What the data for this category looks like
Data sources for this category include medical insurance intelligent monitoring systems, fund supervision rule bases, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. The update frequency is monthly. Daily processed documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decision letters, and rectification reports. Core fields include violation suspicious item identifiers, expense amounts, inspection process nodes, and other related items. Core processing steps include filtering suspicious items from settlement data, retrieving medical records, prescriptions and charging records, identifying violation expenses, and forming handling opinions.
What constraints these characteristics impose on deployment and upgrades
Monthly updated data sources require a stable periodic synchronization link during deployment, to ensure timely retrieval of rule bases and settlement data. Processing needs for multiple types of structured and unstructured documents require storage configurations that support reading, writing, and indexing of multi-format files. The high time cost of core processing steps requires reserving sufficient computing resources during deployment to support batch data operations. A caching mechanism must also be configured to reduce repeated calls. In addition, the significant impact of incorrect violation identification requires retaining a rollback path during upgrades, to avoid configuration changes disrupting audit processes.
How to configure the settings
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 20–30 | Adapts to concurrent read requirements of monthly batch audit data, prevents connection pool exhaustion |
REDIS_URL | Local cache instance or dedicated cloud cache service | Stores audit suspicious item cache and session data, reduces repeated database query pressure |
STORAGE_S3_ENDPOINT | Compliant cloud storage service node | Adapts to storage and retrieval of large files such as drug and consumable traceability codes and scanned medical records |
MONGODB_URI | Independent high-availability database instance | Stores unstructured audit documents and complaint clue data, ensures read and write performance |
PG_URL | Enterprise-level relational database service | Stores structured audit process data, supports complex conditional queries and audits |
FILE_TOKEN_KEY | Randomly generated 32-bit string | Ensures security of file access, prevents unauthorized retrieval of audit documents |
Three common configuration errors
- A several-second delay appears when dragging nodes on the workflow canvas. The root cause is that
REDIS_URLis not configured to enable caching, leading to frequent reading and writing of the database for canvas state and slowed interface response. - The audit suspicious items retrieved by the knowledge base do not match the current batch. The root cause is that the data synchronization task is not configured with a monthly update frequency, resulting in expired stored settlement data.
- Docker deployment fails to start. The root cause is that a compatible Linux operating system image is not selected, causing the dependent runtime environment to fail to load properly.
How to verify a successful configuration
- Run the database connection test script to confirm whether the connection pool configured via
DB_MAX_LINKsupports batch data reading. - Trigger the monthly data synchronization task to check whether
STORAGE_S3_ENDPOINTcan normally store audit documents and drug and consumable traceability code files. - Launch the workflow canvas, drag multiple audit process nodes, and confirm that the interface response delay meets daily usage requirements.
- View the cache monitoring panel to confirm that the cache service corresponding to
REDIS_URLis normally storing session and audit suspicious item data.
The values provided here are common starting points, and should be evaluated against the user’s own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.