What the data for this category looks like
Medical aesthetics research reports come from public reports released by industry associations, operational data disclosed by compliant medical aesthetics institutions, and organized content from third-party medical aesthetics data service providers. Update cycles are primarily monthly or quarterly; some reports for specific segments will be updated temporarily alongside regulatory policy adjustments. Document structures typically include fields such as project classification (e.g., injectables, surgical procedures, skincare treatments), institution qualification level, single-service charge range, regulatory status, and update date. Field units are mostly yuan, qualification levels, and date formats. There is no unified fixed layout format, and the length of individual documents varies widely.
What constraints do these characteristics impose on workflow orchestration?
The scattered sources of medical aesthetics research reports require workflows to connect multiple knowledge base nodes to cover all channels, avoiding information gaps from single data sources. The periodic and temporary adjustment nature of update cycles requires workflows to be configured with scheduled synchronization tasks to ensure retrieved reports are the latest versions. The inconsistent document structures and wide variation in individual document lengths require configuring segmentation parameters for document parsing nodes to adapt to content of different lengths. Differences in field classification and units require adding field verification and classification filtering nodes to ensure retrieved results meet business requirements. Some reports contain sensitive regulatory status information, requiring configuration of filtering nodes to exclude non-compliant content and avoid outputs that do not meet regulatory requirements.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Large Model Temperature Coefficient | 0.3–0.7 | Medical aesthetics research report retrieval requires accurate factual content. This range balances content accuracy and appropriate flexibility, avoiding excessive divergence. |
Knowledge Base Target File Set | Filter by report publishing institution/topic tags | There are segmented tracks in medical aesthetics research reports. Filtering by tags narrows the recall scope, improves retrieval accuracy, and avoids interference from irrelevant content. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | The length of individual medical aesthetics research report documents varies widely. Some long documents require longer parsing time, and this value adapts to most scenarios. |
Batch Execution Node Cycle Interval | 1–3 seconds | When processing multiple reports in batches, this interval avoids frequent API calls triggering rate limits, ensuring stable execution of batch workflows. |
Knowledge Base Recall Count | Top 8–12 entries | Medical aesthetics research report content is professional and segmented. An appropriate recall volume covers core information and avoids excessive redundant content affecting the large model's generation quality. |
Variable Reference Binding Method | Explicitly bind target fields | Avoid parameter exceptions caused by missing variable references or incorrect binding, and adapt to workflow scenarios with multi-node linkage. |
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 individual analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Configuration Mistakes
- Phenomenon: When calling a workflow via API, the batch execution node fails to complete all cyclic tasks, but the online debug executes the full process normally. Cause: The API call does not have a cycle timeout parameter configured, and the default timeout period is shorter than the total time required to process all reports in batches.
- Phenomenon: The knowledge base search node returns irrelevant reports without limiting the specified file set scope. Cause: The file set filtering function is not enabled in the configuration, or the filtering tags do not match the topic/publishing institution of the target reports.
- Phenomenon: The referenced knowledge base ID in the workflow is empty, triggering a node error. Cause: The variable binding method is not used to automatically obtain the knowledge base ID associated with the current workflow, and manual entry is prone to configuration errors.
How to Confirm Successful Configuration
- Run a single workflow debug, and check whether the documents returned by the knowledge base search node fall within the preset file set scope.
- Review the response generated by the large model, confirm that the content is based on the facts of the retrieved research reports, and contains no subjective assumptions or irrelevant content.
- Trigger a batch execution process via API call, and check the execution logs to confirm that all cyclic tasks are completed without abnormal interruptions.
- Check the variable binding configuration, confirm that parameters such as the large model temperature coefficient and knowledge base ID are obtained through the correct method, with no missing or incorrect values.
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.