What data for this category looks like
Medical aesthetics investment research data sources include clinical treatment logs of medical aesthetics institutions, project compliance filing documents, industry technical white papers, public consumer public opinion data, and product parameter documents from consumable manufacturers. Compliance filing documents are updated quarterly. Clinical logs are archived monthly. Public opinion data is updated in real time. Product parameters are adjusted with new product launches.
Documents include structured tables, unstructured text and images, and policy PDF files. Fields cover project filing number, consumable batch number, treatment site, and compliance validity period. Units include yuan per treatment, minutes, milliliters, units, and others.
What constraints do these characteristics impose on the "context and token" link
The mixed structure of medical aesthetics data causes fluctuating token consumption. The token conversion ratio for multimodal images and text requires separate adaptation. The timeliness requirement of real-time public opinion means the context window must cover recently updated content to avoid recalling outdated information.
Splitting long documents requires retaining associations between structured fields. Otherwise, the contextual connection between project parameters and corresponding descriptions will be broken. Additionally, professional terminology and specific units in the medical aesthetics field must be accurately matched during context recall. Otherwise, invalid token usage will occur and retrieval efficiency will be reduced.
How to set configurations
| Configuration Item | Recommended Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Covers long-text contexts for medical aesthetics compliance documents and clinical cases, avoids truncating associations between critical parameters |
chunkSize | 1000–1500 characters | Adapts to the mixed structure of structured fields and text in medical aesthetics documents, avoids splitting critical information such as project numbers and charging standards |
chunkOverlap | 200–300 token | Retains field associations between adjacent segments, avoids losing cross-segment contextual information during retrieval |
recallTopK | Top 6–8 results | Balances field specificity of medical aesthetics data and token consumption, avoids recalling excessive irrelevant clinical cases |
similarityThreshold | 0.75–0.85 | Accurately matches parameters of medical aesthetics projects and policy requirements, filters low-relevance public opinion or non-filing project data |
rerankTopN | Top 3–4 results | Performs secondary ranking on recall results, reduces invalid token usage, and focuses on high-relevance compliance and clinical data |
The parameter values provided on this page are common recommended starting points for configuration setup. Actual values are affected by material format, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: Split project parameters appear in retrieval results. For example, "botulinum toxin" and "100 units" are split into different segments and cannot be associated and matched. Cause: The
chunkOverlapsetting is too small, failing to retain field association information between adjacent segments. - Phenomenon: An error indicating token consumption exceeding the preset upper limit is returned when initiating multimodal medical aesthetics case retrieval. Cause: No multimodal token conversion rules are configured, leading to abnormal token consumption calculated using default ratios. This is a common token consumption control issue in this field.
- Phenomenon: Low-relevance results are returned when entering Chinese medical aesthetics professional queries, while English queries respond normally. Cause: The
similarityThresholdis not adjusted for Chinese professional terminology in the medical aesthetics field, leading to accidental recall of low-relevance documents that occupy the context window.
How to confirm the configuration is correct
- Upload a medical aesthetics clinical case document containing structured parameters and images, check whether the segmented content retains associations between adjacent fields.
- Initiate a query containing multimodal images, check whether the token consumption log matches the preset conversion ratio.
- Adjust the
recallTopKparameter, compare the change in the number of retrieval results to confirm the configuration takes effect. - Enter a Chinese medical aesthetics professional query, check whether the returned results prioritize matching filing projects and compliance documents, and do not include irrelevant content.
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.