What data for this category looks like
This category’s data comes from registered process documents, raw material batch ledgers, DCS production control systems, weighing systems, LIMS laboratory information management systems, chromatography data systems, and QMS quality management systems. Data is updated per production batch and each process step, covering batch production records, material balance reports, reaction and purification process logs, cleaning validation records, impurity profile analysis reports, deviation and change application documents. Data fields include batch identifiers, process parameters, material usage, yield, impurity content, solvent residue values, and other related fields, linking full-chain records for multi-step chemical synthesis or fermentation workflows.
Constraints imposed on deployment and upgrade by these characteristics
Multi-source heterogeneous data sources require adaptive configuration for multi-system docking during deployment, to avoid data synchronization interruptions. High-frequency updates per batch and step require storage and index components to support low-latency writes, preventing data backlogs. Complex multi-step process documents and multi-field structures require retrieval indexes to match the field characteristics of this specific category, to avoid insufficient retrieval precision. Compliance-related records require full traceability across the entire process. Deployment must include complete log retention rules, to ensure audit processes can be traced. For records of multi-step chemical processes, index structures that support nested fields must be configured, to avoid missing information from linked steps during retrieval.
How to set configurations
The following configurations are adapted for FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
MONGODB_URI | Connect to a dedicated replica set address synced with LIMS and chromatography data systems, configure read-write separation | This category’s data requires integration with multiple production and quality systems. A dedicated replica set ensures stability for read-write concurrency, and read-write separation adapts to high-frequency write requirements |
REDIS_URL | 4 GB memory dedicated instance, set timeout to 300 seconds | Caching and session management for high-frequency batch data require sufficient memory. The 300-second timeout matches the average processing duration for single-batch audits |
STORAGE_S3_ENDPOINT | Bind to a compliant storage bucket, enable version control | Compliance records require multiple versions to be retained. Version control supports traceability for audit processes |
DB_MAX_LINK | 200–300 | This value adapts to the average daily batch processing volume of this category, matching concurrent connection needs for multi-system synchronization and retrieval requests |
FILE_TOKEN_KEY | Use the same encryption key as the LIMS system | Ensures consistent document permission across system synchronization, preventing unauthorized access to compliance records |
ROOT_KEY | Configure as a key only known by internal operations staff | Restricts access to the system’s highest permissions, aligning with compliance management requirements for pharmaceutical manufacturing |
Three common configuration mistakes
- Issue: A
text index required for $text queryerror is returned when executing a knowledge base query. Cause: The full-text index for MongoDB was not rebuilt after upgrading FastGPT, and correct index rules were not configured for this category’s multi-field documents. - Issue: Unable to connect to an RDS database when deploying on Alibaba Cloud, with a connection timeout prompt. Cause: The public IP of the FastGPT deployment node was not added to the RDS whitelist, and the correct database connection port was not configured.
- Issue: Data truncation occurs when batch importing batch records. Cause: The database connection pool size was not configured sufficiently. High-frequency writes exhausted connections, resulting in incomplete data synchronization.
How to confirm configuration is correct
- Execute a retrieval test for a complete single-batch record, verify that returned results include detailed fields such as impurity profiles and process parameters, to confirm the index configuration is active.
- Check the database connection monitoring dashboard, confirm that real-time connection count does not exceed the
DB_MAX_LINKconfiguration value, with no connection overflow issues. - Test cross-system document synchronization, verify that impurity profile reports from the LIMS system can be properly imported and linked to the corresponding production batch, to confirm storage and synchronization configurations are working correctly.
- Check the system log files, confirm there are no timeout errors caused by high-frequency writes, to verify that the
REDIS_URLtimeout configuration matches business requirements.
The values provided are common starting points and should be measured against the reader's own samples.