What this category of data looks like
This category of data primarily comes from health supervision information systems, medical institution electronic registration systems, complaint and reporting platforms, and law enforcement inspection records. The update frequency is monthly. Commonly involved documents include supervision inspection forms, medical institution practice licenses, rectification notices, supervision opinions, and penalty case files. Data fields include unified social credit codes of institutions, practice license scope, inspection items, rectification requirements, penalty categories, and more. Most document structures combine fixed forms and standardized official documents.
Constraints during deployment and upgrade
The monthly update rhythm of this category of data requires configuring scheduled synchronization rules during deployment. This avoids frequent data pulling that occupies system resources. Data access from multiple sources needs adaptation to different interface formats and permission rules. This ensures stable connectivity with data sources such as health supervision information systems and medical institution electronic registration systems. The mixed structure of standardized official documents and fixed forms requires adjusting structured parsing adaptation rules during upgrades. This covers field extraction logic for different document types. It also requires ensuring the accuracy of data associations, to avoid matching deviations between institution information from different sources.
How to set configurations
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
MONGODB_URI | mongodb://username:password@host:port/database?authSource=admin | Stores structured business data connected to health supervision information systems, adapts to batch storage needs for multi-institution data |
PG_URL | postgresql://user:pass@host:5432/health_check?sslmode=require | Stores process data for law enforcement inspection records and rectification tracking, supports complex queries to associate institution and personnel information |
REDIS_URL | redis://:password@host:6379/0 | Caches frequently accessed institution qualification information, reduces latency from multi-data-source synchronization |
DB_MAX_LINK | 20–30 | Limits the number of concurrent database sessions, adapts to concurrent needs of monthly batch synchronization, avoids resource exhaustion |
STORAGE_S3_ENDPOINT | Configured per intranet storage node | Stores unstructured documents such as supervision inspection forms and penalty case files, ensures controllable file access permissions in intranet environments |
ROOT_KEY | Generate a random string per intranet deployment security policy | Configures system administrator access permissions, ensures security of intranet deployments |
Three Common Misconfigurations
- Symptom: Intranet deployment fails to connect to the specified model service, returning connection timeout or permission errors. Cause: Intranet proxy rules are not correctly configured, or public network access restrictions are not disabled. This prevents FastGPT from connecting to locally deployed model services.
- Symptom: Structurally extracted fields from supervision inspection forms and penalty case files are missing or mismatched. Cause: Exclusive parsing rules are not configured for the standardized document format of this category. The default parsing logic cannot adapt to the field layout of fixed forms.
- Symptom: In advanced orchestration workflows, the AI dialogue step following the judgment node cannot access the initial inspection request content submitted by the user. Cause: Context variables of the initial request are not retained in the orchestration chain. This causes subsequent AI nodes to fail to read the original question parameters.
How to Verify Successful Configuration
- Run a database connectivity verification script to validate the connection status of
MONGODB_URIandPG_URL, confirming that business data sources can be read normally. - Manually trigger a data synchronization task, check the update records of the cache service, confirming that the caching function configured via
REDIS_URLworks properly. - Upload a standardized document sample of this category, run a structured parsing test, confirming that the field extraction logic matches the fixed format of the document.
- Call the configured model service interface, verifying that the intranet-deployed model can be accessed normally by FastGPT, with no connection or permission abnormalities.
The values provided are common starting points, and should be evaluated against samples from the relevant deployment context.
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.