What This Category of Data Looks Like
Data sources for medical beauty investment research include medical beauty product registration and filing documents, medical institution practice public notices, clinical observation reports, industry association standard guidelines, and upstream consumable supplier technical documents. Update rhythms fall into two categories: real-time updates for newly approved products and policy adjustments, and quarterly synchronization for regular industry updates. Document structures cover multi-paragraph technical descriptions, compliance clauses, and parameter tables. Fields include product name, registration certificate number, applicable skin type, treatment parameters, clinical observation data, and more. Units include professional measurement identifiers such as J/cm², treatments/treatment area.
What Constraints These Characteristics Impose on the Vector Models and Indexing Link
Medical beauty data contains a large number of professional technical parameters and structured tables, requiring vector models to accurately encode structured fields and professional terms to avoid semantic ambiguity. Data updates have two rhythms: real-time and periodic, requiring indexes to support flexible switching between incremental synchronization and full reconstruction. Individual document lengths vary widely, ranging from short compliance clauses to long clinical reports, requiring indexes to support adaptive segmentation strategies. Some content involves compliance requirements, requiring accurate recall of relevant clauses, with high requirements for recall relevance ranking.
How to Configure the Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | text-embedding-3-large or bge-large-zh-v1.5 | There are many professional terms in the medical beauty field, and this type of model has more stable semantic encoding effects for the medical beauty domain |
chunk_size | 800–1200 characters | Medical beauty documents contain long technical paragraphs and units split from tables; this range can retain semantic integrity |
index_refresh_interval | 3600 seconds | Balances real-time update requirements and system performance of index construction, avoiding frequent full synchronization triggers |
retrieval_top_k | Top 8–12 results | Medical beauty investment research queries need to cover multi-dimensional parameters; too many will introduce redundant content, too few will miss relevant information |
table_parse_enable | Enabled | Medical beauty documents contain a large number of parameter tables; enabling this preserves the vectorization and indexing effects of structured content |
similarity_threshold | 0.75–0.85 | Compliance-related content requires a high matching degree to avoid recalling irrelevant documents and ensure the accuracy of investment research content |
The parameter values provided on this page are all 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 testing on local samples prior to finalization is recommended.
Three Common Mistakes
- Phenomenon: Slow index recall speed occurs for QA split file collections created via OpenAPI. Cause: A reasonable
chunk_sizeis not configured, resulting in overly long individual segments, which increases word segmentation and encoding time during index construction. - Phenomenon: Abnormal results returned by vector encoding, with low matching accuracy for professional terms. Cause: A general-purpose embedding model is selected, and semantic encoding logic is not optimized for the medical beauty domain.
- Phenomenon: "Index construction failed" is displayed in the interface, with status code 400 returned. Cause:
table_parse_enableis not enabled, causing documents containing tables to fail to be parsed correctly, triggering a parsing timeout error.
How to Confirm the Configuration Is Correct
- Upload a medical beauty document containing technical parameter tables, check the segmented results after index construction, and confirm that table content is correctly split and encoded.
- Initiate a query targeting medical beauty product parameters, verify the relevance ranking of recalled results, and confirm that the parameter configuration meets business requirements.
- Submit a new medical beauty compliance document, verify that the incremental index completes the update within the preset update cycle.
- Call the vector encoding interface, check the returned embedding vector dimensions, and confirm that they match the selected
embedding_modelparameter.
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.