What the data for this category looks like
Medical beauty financial report data mainly comes from publicly disclosed quarterly and annual financial reports of chain medical beauty institutions, internal operational ledgers, and upstream supply chain settlement data. Update cadence includes fixed quarterly and annual updates for public reports, and monthly and weekly on-demand updates for internal operational data. Document structure includes revenue breakdown (light medical beauty, surgical medical beauty, consumables revenue), customer unit price, customer acquisition cost, store operation data, compliance rectification records, and other fields. Revenue-related fields use ten thousand yuan as the unit. Customer unit price and customer acquisition cost use yuan as the unit. Compliance records are count-based fields with no unit.
What constraints do these characteristics impose on vector models and indexing?
The multi-dimensional breakdown fields, mixed long-short text structure, and differentiated update cadence of medical beauty financial report data impose multiple constraints on the vector models and indexing process. Multi-dimensional revenue breakdown and associated fields require models to capture semantic associations in specific business scenarios, to avoid recalling irrelevant business data. Mixed long and short text document structures require models to adapt to text vectorization of different lengths, while indexing must support precise matching of mixed fields. Differentiated update frequencies require support for incremental index updates to avoid performance losses caused by full reconstruction.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
embedding_model | shaw/dmeta-embedding-zh or bce-embedding-base_v1 | Supports semantic understanding for Chinese medical and financial report scenarios, matches business characteristics of the specialized field |
chunk_size | 800–1200 characters | Balances semantic integrity of long medical beauty financial report texts and indexing density, avoids semantic fragmentation caused by overly short segments |
recall_top_k | Top 8–12 results | Financial report data has multiple dimensions, requires recalling enough relevant segments to support complete analysis |
index_incremental_update | Enabled | Adapts to differentiated update cadences, reduces computational overhead from full index reconstruction |
similarity_threshold | 0.72–0.85 | Distinguishes semantic similarity across specialized business segments of medical beauty financial reports, filters irrelevant recalled results |
embedding_batch_size | 16–32 | Adapts to hardware configurations with 8 cores and 64GB memory, avoids memory overflow during vector computation |
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 targeted analysis. It is recommended to test on your own samples before finalizing values.
Three common errors
- Symptom: The interface displays "No available channel" or the embedding call returns the error "Token unauthorized to use model". Cause: The access address of the self-hosted embedding model was not directly configured. Only a transit channel was used for configuration, and the correct model identifier was not matched.
- Symptom: Knowledge base search takes an excessively long time, with hardware configuration of 8 cores, 64GB memory + RTX2070. Cause:
chunk_sizeis set too long orrecall_top_kvalue is too high, increasing the computational load of vector retrieval. - Symptom: Recalled financial report segments do not match the query semantics. Cause: A general-purpose embedding model was selected, which does not adapt to the semantic characteristics of the specialized business of medical beauty financial reports.
How to confirm correct configuration
- Execute a test call of the embedding model, verify that the returned vector dimensions match the standard dimensions of the selected model, to confirm the model configuration is correct.
- Upload a single medical beauty financial report sample, check that the number of generated text segments matches the preset segment rules, to confirm the segment configuration is correct.
- Initiate a preset financial report query request, verify that the number of recalled results conforms to the preset retrieval rules, to confirm the retrieval configuration is correct.
- Trigger an incremental update task, check that only newly added financial report data is included in the index, to confirm the incremental update configuration is correct.
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.