Data profile for this category
This category covers production entities for vaccines, antibodies, blood products, and sterile injectables. Daily documents include cell bank or seed lot records, culture and purification records, aseptic filling records, environmental monitoring data, media fill simulation results, sterility test reports, and batch release documents. Data sources include cell bank and seed lot systems, process analysis equipment, cleanroom monitoring systems, MES, LIMS, and equipment integrity testing tools. Data update frequencies include synchronization with each production batch, real-time monitoring uploads, and updates per fixed monitoring schedules.
What constraints these characteristics impose on the deployment and upgrade phase
The multi-source, scattered nature of data in this category requires configuring cross-system data connection adaptation rules during deployment. This unifies data formats and field mappings from different sources, preventing data parsing errors. Diverse update rhythms — including real-time monitoring data, batch-level records, and fixed-cycle scheduled data — require setting up a layered synchronization scheduling mechanism during deployment. This ensures timeliness and consistency for all data types. Multiple document structures, including structured test parameters and unstructured records, require adapting multi-format document parsing rules during deployment. This ensures the audit phase can fully retrieve valid information. Data is tied to batch and production process identifiers. This requires configuring a unified identifier mapping logic during deployment, preventing cross-batch data confusion.
How to set the configurations
| Configuration Item | Recommended Value | Rationale for This Setting |
|---|---|---|
MONGODB_URI | Exclusive connection string pointing to the enterprise's joint LIMS and MES storage repository | This category needs to store structured test data and unstructured production records. MongoDB supports multi-type data storage, and the exclusive connection ensures data isolation |
REDIS_URL | Connection address pointing to the enterprise's internal cache node, with cache validity set to 1200–1800 seconds | Real-time cleanroom monitoring data requires high-frequency reading. This cache period covers the core monitoring window of a single production batch |
STORAGE_S3_ENDPOINT | Terminal endpoint pointing to the enterprise's internal compliant object storage service | Documents in this category include sterility test raw data and batch records. Internal storage ensures data compliance and access efficiency |
DB_MAX_LINK | Set to 25 concurrent connections | This category's data connection sources include 6 types of production and testing systems. This value balances concurrent access and server resource usage |
FE_DOMAIN | Configured as the exclusive domain name of the enterprise's internal quality audit system | This ensures the audit page is only accessible to internally authorized personnel, complying with production environment access specifications |
FILE_TOKEN_KEY | Generate an exclusive key following the enterprise's internal key management process | This ensures access permissions and data integrity for uploaded aseptic production documents |
Three common errors
- Phenomenon: An error related to chakra-ui/react occurs when running npm install. Cause: The Node.js version of the deployment environment does not match the dependency compatibility requirements of FastGPT v4.17.0, and dependencies are not installed using the specified version of the package manager.
- Phenomenon: A 500 error is returned by the curl test after deploying with Docker. Cause:
REDIS_URLorMONGODB_URIis not configured correctly, causing the system to fail to connect to core storage and cache services. - Phenomenon: Some cleanroom monitoring data fields are empty on the audit page. Cause: Cross-system data field mapping rules are not configured, causing fields from some source systems to fail to parse into the target format correctly.
How to confirm the configuration is complete
- Run the dependency installation command, start the service after confirming no errors, and verify that the frontend page loads normally.
- Call the data pull interface, confirm that test data pulled from systems such as LIMS and MES matches preset rules for format and field mapping.
- Upload a sterility test record document, confirm that the system can parse it normally and store it to the specified storage node.
- Check the connection status of the cache service, confirm that real-time monitoring data can be written to and read from the cache normally.
The values provided here are common starting points and should be measured against the reader's own samples.