What this category of data looks like
Medical beauty intelligent due diligence reports are primarily used by financial institutions for compliance reviews of entities related to medical beauty consumer credit and medical beauty insurance. Data sources include business licenses of medical beauty institutions, public records of medical beauty project filings, information on practicing physicians’ qualifications, public regulatory data from local health commissions, and public information from third-party medical beauty compliance platforms. Data update frequencies vary: regulatory data is synchronized monthly, qualification information voluntarily reported by institutions is updated in real time, and user review data is pulled every hour. The document structure includes structured fields and unstructured attachments. Structured fields cover the unified social credit code of the institution, physician practice license number, project filing number, and compliance status identifier. Attachments include PDF versions of practice licenses, images of physician qualification certificates, and similar materials.
Constraints on HTTP interfaces and external systems
The data sources for medical beauty intelligent due diligence reports include strictly formatted structured qualification fields, multiple types of compliance attachments, and significant differences in update frequencies across different data sources. This requires HTTP interfaces to support format validation for structured fields to prevent invalid data from entering the system. Interfaces must support upload and pull operations for multiple attachment types such as PDF and JPG. Differentiated scheduled scheduling parameters must be configured for data sources with different update frequencies. Additionally, since medical compliance data is involved, interface authentication must be bound to granular permissions to prevent unauthorized access. When connecting to third-party regulatory data sources, field naming differences across platforms must be accommodated, and field mapping rules must be configured.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 1024 MB | Medical beauty due diligence attachments include practice license PDFs and scanned physician qualification documents, which typically have large individual file sizes, so the upload limit must be relaxed |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Parsing large PDF attachments takes a long time, so extending the timeout threshold prevents task interruptions |
API_PERMISSION_SCOPE | ["medical_compliance", "public_data"] | Medical beauty due diligence only requires access to medical qualification and public regulatory data sources, limiting the permission scope improves security |
SCHEDULE_INTERVAL | 3600 seconds | User review data has high real-time requirements, while institutional qualification data can be synchronized hourly, adapting to the update rhythms of different data sources |
FIELD_VALIDATION_RULE | Validate according to medical beauty qualification field formats | Structured fields such as the unified social credit code must conform to the 18-digit rule to prevent invalid data from being integrated |
REQUEST_RETRY_TIMES | 2 times | Third-party regulatory data sources have unstable responses, configuring a limited number of retries ensures the success rate of interface calls |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material formats, data volumes, and business rules. Specific issues require individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Symptom: After calling the file upload interface, the agent backend task freezes, and the interface returns a 504 timeout status code. Cause: In FastGPT 4.9.7, the
UPLOAD_FILE_MAX_SIZEconfiguration was not adjusted to accommodate the size of medical beauty attachments, and uploading large files caused backend resource exhaustion. - Symptom: The Playwright MCP service cannot be resolved, and accessing the same network segment address using local development tools works normally. Cause: The server address of the medical beauty data source was not added to the HTTP interface cross-origin whitelist, resulting in cross-origin requests being blocked.
- Symptom: After deploying with docker-compose, workflow and knowledge base configurations are lost, but the interface can be called normally. Cause: No persistent storage volume was mounted, and configuration files were reset after the container restarted; only the basic services dependent on the interface were unaffected.
How to confirm configurations are complete
- Initiate an interface request for qualification fields, verify that the returned structured field formats meet the requirements of medical beauty due diligence, and confirm that the field mapping rules are in effect.
- Upload a PDF attachment of a medical beauty institution’s practice license, check that the interface receives and parses the file normally without abnormal errors.
- View the scheduled scheduling logs, confirm that the pull intervals for different data sources match the configured
SCHEDULE_INTERVALparameter. - Test cross-origin requests: initiate an interface call from a non-whitelist domain to confirm the request is blocked, and initiate a call from a whitelist domain to confirm normal functionality.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.