Multi-turn Conversation and Prompt Engineering for Medical Aesthetics Investment Research Knowledge Base Construction

Medical aesthetics investment research data primarily comes from practicing registration documents of medical aesthetics institutions, National

What the data for this category looks like

Medical aesthetics investment research data primarily comes from practicing registration documents of medical aesthetics institutions, National Medical Products Administration registration approval materials for medical aesthetics products, clinical standard documents released by industry associations, public pricing announcements for medical aesthetics procedures, and consumer public opinion feedback. Data updates have no fixed cycle. Newly approved medical aesthetics products, policy adjustments, and institutional compliance changes all trigger updates. Documents include structured fields such as product registration certificate numbers, applicable body parts, and charging units, semi-structured clinical case reports, and unstructured industry analysis articles. Some documents include compliance labels and image attachments.

What constraints these data characteristics impose on multi-turn conversation and prompt engineering

The characteristics of medical aesthetics investment research data impose multiple constraints on multi-turn conversation and prompt configuration. Structured registration documents and pricing data require conversations to accurately match fields and units. Prompts must clearly specify retrieval scope and format requirements. Unstructured clinical reports and public opinion content need longer context windows to associate cross-document information across multiple rounds of questions. Frequently updated data requires regular refresh mechanisms to avoid returning outdated compliance information. Documents with image attachments need to adapt to multimodal retrieval prompt logic to ensure content associated with images can be properly retrieved.

How to set configurations

Configuration ItemRecommended ValueRationale
maxConversationContext8000–12000 charactersAdapts to context association needs for multiple rounds of clinical cases and policy changes, avoids losing key information from historical questions
rag_recall_top_nTop 8–12 resultsCovers multiple document types including structured registration data and unstructured clinical reports, reduces the chance of missing key investment research information
rag_similarity_threshold0.75–0.85Filters low-relevance public opinion content, retains retrieval results that highly match medical aesthetics compliance and product parameters
parse_file_timeout300 secondsSupports parsing long-format clinical case reports, avoids file upload failures caused by parsing timeouts
upload_file_max_size100 MBAdapts to upload requirements for medical aesthetics registration documents and large clinical case documents
interrupt_on_new_requestEnabledPrevents queue blocking during consecutive investment research requests, ensures fast response to new questions

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. Each specific case requires individual analysis. Testing on local samples is recommended before finalizing settings.

Three Common Mistakes

  • Phenomenon: A 503 error is returned when uploading medical aesthetics registration documents, while uploading normal knowledge base files proceeds normally. Cause: upload_file_max_size is not configured to adapt to the size of large registration files, leading to service overload during large file uploads.
  • Phenomenon: When using a strict question-and-answer template, the system prompts that no answer is found after retrieving documents containing associated image addresses in the knowledge base. Switching to a general template allows normal responses. Cause: The strict question-and-answer template does not include adapted logic for multimodal content. It only supports precise matching of text fields and cannot process associated documents corresponding to image addresses.
  • Phenomenon: When multiple medical aesthetics investment research questions are sent consecutively, subsequent requests wait for previous requests to complete before responding. Cause: The interrupt_on_new_request configuration is not enabled. Old conversation requests are not interrupted, leading to request queue blocking.

How to confirm configurations are properly set

  • Upload a single medical aesthetics registration file of no less than 50 MB. Check upload progress and return status to confirm the upload_file_max_size configuration covers the file size requirement.
  • Initiate a conversation with multiple rounds of clinical case queries and compliance policy retrievals. Check if the context fully associates with historical questions to confirm the maxConversationContext configuration is reasonable.
  • Send two different medical aesthetics investment research questions simultaneously. Check if the later request responds quickly to confirm the interrupt_on_new_request configuration is active.
  • Switch to the strict question-and-answer template, upload a knowledge base document containing an image address, retrieve the corresponding content, and check if a normal answer is returned to confirm the template is configured to adapt to multimodal associated documents.

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.