What the data for this category looks like
Data for this category originates from the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and complaint clues. Daily processed documents include audit notices, inspection work drafts, suspicious item lists, interview records, handling decision letters, and rectification reports. Fields cover violation identification items, rectification completion status, handling basis, and other relevant items. The update frequency is monthly. Data must be synchronized and integrated across multiple systems, and aligned with the document format requirements of the audit workflow. This supports full-link summary from suspicious item screening to handling opinion formulation.
What constraints these characteristics impose on deployment and upgrade
Monthly updated batch data requires stable database connection pool and scheduled synchronization task configuration during deployment, to avoid data synchronization interruptions. Multi-source heterogeneous data sources require adaptation to different storage and interface configurations, to ensure consistency between audit data, the rule base, and complaint clues. The diversity of document formats requires the platform to support multiple types of file parsing, while ensuring data integrity during processing. This prevents missing key fields in audit ledgers due to configuration errors. In addition, the audit workflow has many time-consuming links. Reasonable configuration of task queue resources is required to prevent task timeouts due to insufficient resources.
How to configure the settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
DB_MAX_LINK | 10–15 | Adapts to multi-source data connection requirements for monthly batch synchronization, avoids connection pool exhaustion |
MONGODB_URI | Connection address of the deployed MongoDB instance (includes authentication information) | Stores unstructured document data such as audit suspicious item lists and rectification reports |
REDIS_URL | Connection address of the deployed Redis instance, with timeout set to 300 seconds | Caches frequently accessed fund supervision rule base data, reduces repeated query overhead |
STORAGE_S3_ENDPOINT | Access endpoint of the deployed object storage service | Stores batch exported audit ledgers and report files |
FILE_TOKEN_KEY | 32-character custom secret string | Generates access tokens for audit documents, ensures data security |
Three common misconfigurations
- Symptom: After upgrading to FastGPT v4.17.0, the aiproxy container frequently reports curl execution failure after startup. Cause: Object storage permissions corresponding to
STORAGE_S3_ENDPOINTare not correctly configured, preventing normal pulling of audit ledger files. - Symptom: Custom environment variables configured during deployment cannot be read in workflows. Cause: Environment variable pass-through configuration is not added to container startup parameters, preventing the platform from accessing custom parameters.
- Symptom: Monthly data synchronization tasks frequently time out and fail. Cause:
DB_MAX_LINKis set too low, causing the database connection pool to be exhausted during multi-source data synchronization, preventing successful data pulling.
How to confirm configurations are valid
- Log in to the FastGPT backend configuration page, check the connectivity test result for
MONGODB_URI, and confirm it displays normal. - Trigger a simulated monthly data synchronization task, review the task log, and confirm that multi-source data is successfully pulled and integrated.
- Add an environment variable reading node in a workflow, enter the name of the custom environment variable, and confirm it returns the preset value normally.
- Check the aiproxy container runtime log, confirm there are no continuous curl error messages, and verify the service runs stably.
The values provided are common starting points and should be evaluated against the target deployment’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.