What data looks like for this category
Data sources for this category include design history files, component and material batch records, MES systems, cleanroom environment monitoring equipment, sterilization equipment, inspection and calibration systems. Data is generated once per batch or per unit of product. Cleanroom environment monitoring data is updated on a fixed schedule. Daily documents cover production process specifications, batch production records, cleanroom zone records, sterilization batch records, inspection reports, software version records, adverse event and traceability materials. Data fields include unique identifiers, batch numbers, cleanroom temperature and humidity parameters, sterilization temperature and time, inspection results, software version numbers, and more. Units include ℃, Pa, batch numbers, dates, and similar standard units.
What constraints these characteristics impose on database and operations maintenance
Data for this category is generated per batch or per individual product unit. This requires the database to support partitioned storage by batch, to enable fast traceability of full-lifecycle data for a single product. Multi-source data integration requires unified field mapping rules to adapt to output formats from different devices and systems. High-frequency batch write demands require database connection pool configurations to reserve sufficient resources, to avoid concurrent write conflicts. The mandatory requirement for adverse event traceability means data must be immutable. Audit logging must be enabled to record all data modification operations. Additionally, data includes sterility control information related to patient safety. Strict access controls are required to restrict unauthorized access.
How to configure settings
These configuration items are adapted for FastGPT v4.17.0 database and operations scenarios:
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | mongodb://<user>:<pass>@<host>:27017/fastgpt?authSource=admin&retryWrites=true&w=majority, use version 5.0.25 or later | Adapts to batch production data writes and full-lifecycle traceability queries, fixes known security vulnerabilities in MongoDB 5.0.18 |
PG_URL | postgresql://<user>:<pass>@<host>:5432/fastgpt | Stores structured batch records, inspection reports, and similar data; supports complex relational queries |
DB_MAX_LINK | 80–120 connections | Balances single-node load and concurrent write requirements, adapts to high-frequency data submissions per production batch |
REDIS_URL | redis://<pass>@<host>:6379/0 | Caches real-time cleanroom environment monitoring data and user permission verification information, reduces database query pressure |
Three common misconfigurations
- Symptom: Batch running nodes cannot enable concurrent execution, and the console reports "concurrency connection limit exceeded". Cause: The
DB_MAX_LINKconfiguration value does not match the concurrent write requirements of production batches, and insufficient connections are reserved to handle simultaneous data reports from multiple devices. - Symptom: Database queries for sterilization batch records return status code 401, and data cannot be read. Cause: The
MONGODB_URIuses version 5.0.18, which has known security vulnerabilities that block some authorized access. - Symptom: An attempt to connect to a database prompts "unsupported database type", and production data cannot be imported. Cause: A database type not supported by FastGPT is used, and production data is not synchronized to the storage instance corresponding to
MONGODB_URIorPG_URL.
How to confirm configuration is complete
- Run a batch write test, submit simulated sterilization batch records and cleanroom environment monitoring data, check write success rate and query response time.
- View the database version log to confirm that the MongoDB instance is using version 5.0.25 or later, with no known security vulnerabilities.
- Export the trained knowledge base content, verify that the storage path matches the table space corresponding to
PG_URLorMONGODB_URI, and confirm that tenant information is correct. - View the
REDIS_URLconnection status panel, confirm that the cache service is running normally, with no timeout or connection disconnection errors.
The values provided here are common starting points, and should be evaluated against the reader's own samples.