What the data for this category looks like
This category of data mainly comes from the medical insurance intelligent monitoring system, fund supervision rule base, medical insurance settlement data, drug and consumable traceability codes, and report clues. Daily produced documents include audit notices, inspection work papers, suspicious item lists, interview records, handling decision letters, and rectification reports. Fields cover audit identifiers, designated medical institution information, violation item classification, involved expense details, handling opinions, and more. Data is updated monthly, and must cover full-process records of all current audit work and settlement-related data.
What constraints do these characteristics impose on the database and operations link
The monthly full update requirement means the database must support periodic batch data import and incremental synchronization, to avoid overload from single requests. Multi-source data fusion needs a unified data mapping intermediate layer, to reduce access costs for data in different formats. Sensitive fund data and audit process documents must be stored separately, with fine-grained access permissions configured. The fixed update rhythm requires the operations link to set up scheduled verification tasks, to ensure data consistency. This scenario focuses mainly on post-event audits, with most data being historical settlement records. Real-time synchronization is not needed, but stable performance for batch queries must be maintained.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PG_URL | postgresql://audit_user:***@db-host:5432/medicare_audit | Stores structured audit rules and designated institution filing data, supports complex conditional queries |
MONGODB_URI | mongodb://audit_docs_user:***@mongo-host:27017/audit_documents | Stores unstructured documents such as audit notices and inspection work papers, supports flexible field expansion |
DB_MAX_LINK | 20–30 | Adapts to concurrent requests for monthly batch data import, avoids exhausting database connection resources |
REDIS_URL | redis://redis-host:6379/0 | Caches calculation results of monthly audit temporary suspicious item lists, reduces repeated database query overhead |
Three Common Mistakes
- Phenomenon: Token consumption statistics from the model do not match token consumption logs in the API backend. Cause: FastGPT's token statistics include full audit data from context splicing, while some APIs only count tokens for model-generated content. The two statistical scopes are inconsistent.
- Phenomenon: The database connection module does not offer an Oracle type option. Cause: The core data source of this scenario is the standard interface of the medical insurance intelligent monitoring system, which supports PostgreSQL and MongoDB by default. No built-in Oracle driver adaptation is included.
- Phenomenon: Attempting to use a database query to return a JPG image as output results in empty or incorrectly formatted output. Cause: FastGPT's database module only supports structured data and text-based unstructured data queries. No built-in logic for parsing and returning binary file streams is included.
How to Confirm Proper Configuration
- Use the database connection test tool in FastGPT v4.17.0 to verify connectivity for
PG_URLandMONGODB_URI, and confirm there are no authentication or network errors. - Import monthly audit sample data, and check that the storage formats of structured data and unstructured documents meet expected field requirements.
- Run batch audit tasks, and observe whether the number of connections for
DB_MAX_LINKstays within the set range, with no connection exhaustion errors. - View temporary suspicious item list data cached in Redis, and confirm that cache hit and expiration logic functions normally.
The values provided are common starting points and should be measured against the reader'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.