What the Data for This Category Looks Like
Data for medical aesthetic profit yield and market trends originates from daily operation ledgers of medical aesthetic institutions, as well as public quotes and consumption data from third-party medical aesthetic industry monitoring platforms. Market data updates daily. Revenue-related statistics for individual items update every 7 days. Each individual data document includes the project's unique identifier, project name, daily listed average price, daily procurement cost, daily in-store customer visits, and associated service area code. The unit for price-related fields is yuan. The unit for visit count fields is times. The area code is a 6-digit administrative division code. A single operation ledger document usually contains full monthly operation details for a single institution, with a wide range of character counts.
Constraints on Multiturn Dialogue and Prompting
Daily updated market data requires that any data query triggered during the multiturn dialogue workflow calls the latest daily dataset. Cached data older than 24 hours must not be used. Revenue statistics updated every 7 days require prompts to clearly distinguish trigger conditions for real-time market queries and periodic revenue queries. This prevents confusion over data timeliness. The multi-field document structure requires prompts to guide users to clearly specify the query project type and service area. Necessary parameters must be completed through multiturn follow-up questions if not provided. The existence of area codes requires the dialogue workflow to map user-mentioned area names to standard codes. This ensures accurate data matching. The volatility characteristic of daily data requires prompts to include objective description rules for volatility. This avoids subjective pre-judgment. The wide range of document character counts also requires the dialogue workflow to perform segmented recall for long documents. This prevents context overflow.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 characters | Medical aesthetic data has many fields. Multiturn dialogue must retain historical information such as user-specified projects and areas to avoid context overflow |
recallTopK | Top 6 entries | There are many types of medical aesthetic projects. Enough candidate results must be returned without overly disrupting dialogue logic |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Medical aesthetic operation ledger documents usually contain multiple lines of details, which take longer to parse. Sufficient processing time must be reserved |
promptTemplate | Prioritize querying daily market data based on user-specified projects and areas, then supplement corresponding periodic revenue data | Clearly distinguish the query order of the two types of data to avoid user confusion over data timeliness |
httpRequestTimeout | 60 seconds | Response delays for third-party medical aesthetic data interfaces are usually within 30 seconds. A reasonable buffer time is reserved |
maxHistoryTurns | First 3 turns | Context for medical aesthetic queries is usually concentrated in the most recent 3 project and area specification requests. Excessive turns will increase context load |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three Common Mistakes
- Symptom: Invoking the application causes the conversation details page to fail to display internal data processing logs. Cause: The
enableDetailLogparameter is not enabled, resulting in no recording of the application's internal knowledge base recall and prompt splicing process. - Symptom: Uploading a medical aesthetic operation ledger file results in an empty data processing result. Cause: The
PARSE_FILE_MAX_SIZEparameter is not adjusted to accommodate the character count of large ledger documents, or theenableOcrparameter is not enabled to process scanned ledger files. - Symptom: No conversation history records are stored in the MongoDB database. Cause: The
enableChatHistoryparameter is not enabled, or the MongoDB connection string and storage permissions are not configured correctly.
How to Verify Proper Configuration
- View the application's configuration page to confirm that relevant parameters such as
enableDetailLogandenableChatHistoryare enabled. Use test conversations to view internal logs in the details page and session records stored in MongoDB. - Upload a small medical aesthetic operation ledger file to trigger the data processing workflow, and confirm that the processing result includes expected fields and data entries.
- Initiate two rounds of dialogue: first specify a project and area to query daily market data, then query the revenue data for that project. Confirm that the dialogue context retains the previously specified parameters without requiring repeated input.
- Invoke the HTTP interface to send a knowledge base conversation request, specifying the configured project and area parameters. Confirm that the returned results include relevant content recalled from medical aesthetic data, consistent with the results from the web interface conversation.
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.