Data Characteristics in This Category
Pharmacovigilance data in pharmaceutical e-commerce primarily comes from drug sales records, user consultation feedback, online reviews, drug inserts, and public adverse drug reaction databases. Data updates frequently, especially with new drug launches or batch updates. Document structures typically include structured sales orders, user profiles, and semi-structured or unstructured user feedback text. Common fields include drug generic name, batch number, manufacturer, purchase date, quantity, patient age, gender, allergy history, symptom description, adverse reaction type, severity, and treatment measures. Units often include milligrams (mg), grams (g), milliliters (ml) for dosage; daily or weekly frequency; and days, hours for time.
Constraints Imposed by These Features on Multi-Turn Conversations and Prompts
Data characteristics in pharmaceutical e-commerce pharmacovigilance impose specific requirements on multi-turn conversation and prompt design. First, the professional and rigorous nature of drug information demands that the dialogue system precisely cite or extract content from drug inserts in the knowledge base when understanding user intent and generating responses, avoiding vague or misleading statements. Second, user feedback often contains unstructured symptom descriptions, requiring prompts to guide the model in effective information extraction, such as identifying disease names, symptom keywords, dosage, and timing. The high frequency of data updates means the knowledge base must stay synchronized, and prompts should instruct the model to prioritize retrieving the latest data. Additionally, users may mention multiple drugs or symptoms, so multi-turn conversations must maintain contextual consistency, ensuring each interaction accurately relates to previous discussions to prevent information loss or confusion.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 tokens | Accommodates multi-turn conversation context and long drug inserts. |
Similarity threshold (Similarity Threshold) | 0.75 | Improves precision of knowledge base retrieval, reducing irrelevant information interference. |
Recall count (Recall Count) | 5 entries | Covers potentially relevant knowledge points, providing comprehensive references for the model. |
Chunk size (Segment Length) | 500 characters | Balances semantic completeness of text with vector retrieval efficiency. |
Rerank result count (Reranked Return Count) | 3 entries | Prioritizes displaying core information most relevant to user intent. |
temperature | 0.5 | Ensures accuracy and stability of responses, preventing excessive divergence. |
Three Common Mistakes
- The model returns "Connection Error" or "Gateway Timeout." This usually indicates an unstable connection with the upstream model service or a request body that is too large, leading to a timeout.
- The response fails to accurately identify drug names or symptoms mentioned by the user. This happens when prompts provide insufficient guidance for entity recognition or the knowledge base lacks comprehensive coverage of relevant terms.
- In multi-turn conversations, the model fails to maintain context from previous turns, leading to disconnected responses. This might be due to
maxContextbeing set too low or prompts failing to effectively instruct the model to maintain conversation state.
How to Confirm Proper Configuration
- Conduct multi-turn conversation tests to verify if the model continuously understands and responds to context related to drugs and symptoms.
- Input complex queries containing multiple drug names and symptoms to check if the model accurately identifies all key entities and provides relevant knowledge.
- Intentionally introduce easily confusable drug names or symptom descriptions to observe if the model can clarify through knowledge base retrieval or follow-up questions.
- After regular knowledge base updates, test if the model can cite the latest drug information or adverse reaction data.
The values provided are common starting points and should be measured against the reader's 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.