Data Characteristics
Health management regulations and SOP documents originate from medical institutions, corporate health centers, and government health departments. These documents have a low update frequency. For example, national health standards may update every few years, while internal SOPs might revise annually due to technological advancements or management requirements. Document structures are typically hierarchical, including general provisions, responsibilities, processes, risk management, and appendices. Data fields and units are highly standardized. For instance, blood pressure values use mmHg, blood glucose values use mmol/L or mg/dL, with strict distinctions between normal and abnormal ranges. Disease diagnosis codes follow International Classification of Diseases (ICD) standards. Drug dosage units use mg, g, mL, etc.
Constraints on Multi-Turn Conversations and Prompts
The low update frequency of health management regulations means knowledge bases do not require frequent full updates. However, incremental revisions need precise identification and merging to avoid version conflicts. The hierarchical document structure requires RAG recall to understand contextual relationships. This prevents recalling only fragments and losing overall regulatory logic. Standardized fields and units are critical for multi-turn conversation accuracy. The system must correctly identify and process numerical values with different units, such as converting user-input mmol/L to internally stored mg/dL. Medical terminology requires prompts to guide the model to precisely understand user intent, avoiding vague statements that could lead to misdiagnosis or misleading advice. Conversation history context management must differentiate between users asking about specific values and users inquiring about relevant regulatory clauses.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6 turns | Regulatory Q&A typically focuses on specific clauses. 6 turns cover most follow-up and clarification scenarios. |
Chunk size (Segment Length) | 800–1200 characters | Health management regulation documents have long paragraphs with multiple provisions. 800–1200 characters maintain semantic integrity. |
Recall count (Recall Count) | 5 items | Recalling 5 relevant document snippets increases coverage while controlling model input length and reducing redundancy. |
Similarity threshold (Similarity Threshold) | 0.75 | Ensures recalled document snippets are highly relevant to the user query, reducing inaccurate regulatory clause citations. |
Rerank result count (Rerank Return Count) | 3 items | After reranking, selecting the 3 most relevant items for the model improves final answer precision. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large regulatory files take longer to parse. 600 seconds prevents parsing timeouts and ensures successful file uploads. |
Common Pitfalls
- Numerical unit confusion or calculation errors in conversations occur when prompts do not explicitly require the model to validate unit consistency or perform unit conversions.
- When a user asks for detailed content of a regulatory clause, the system returns irrelevant paragraphs. This happens when the knowledge base segmentation strategy is too coarse, failing to effectively preserve the document's hierarchical structure.
- After a user uploads a health report with attachments, the system fails to parse attachment content or link attachment text with historical conversations. This is due to
UPLOAD_FILE_MAX_SIZEorPARSE_FILE_TIMEOUT_SECONDSparameters being set too low, causing file upload or parsing failures.
Validation Steps
- Ask multi-turn questions about key numerical fields in health management regulations, such as normal blood pressure and blood glucose ranges. Verify the system correctly identifies units and provides standard-compliant answers.
- Ask follow-up questions on regulatory clauses at different hierarchical levels. Check if the system's recalled document snippets maintain contextual completeness and logical coherence.
- Upload health management regulation files in various formats and sizes (e.g., PDF, DOCX). Check if files parse successfully and correctly build the knowledge base.
- Simulate a user uploading an attachment (e.g., a medical examination report) during a conversation. Verify the system identifies attachment content and incorporates it into the conversation context for Q&A.
The values provided are common starting points. Measure against your own 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.