What the data for this category consists of
Data for medical aesthetic intelligent due diligence reports comes primarily from medical aesthetic institution practice license archives, project fee disclosures, doctor qualification filings, complaint and rectification records released by regulatory authorities, and public information from industry associations.
Update rhythms vary: Institutional practice qualifications are updated uniformly by local regulatory authorities every quarter. Project pricing adjusts at irregular intervals based on market changes. Doctor qualification changes are synced in real time. Complaint and rectification records are released according to regulatory notification cycles.
Documents include structured fields and unstructured content. Structured fields include: institution name, practice address, practice license number, project code, single service fee standard (unit: yuan per time), doctor practice license number (unit: identification number). Unstructured content includes doctor years of practice descriptions, complaint case details, and full text of regulatory rectification documents.
What constraints these characteristics impose on vector models and indexing
The structured fields in medical aesthetic data are numerous and have clear units. This requires vector models to accurately capture the semantic association between numerical values and their units, to avoid retrieval mismatches between fee standards and their units.
The data contains a large number of medical aesthetic professional terms and regulatory expressions. This requires embedding models to have semantic understanding capabilities for the Chinese medical field, to ensure retrieval accuracy of professional content.
Data update rhythms are uneven, with some content requiring real-time synchronization. This requires indexes to support flexible switching between incremental updates and full reindexing, to reduce resource consumption.
Document lengths vary significantly, ranging from single qualification numbers to dozens of pages of rectification reports. This requires the index's segmentation strategy to be adjustable, to avoid truncation of professional terms.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | shaw/dmeta-embedding-zh or a Chinese embedding model adapted for the medical field | This model has strong semantic understanding of Chinese professional terms, and is suitable for professional text scenarios in the medical aesthetic field |
chunk_size | 800–1200 characters | Balances semantic completeness for short qualification fields and reasonable segmentation for long rectification reports, avoiding excessive splitting or retaining redundant content |
chunk_overlap | 100–150 characters | Prevents professional terms from being truncated after segmentation, retains contextual semantic association, and improves retrieval accuracy |
retrieval_top_k | Top 8–12 results | Medical aesthetic due diligence requires covering multi-dimensional information such as institution qualifications, projects, and complaints; an appropriate number of recalls ensures retrieval comprehensiveness |
similarity_threshold | 0.72–0.78 | Distinguishes semantic similarity of professional terms, filters irrelevant documents, and avoids redundant retrieval results |
incremental_index_enabled | true | Supports incremental updates for real-time data such as regulatory notifications and institutional qualification changes, reducing resource consumption from full reindexing |
The parameter values provided on this page are common starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Knowledge base search response times out. The interface displays a "retrieval timed out" prompt or returns HTTP status code 504. Cause: Index parameters are not adjusted for medical aesthetic data vector dimensions, and hardware resources are not reasonably allocated to the vector retrieval process, resulting in underutilization of 8-core 64G memory and RTX2070 hardware capabilities.
- Phenomenon: A large number of irrelevant medical aesthetic project documents appear in retrieval results, with semantic mismatch with the target retrieval content. Cause: The
similarity_thresholdvalue is set too low, failing to filter results that do not meet semantic similarity standards, leading to recall of redundant content. - Phenomenon: Custom indexing does not take effect, and newly added medical aesthetic institution qualification documents cannot be retrieved. Cause: A large language model is mistakenly used as the embedding model to generate vectors, resulting in loss of semantic meaning of professional terms, or the
incremental_index_enabledparameter is not enabled, and incremental index synchronization is not performed.
How to confirm the configuration is properly set
- Check the vector model loading logs to confirm that the target embedding model has been successfully loaded, with no parameter errors or loading failure prompts.
- Manually upload a medical aesthetic institution qualification document, perform a retrieval test, and confirm that the returned results contain content matching the document's semantics, and the number of recalled results matches the
retrieval_top_ksetting. - Modify an uploaded medical aesthetic project document, wait for index synchronization, then perform a retrieval, and confirm that the modified content has been correctly recalled.
- Check the hardware resource monitoring panel to confirm that the CPU, memory, and GPU resources occupied by the vector retrieval process meet the configuration requirements, with no lag caused by resource bottlenecks.
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.