What the data for this category looks like
Medical aesthetics financing daily report data comes from public financing announcements of medical aesthetics institutions, local financial supervision disclosure information, and third-party medical aesthetics industry data platforms. Updates run daily, summarizing financing events in the medical aesthetics sector from the previous natural day. Each single data document includes seven fixed fields: financing entity name, financing amount, financing round, investor list, completion time, institution registration location, and core medical aesthetics business scope. Financing amount units are uniformly ten thousand yuan or hundred million yuan in RMB.
What constraints these characteristics impose on multi-turn dialogue and prompt engineering
The multi-source data setup for medical aesthetics financing daily reports requires frequent switching between field mappings with different disclosure standards during dialogue. For example, some announcements only disclose total financing amounts, not per-round amounts. The daily update requirement means dialogue contexts must retain the latest date anchor to avoid referencing expired events. The diversity of fixed fields requires prompts to clearly specify extraction rules for each field type, preventing the model from mixing up associations between financing rounds and business scopes. Additionally, demand for cross-entity information comparison means multi-turn dialogue context windows must carry more context for cross-item comparison, and invalid information must be excluded.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
maxContext | 8000–12000 token | Medical aesthetics financing daily reports require context for multi-item comparison. This range can cover the field information of 3 to 5 complete financing events, avoiding context truncation |
relevanceThreshold | 0.72–0.80 | For the semantic matching needs of structured financing data, this range can filter low-relevance non-medical aesthetics financing events while retaining associated information of cross entities in the same round |
recallCount | Top 10 entries | Meets the basic data volume for multi-item comparison, avoiding insufficient comparison dimensions caused by too few recalled entries |
rerankTopN | Top 5 entries | Focuses on highly relevant financing events, reducing interference from redundant information in multi-turn dialogues |
knowledgeBaseRefreshInterval | 86400 seconds | Matches the daily update rhythm of medical aesthetics financing daily reports, ensuring that the knowledge base content called by the dialogue is synchronized with that day’s events |
promptTemplate | Fixed field extraction + context comparison rules | Clearly specifies the extraction logic for each type of field, preventing the model from generating content for non-specified fields and adapting to the question and answer needs of structured data |
The parameter values provided on this page are conventional recommendations used to set the starting point for configuration. Actual values vary based on material form, data volume, and business rules. Address each specific case individually, and test on your own samples before finalizing values.
Three common errors
- Phenomenon: Non-medical aesthetics financing events appear in multi-turn dialogues. Cause: A text understanding model adapted for structured financial data was not selected. General-purpose models lack sufficient accuracy when identifying field semantics for niche sectors.
- Phenomenon: Dialogue-returned financing data does not match the date of the daily report. Cause: Automatic knowledge base refresh rules were not configured, causing the knowledge base content called by the dialogue to lag behind that day’s updated financing events.
- Phenomenon: API calls for dialogue return results missing specified fields. Cause: The
datasetIdparameter associated with the knowledge base was not included in the API request, so the dialogue cannot recall the corresponding medical aesthetics financing daily report data.
How to confirm the configuration is properly set
- Initiate a query for a single financing event, verify that the fields returned by the dialogue fully match the fixed fields from the data source, and adjust the prompt until all specified fields are extracted correctly.
- Initiate a comparative query for two financing events, verify that the dialogue correctly distinguishes between the financing amounts and rounds of different entities, and adjust the values of corresponding configuration items until the comparison logic is clear.
- Call the API to send a dialogue request, check that the request body includes all required parameters associated with the knowledge base, and verify that the returned results contain financing data from the corresponding knowledge base.
- Wait for a full update cycle, initiate a query for the day’s latest financing event, verify that the dialogue recalls the latest data, and adjust knowledge base refresh-related configuration items until the refresh logic works as expected.
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.