What the data for this category looks like
Data sources include approved formulation processes, material batch information, electronic batch records from MES systems, equipment and environmental monitoring data, test results from LIMS systems, and deviation and CAPA records from quality management systems. Data updates follow the rhythm of each production batch, each key process step, and fixed monitoring schedules. Daily documents include master production files, batch production records, batch packaging records, cleaning records, test records, deviation/CAPA/change documents, and batch release review forms. Fields cover material batch numbers, process parameter values, test results, deviation descriptions, and more. Units vary by parameter type: process temperature uses degrees Celsius, material weight uses kilograms.
What constraints these characteristics impose on the database and operations domain
Multi-source heterogeneous data sources require databases to support mixed storage of structured and unstructured data. High-frequency batch- and step-wise updates require databases with high concurrent write performance to avoid data blocking. Complex document structures and compliance requirements demand database support for field validation and audit trails to prevent unauthorized data modification. Synchronization of documents and operations under GMP means database state must align tightly with production workflow status. Operational changes must not disrupt ongoing production batches. Data anomalies can prevent batch release or trigger recalls, so complete backup and recovery mechanisms are required to preserve data integrity.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
| Database Connection Address | mongodb://root:your_password@your_host:27017/fastgpt?authSource=admin | Adapts to persistent storage of multi-source business data, supports reading and writing of batch records and LIMS data |
| PostgreSQL Connection Address | postgresql://fastgpt_user:your_password@your_host:5432/fastgpt?schema=public | Stores structured deviation, CAPA and release review records, supports complex compliance queries |
| Maximum Database Connections | 20–30 | Meets high concurrent write demands during each production batch, prevents connection pool exhaustion |
| Database Connection Timeout | 600 seconds | Avoids long connection blocking, supports continuous data writes during production |
| Redis Cache Address | redis://:your_password@your_host:6379/0 | Caches real-time equipment and environmental monitoring data, reduces query pressure on the primary database |
| Database Backup Cycle | Daily 00:00 | Ensures recoverability of batch record data, complies with regulatory requirements |
Three Common Misconfigurations
- The
init root user errorlog appears when starting FastGPT, and database initialization cannot be completed. Cause: Authentication parameters forMONGODB_URIare not configured correctly, or the MongoDB service is not running properly. - After source code deployment, the database associated with the Docker-deployed instance does not sync knowledge base content. Cause: The database connection address is not filled correctly, or the initial data synchronization script is not executed.
- Connection timeout errors appear after bulk database requests are sent. Cause: A reasonable maximum database connection count is not set, and the connection pool is exhausted, preventing new requests from being established.
How to Verify Successful Configuration
- Run a database connection test command to verify that the configured connection address can establish a normal connection. The connection timeout threshold is adjusted based on business concurrent volume.
- Import a simulated batch production record, check that the corresponding field information can be correctly stored and retrieved in the database.
- View the system monitoring panel for FastGPT v4.17.0, confirm that the database connection count does not exceed the configured upper limit, and that connection status is stable.
- Trigger a manual data backup operation, confirm that the backup file is generated normally, and that the backup cycle is set in accordance with regulatory requirements.
The values provided are common starting points and should be measured against the reader's own samples.