What the data for this category looks like
Medical aesthetic intelligent due diligence data comes from the health department’s medical institution practice qualification database, the drug supervision department’s medical aesthetic consumables registration and filing system, and user feedback records from third-party medical aesthetic service platforms. Update rhythms vary: institutional practice qualifications are updated quarterly, consumable registration certificates are updated with drug supervision spot checks, and user feedback is synced in real time. The document structure is divided into five modules: basic institutional information, practicing physician qualifications, medical aesthetic project compliance filing, consumable traceability information, and complaint handling records. Fields include: institution name, practice license number, physician practice certificate code, consumable registration certificate number, complaint time and content. There are no unified units, and some fields are fixed-format strings.
What constraints do these characteristics impose on the "tool calling and plugins" link
Multi-source data calls require connecting multiple heterogeneous APIs, and differences in authentication methods and return formats of different interfaces must be handled. Update rhythms of different data sources are inconsistent, so latest data must be filtered by timestamp during tool calling to avoid returning expired compliance information. Medical aesthetic due diligence data contains a large number of qualification number fields with fixed formats. Tool calling must verify field formats in advance, otherwise the API will return invalid results. A single due diligence report involves a large number of fields, so tool calling must accurately filter valid fields to avoid redundant data interfering with subsequent processing.
Configuration settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
http_request_timeout | 120 seconds | Medical aesthetic due diligence requires connecting cross-departmental APIs such as health and drug supervision departments. Single requests take a long time, and 120 seconds covers the normal response cycle of most interfaces |
plugin_tool_max_retry | 2 times | Compliance data sources may have temporary fluctuations. Retrying 2 times reduces the probability of single request failure and avoids interrupting the due diligence process |
parse_field_validate_switch | Enabled | Medical aesthetic data contains fixed-format fields such as practice certificate numbers and consumable registration certificate numbers. Enabling verification can intercept non-standard request parameters in advance |
multi_source_api_merge_strategy | Merge by update time in descending order | Update rhythms of different data sources differ. Sorting by update time prioritizes returning the latest compliance information |
response_field_whitelist | Institution name, practice license number, consumable registration certificate number, complaint record | Only retain fields necessary for due diligence to reduce data transmission volume and avoid redundant information interfering with subsequent report generation |
plugin_call_trigger_mode | Keyword trigger | Trigger keywords for medical aesthetic due diligence scenarios are clear, such as "institution qualification query" and "consumable filing verification", which can accurately trigger tool calling |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: When calling ComfyUI to generate medical aesthetic project renderings via tool calling, a
422 Unprocessable Entityerror is returned after sequential execution, and the generated image does not match the specified consumables. Cause: The consumable registration certificate number was not passed as a required parameter, causing the generation logic to fail to associate correct compliance consumable information. - Phenomenon: When calling an HTTP request containing a scanned copy of a medical aesthetic institution's business license, a
400 Bad Requesterror is returned. Cause: TheContent-Typeof the request header was not correctly set tomultipart/form-data, so file-format upload parameters cannot be recognized. - Phenomenon: After sending a due diligence request, the model does not automatically trigger tool calling and directly outputs a general answer. Cause:
plugin_call_trigger_modewas not configured as keyword trigger, so the model cannot recognize tool calling requirements in due diligence scenarios.
How to confirm the configuration is complete
- Send a test request containing "medical aesthetic institution qualification query" to check whether the corresponding API call is automatically triggered on the interface, and whether the log shows that the request has been sent.
- Submit test data containing an incorrectly formatted practice certificate number to check whether a field verification failure prompt is returned.
- Call the multi-source API merging function to check whether the returned results are sorted by update time in descending order, with the latest data displayed first.
- Upload a test file containing medical aesthetic consumable information to check whether the tool correctly extracts and verifies the consumable registration certificate number field.
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.