooling acceptance reports fail to load. Cause:
FILE_TOKEN_KEYis not configured correctly, and the enterprise identity verification system is not linked. - Issue: When classifying multiple knowledge bases, matching only uses the current query, and historical change history context cannot be associated. This leads to biased classification results. Cause: Context association configuration is not enabled, and historical calibration records are not loaded as a classification basis.
- Issue: Connection timeout occurs when uploading CMM data in bulk. Cause:
DB_MAX_LINKis set too low, exhausting database connections during concurrent access.
How to confirm the configuration is complete
- Run a database connection test to verify that nodes configured via
MONGODB_URIandPG_URLcan be accessed normally. - Upload a fixture drawing file to verify that the storage node configured via
STORAGE_S3_ENDPOINTcan store and read files normally. - Create a knowledge base that includes calibration records and change histories to verify that the context association function can load historical documents normally.
- Configure the
ROOT_KEYpermission scope to verify that unauthorized roles cannot access tooling data for the corresponding project.
The values provided are common starting points and should be measured against the relevant organization’s own samples.