Multi-Turn Conversations and Prompts for Health Management

Data Characteristics in this Category

Health management registration and declaration documents draw from diverse data sources. These typically include clinical trial reports, user health records, medical device or software manuals, regulatory compliance documents, and risk assessment reports. Data update frequency is relatively low, primarily occurring after product iterations, regulatory updates, or large-scale clinical studies. Document structures are predominantly unstructured text, accompanied by numerous tables and charts, such as those found in "Medical Device Registration and Declaration Document Requirements and Instructions" or "Health Management Service Specifications." Fields and units involve medical indicators (e.g., blood glucose units mmol/L, blood pressure units mmHg), statistical parameters (e.g., P value), dosage units (e.g., mg), and time units (e.g., year, month). Specific regulatory codes and classifications are also common.

Constraints Imposed by These Characteristics on "Multi-Turn Conversations and Prompts"

The unstructured text nature of health management registration and declaration documents requires multi-turn dialogue systems to effectively handle lengthy, highly specialized medical terminology and regulatory provisions when understanding context. Low update frequency means initial knowledge base construction must be comprehensive, with subsequent maintenance focusing on incremental updates and version management. The large volume of tabular and graphical data presents challenges for document parsing, requiring accurate information extraction to avoid losing critical data. The specificity of medical indicators and regulatory codes necessitates precise prompt design to guide the model in identifying and interpreting these specialized fields and units. This prevents the model from confusing or misinterpreting values, such as mixing units, when generating responses.

Configuration Settings

Configuration Item	Recommended Value	Rationale for this Value
`Chunk size` (Segment Length)	`500-800 characters`	Balances long text comprehension with recall efficiency, preventing information overload in a single segment.
`Recall count` (Recall Count)	`top 8-12 items`	Registration and declaration documents are highly interconnected, requiring more context for complex questions.
`Similarity threshold` (Similarity Threshold)	`0.75`	Ensures the professionalism and accuracy of recalled content, filtering out low-relevance segments.
`Rerank result count` (Rerank Return Count)	`top 5 items`	Further optimizes recall results, focusing on the most core information and reducing the model's processing burden.
`maxContext`	`8000 tokens`	Adapts to the complexity of health management domain questions and the depth of context, supporting multi-turn conversations.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles large declaration documents and embedded chart content, preventing file parsing timeouts.

Three Common Mistakes

A 404 status code with no response body in the conversation usually indicates that the backend service failed to correctly process the request route, or the knowledge base model did not load successfully.
Refreshing the page displays "No available index model detected." This may be due to a knowledge base index build failure or an index service anomaly, causing the agent to be unable to find associated knowledge sources.
The conversation interface fails to return the original source. This typically occurs when the Reference Source field is not enabled or incorrectly configured, leading the model to only output generated content.

How to Confirm Correct Configuration

Submit complex queries containing professional terminology and regulatory numbers. Observe if the model accurately identifies and provides relevant explanations. This assesses the suitability of Similarity threshold (Similarity Threshold) and Recall count (Recall Count).
Upload a large declaration document containing tables and charts. Check if the system can fully parse it and accurately cite data from it in the conversation. This confirms if the PARSE_FILE_TIMEOUT_SECONDS configuration is sufficient.
Conduct multi-turn follow-up questions. Examine the model's performance in context understanding and coherence. This evaluates whether the maxContext parameter effectively maintains the conversation state.

The values provided are common starting points and should be measured against specific samples.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-21.