What the Data for This Category Looks Like
Medical aesthetic investment research data primarily comes from the National Medical Products Administration medical device registration and filing database, public reports from medical aesthetic industry associations, and de-identified clinical diagnosis and treatment and consumer feedback documents. Data update frequency fluctuates with new product launches and regulatory policy adjustments. New corresponding documents are added within 1 to 2 weeks after product registration is completed. Document structure falls into three categories: structured product parameter tables, long-text clinical case reports, and policy interpretation documents. Most fields have clear units. For example, duration is measured in months, product unit price is measured in yuan, and registration certificate numbers are string fields with fixed formats.
What Constraints These Characteristics Impose on Model Access and Configuration
The multi-source mixed nature of medical aesthetic investment research data requires the model access link to support multi-format document parsing. It must adapt to different splitting logic for structured product parameter tables and long-text clinical cases. Frequently updated data requires configuring incremental synchronization task cycle parameters to avoid data lag. Fields with clear units need dedicated entity extraction rules. Default general extraction logic cannot accurately match medical aesthetic-specific field formats. Long-text clinical cases occupy more context windows. Adjust context carrying parameters to avoid semantic truncation. Compliance-related data requires configuring sensitive word filtering rules to block sensitive content such as unfiled products and unauthorized promotions.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8192-16384 tokens | Medical aesthetic investment research documents include long clinical cases and multi-product parameter tables. This range covers most single-batch recalled compliant documents and avoids context overflow |
chunkSize | 1000-1500 characters | Medical aesthetic product parameter tables mostly contain short structured fields, while clinical cases are long texts. This range balances semantic integrity and recall density after splitting |
embeddingBatchSize | 32 items/batch | Medical aesthetic data includes a large number of short texts such as registration certificate numbers and compliance numbers. Small batch sizes reduce the risk of parsing timeouts |
sensitiveWordFilter | Enabled, linked to medical aesthetic compliance sensitive word library | Medical aesthetic investment research involves medical devices and diagnosis and treatment behaviors. It is necessary to filter content related to unfiled products and unauthorized promotions |
ragRecallTopK | Top 6-8 entries | Medical aesthetic investment research needs to balance three types of information: product parameters, clinical effects, and policies. Excessive recall dilutes core semantics |
maxOutputTokens | 16384 tokens | Medical aesthetic investment research responses require multi-product comparison and case breakdown, requiring sufficient output space |
The parameter values provided on this page are all conventional recommendations used to determine the starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing the configuration.
Three Common Misconfigurations
- The symptom is that the model prompts that the configuration file path does not exist during startup, and loading fails. The cause is failure to place the configuration file in the specified mounting directory according to platform specifications. Local non-shared paths are mistakenly used as loading paths.
- The symptom is that the model output is truncated at 12288 tokens, with a prompt that the reply limit is exceeded. The cause is failure to configure
maxOutputTokensto a value that matches business requirements. The default configuration does not cover medical aesthetic long-response scenarios. - The symptom is that global variable modifications do not take effect in the same conversation. The cause is failure to enable session context persistence configuration. Variables only take effect during a single component call and cannot retain modified results in continuous conversations.
How to Confirm Successful Configuration
- Upload a single medical aesthetic product registration certificate document. Confirm that parsed fields include preset entries such as registration certificate number and applicable body parts. Adjust
chunkSizeto a reasonable range matching semantic splitting requirements. - Launch an investment research query involving multi-product comparison. Confirm that output length is not arbitrarily truncated. Adjust
maxOutputTokensto the value range required by the business. - Upload batch-updated medical aesthetic policy documents. Confirm that the incremental synchronization task executes per the preset cycle. Verify that the
embeddingBatchSizeconfiguration adapts to batch parsing needs. - Trigger a sensitive word filter test. Upload a document containing unauthorized promotional content. Confirm that sensitive content is blocked. Verify that the sensitive word library configuration is active.
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.