What the data for this category looks like
Medical aesthetics research reports originate primarily from brokerage research institute reports on the medical aesthetics sector, vertical industry think tank monitoring data, and public operational analysis from leading medical aesthetics institutions. Regular update cycles include quarterly deep reports, monthly track dynamics, and weekly policy and project information. Typical document structures cover overall industry overview, segmented track breakdowns such as injectable and phototherapy medical aesthetics procedures, regional market distribution, operational data from leading institutions, compliance policy explanations, and risk warnings. Fields include institution name, per-procedure service cost, monthly in-store visits, number of covered store locations, and similar items. Units are mostly yuan, visits, and locations. There is no unified standardized format, and some documents include project cases and user feedback text.
Constraints imposed by these characteristics on vector models and indexing
Medical aesthetics research reports contain a large number of medical aesthetics-specific terms, such as Thermage, Juvederm, botulinum toxin, and others. This requires vector models to have vertical domain semantic alignment capabilities; general-purpose models may fail to accurately identify association relationships between specialized terms. Document lengths vary significantly, ranging from a few pages of brief analysis to dozens of pages of deep reports. A layered chunking strategy is needed to avoid context breaks. There is also a mix of structured and unstructured data, so indexing must support hybrid retrieval of semantic and structured fields. Medical aesthetics industry policies and market dynamics are updated frequently, so indexing must support incremental synchronization to ensure data timeliness.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Adapts to the long-text structure of medical aesthetics research reports, retains complete semantics for a single scenario, and avoids context breaks after chunking |
vector_model | bge-large-zh-1.5 | This model is adapted to Chinese medical vertical domain specialized terms, and can accurately align the semantic features of terms such as medical aesthetics procedures and institutions |
multi_vector_enabled | Enabled | Supports generating multiple sets of vectors from a single set of data, adapts to the multi-chapter content structure of medical aesthetics research reports, and addresses the multi-vector configuration requirements of version 4.8.7 |
recall_top_k | Top 10 results | Covers relevant content across multiple segmented tracks in medical aesthetics research reports, balancing recall breadth and precision |
index_update_strategy | Incremental update | Synchronizes research report data incrementally by release time, adapting to the high-frequency dynamic update characteristics of medical aesthetics industry research reports |
similarity_threshold | 0.72–0.78 | Filters low-correlation recall results, adapting to the semantic similarity judgment of specialized terms in medical aesthetics research reports |
The parameter values provided on this page are common starting points for configuration setup. 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 errors
- Phenomenon: Retrieval results do not include annotated medical aesthetics project association analysis. Cause: No vectorization rules for auxiliary fields were configured, resulting in auxiliary data not being included in the index vector generation scope.
- Phenomenon: In version 4.8.7, multiple sets of vectors cannot be generated for a single research report, and retrieval results only match the opening content of the document. Cause: The
multi_vector_enabledconfiguration was not enabled, and chunking mapping was not completed according to document chapters. - Phenomenon: Retrieval results are normal during local testing but deviate significantly after server deployment. Cause: The vector model embedding dimensions used locally and on the server are inconsistent, leading to vector space alignment errors and incorrect semantic matching.
How to confirm correct configuration
- Upload a sample medical aesthetics research report, check the vector generation log to confirm that the chunking results corresponding to the
chunk_sizeparameter match the expected length. - Call the retrieval interface, input medical aesthetics project specialized terms as query words, and check whether the chapter matching degree of the returned results meets the configured recall quantity requirements.
- Compare the vector model version and embedding dimensions between the local and server environments to confirm that both parameters are consistent.
- Check the index refresh log to confirm that the incremental update task executes according to the configured
index_update_strategycycle.
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.