This page applies to FastGPT v4.17.0.
What this category of data looks like
Data for this category includes documents such as gauge ledgers, calibration plans, calibration certificates, interim checks, original test records, reference material ledgers, and deviation handling. Data sources include internal enterprise metrology management systems, external calibration institution certificates, standard instruments, LIMS systems, and environmental monitoring equipment. Data updates are triggered by calibration cycles, completion of single tests, or abnormal events, with no fixed unified frequency. Document fields include unique instrument identifiers, calibration dates, validity periods, measured values, measurement uncertainties, environmental temperature and humidity records, traceability chain information, and more. Some fields must match legally required metrological units such as millimeters, newtons, and degrees Celsius.
What constraints do these characteristics impose on deployment and upgrade
Multiple scattered data sources require deployment to support adaptive configuration for heterogeneous system docking. Non-fixed update frequencies require deployment of flexible on-demand synchronization and trigger-based update mechanisms. Documents contain a large number of traceability-related associated fields and large-volume files. The storage layer must support structured queries and separate object storage. Since data errors will lead to invalid quality judgments, a data verification and backup mechanism must be configured during deployment. This category requires retaining full traceability chain data to support quality judgments. Deployment links must reserve docking interfaces with LIMS systems to avoid refactoring costs during subsequent upgrades.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:27017/fastgpt_meter?authSource=admin | Docks with the ledger and calibration record storage of the metrology management system, supports multi-document associated queries |
PG_URL | postgresql://user:pass@host:5432/lims_sync?sslmode=require | Stores structured traceability data such as calibration certificates and test records, supports complex conditional retrieval |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed data such as instrument status and calibration reminders, reduces database pressure |
STORAGE_S3_ENDPOINT | http://minio.internal:9000 | Stores large-volume calibration certificates and original test data, supports cross-node access |
DB_MAX_LINK | 20–30 | Adapts to concurrent connection counts during multi-source data synchronization, avoids database overload |
FILE_TOKEN_KEY | Random 32-bit string | Generates file access tokens, ensures access security for sensitive documents such as calibration certificates |
Three common misconfigurations
- After starting the service, a MongoDB connection failure error occurs. The symptom is console output of "Authentication failed" or "Connection refused". The cause is that
MONGODB_URIis not configured with exclusive authentication information for the metrology system docking database, or network policies restrict access to port 27017. - Configuration of a local Qwen model results in a call failure. The symptom is a "model response is empty" return in conversations. The cause is failure to adapt to the key verification rules of the local model, and incorrect use of the key filling method for general cloud models.
- The system automatically deletes test records beyond the set period. The symptom is that historical calibration certificates cannot be queried. The cause is failure to disable the default log cleanup configuration, and failure to adjust the retention policy for metrological traceability data.
How to confirm the configuration is complete
- Run the FastGPT built-in database connectivity detection tool, verify the connection status of
MONGODB_URIandPG_URL, confirm that basic fields of the gauge ledger can be queried. - Upload a standard calibration certificate file, confirm that the file can be stored normally and accessed via the storage endpoint configured by
STORAGE_S3_ENDPOINT. - Initiate a multi-source data synchronization task, simulate concurrent connection counts, confirm that the database connection count does not exceed the configuration threshold of
DB_MAX_LINK, and no overload errors occur. - Generate
FILE_TOKEN_KEYand test file access with a token, confirm that unauthorized requests cannot download stored test record documents.
The values provided are common starting points and should be measured against the reader's own samples.