Data Characteristics
Cardiovascular product data primarily originates from clinical trial reports, drug inserts, medical device registration certificates, academic papers, and specialized medical databases (e.g., PubMed, Medline). Data updates are relatively stable, triggered mainly by new drug or device approvals, clinical guideline updates, and adverse event reports. Document structures vary. Drug inserts typically include standard sections such as indications, contraindications, dosage and administration, and adverse reactions. Clinical trial reports detail study design, subject characteristics, primary endpoints, and secondary endpoints. Data fields include drug name, active ingredient, dosage form, specifications, manufacturer, target population, mechanism of action, clinical efficacy data, and adverse event rates. Common units include milligrams (mg), micrograms (μg), milliliters (ml) for dosage, days, weeks, months for time, and physiological indicators like blood pressure (mmHg) and heart rate (bpm).
Constraints Imposed by Data Characteristics on Multi-Turn Conversations and Prompt Engineering
The diverse structure of cardiovascular product data requires the multi-turn conversation system to have robust document parsing capabilities. This enables accurate extraction of key information from various text formats. For example, efficacy data from clinical trial reports often appears in tables, requiring the model to understand table structures. The stability of data updates means the knowledge base does not need overly frequent full updates. However, critical guidelines or drug warnings require timely incremental synchronization. The precision of specialized fields like dosage, units, and physiological indicators demands high-quality prompt generation to avoid misleading responses due to unit confusion or numerical errors. In multi-turn conversations, users may progressively refine questions about a product or indication. The system must accurately understand intent based on context and effectively link different document types, such as navigating from a drug insert to detailed data from a related clinical trial.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Accommodates complex cardiovascular product descriptions and multi-turn conversation history while maintaining response speed. |
Chunk size (Chunk Length) | 800–1200 characters | Balances semantic integrity with retrieval efficiency, suitable for lengthy documents in the cardiovascular domain. |
Recall count (Retrieval Count) | Top 7 | Increases coverage of relevant information to address the multi-dimensional complexity of user queries. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves the accuracy of retrieved results, filtering out irrelevant or weakly related medical information. |
Rerank result count (Reranked Return Count) | Top 3 | Refines the final answer presented to the user, focusing on the most core cardiovascular product information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses the parsing time requirements for large clinical trial reports or multi-page PDF inserts. |
Common Pitfalls
- Responses show unit confusion or numerical errors in drug dosages or indications. This occurs when prompts do not explicitly require strict validation of numbers and units, or when related data in the knowledge base is ambiguous.
- When users ask about treatment plans for specific cardiovascular diseases, the system provides only single product information without integrating relevant guidelines or comparing similar products. This happens when the knowledge base indexing strategy does not adequately consider the multi-dimensional relationships between diseases, products, and guidelines.
- During a conversation, the system responds to non-medical requests, such as "read the code in the directory." This occurs when prompts do not effectively limit the conversation scope, leading the model to fail in identifying and rejecting out-of-domain questions.
Verification Steps
- Conduct multi-turn conversation tests for common questions about typical cardiovascular products (e.g., statins, anticoagulants). Check the accuracy of key information in responses, such as drug names, dosages, indications, and adverse reactions. Compare with official inserts to ensure no discrepancies.
- Simulate users progressively refining queries (e.g., from "hypertension treatment" to "side effects of a specific antihypertensive drug"). Observe whether the system correctly understands intent across turns and provides coherent and relevant medical information, ensuring context comprehension.
- Test with queries containing incorrect or ambiguous numerical units. Verify if the system can identify and correct these errors, or clearly indicate insufficient information. This assesses the effectiveness of prompt validation for numbers and units.
- Attempt to input questions unrelated to the cardiovascular domain. Check if the system explicitly declines to answer or guides the user back to the professional domain. This evaluates the prompt's effectiveness in defining conversation boundaries.
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.