What this category's data looks like
Medical aesthetic marketing content primarily comes from internal project introduction documents, user case comparison images, consultant communication scripts, event promotion copy, and live streaming scripts. Data update frequency fluctuates with marketing cycles. Update rates are higher during new equipment launches and quarterly promotions, while routine project introduction updates are relatively stable. Content includes short text such as social media short copy, long text such as official website project details, and image assets with metadata. Metadata fields include project category, applicable skin type, release channel, and upload time. Units include character count, image pixel dimensions, and video duration.
What constraints these characteristics impose on vector models and indexing
The multi-type data characteristics of medical aesthetic marketing content create clear constraints for vector models and indexing:
- Short text accounts for a high proportion of the dataset. Vector models adapted for short text semantic extraction are required to avoid losing core marketing information during segment processing.
- Multimodal assets account for a large proportion of the dataset. Simultaneous support for joint indexing of text and image vectors is required.
- Update rhythm is uneven. The dataset includes both daily incremental updates and batch updates during event periods, requiring flexible switching between incremental and full indexing.
- Metadata fields are rich. Recall configuration that supports filtering by metadata is required to accurately match user retrieval needs.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
EMBEDDING_MODEL | bge-small-zh-v1.5 | Adapts to the semantic extraction needs of medical aesthetic short marketing copy, while balancing computational efficiency and vector accuracy |
IMAGE_EMBEDDING_MODEL | clip-vit-base-patch32 | Supports multimodal vector generation for medical aesthetic project case images and event posters, covering cross-modal retrieval scenarios |
CHUNK_SIZE | 800-1200 characters | Balances semantic integrity of long documents such as live streaming scripts and reasonable segmentation of short copy, avoiding semantic fragmentation |
RECALL_TOP_K | Top 6-10 results | Matches the precise needs of medical aesthetic consultations, avoiding excessive redundant results that reduce decision efficiency |
INDEX_INCREMENTAL_ENABLE | Enabled | Adapts to the rhythm of batch updates during events and daily incremental updates, reducing computational overhead from full indexing |
PARSE_IMAGE_ENABLE | Enabled | Covers the indexing needs of large volumes of case images in medical aesthetic marketing content, supporting cross-modal recall |
The parameter values provided on this page are common recommended starting points for configuration. 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 mistakes
- Issue: In local deployments of version 4.9.0, no image indexing related configuration options appear when creating a knowledge base and uploading documents. Cause: The local deployment did not install the dependency components required for multimodal vector models, or did not enable the image parsing function in environment variables.
- Issue: Retrieval results only return text matching content, and do not include matching results for case images. Cause: The
PARSE_IMAGE_ENABLEconfiguration was not enabled, or corresponding vector indexes were not generated for image assets. - Issue: Retrieval similarity results for short social media marketing copy have large deviations. Cause: A vector model optimized for long text was used, and the semantic extraction logic was not adapted for short text.
How to confirm configurations are set correctly
- Upload test documents that include project copy and case images, check the knowledge base parsing log to confirm whether image vector metadata was generated.
- Initiate a cross-modal retrieval test, input a query term that includes project descriptions and visual features, and check whether the returned results include both text and image matching content.
- Upload updated event copy documents, check the indexing task list to confirm that only newly added or modified document blocks are processed, verifying that incremental indexing is active.
- Adjust the
RECALL_TOP_Kparameter and initiate a retrieval, check whether the number of returned results matches the configured value, confirming that the recall parameter 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.