What the Data for This Category Looks Like
Financial report data for the medical beauty category comes from three primary sources: public annual, semi-annual, and quarterly reports of listed medical beauty enterprises, operating data briefings released by industry associations, and desensitized internal operating ledgers of compliant medical beauty institutions. The update rhythm follows layered timelines: listed enterprise financial reports update on fixed quarterly, semi-annual, and annual schedules. Industry briefings update monthly. Internal operating ledgers update daily. A single document typically includes three modules: financial statement text, discussion and analysis of operating conditions, and related party transaction explanations. Total word counts vary widely. Documents contain fields such as revenue details, consumable procurement amounts, store operating costs, in-store visitor counts, and service unit prices. Most units use Renminbi yuan and visitor counts.
What Constraints Do These Characteristics Impose on Knowledge Base Retrieval and Recall?
Multi-source, heterogeneous data sources including PDF financial reports, Excel ledgers, HTML industry briefings, and more require the knowledge base to support unified parsing and vector mapping rules across document formats. Layered update rhythms—daily internal ledgers, monthly industry briefings, quarterly and annual listed financial reports—require support for incremental update trigger logic configured per data source. This avoids resource consumption from full reprocessing. Wide variation in single document word counts requires balancing semantic integrity during chunking. This prevents core business associations from being lost when long texts are split. Rich business field dimensions require the retrieval link to support filtering recall results by specific fields. This improves precise matching rates.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_CHUNK_SIZE | 800–1200 characters | Aligns with the business semantic unit length of medical beauty financial reports. Avoids splitting business associations too finely, or reducing vector precision with overly long chunks. |
PARSE_CHUNK_OVERLAP | 100–150 characters | Retains cross-chunk context for long financial report documents. Prevents semantic breaks from harming recall accuracy. |
RECALL_TOP_N | Top 6–8 results | Covers the multi-field retrieval needs of medical beauty financial reports. Reserves sufficient segments for subsequent reranking and filtering. |
SIMILARITY_THRESHOLD | 0.72–0.78 | Balances recall coverage and precision for medical beauty professional terms. Filters irrelevant business segments. |
INCREMENTAL_UPDATE_ENABLE | Enabled | Adapts to the layered update rhythm of medical beauty financial reports. Reduces resource consumption from full parsing. |
MAX_DOCUMENT_PARSE_TIME | 300–600 seconds | Aligns with the parsing time of long single medical beauty financial report documents. Prevents task interruptions from mid-run timeouts. |
Three Common Misconfigurations
- Phenomenon: AI responses include large numbers of unconnected knowledge base reference segments, with no way to directly filter irrelevant content. Cause: No reasonable value is configured for
SIMILARITY_THRESHOLD, or the number of recalled entries is set too high, leading to irrelevant business segments being included in retrieval results. - Phenomenon: Long document vector ingestion tasks fail frequently. Logs show vector dimension mismatch errors. Cause:
PARSE_CHUNK_SIZEis not adjusted to match the semantic units of medical beauty financial reports, leading to chunk lengths exceeding the maximum context window supported by the model. - Phenomenon: After a knowledge base update, partial modifications to a single document are not synced to the vector database, and only full document reuploads are supported. Cause: The
INCREMENTAL_UPDATE_ENABLEconfiguration is not enabled, or update trigger rules for document metadata are not correctly bound.
How to Verify Proper Configuration
- Upload a test segment of a medical beauty financial report, review the parsed chunk results, and confirm that chunk lengths fall within the preset
PARSE_CHUNK_SIZErange, and that adjacent chunks have overlap. - Initiate a financial report keyword search, review the returned recall results, and confirm that the number of results matches the
RECALL_TOP_Nconfiguration, and that matching aligns with business expectations. - Upload a partially modified version of an already ingested document, trigger an update task, and re-run a search to confirm that the modified content has been correctly recalled.
- Check the system monitoring dashboard, confirm that incremental update tasks trigger at the preset rhythm, and that no abnormal resource usage from full parsing occurs.
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
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.