Data Characteristics
Research and development document data for Direct-To-Patient (DTP) pharmacies primarily originates from pharmaceutical company clinical trial reports, drug inserts, pharmacist training manuals, patient medication guides, and various documents issued by drug regulatory agencies. Document update frequency depends on new drug approvals, expanded indications, or regulatory changes. Updates typically occur quarterly or semi-annually, with more frequent updates for urgent changes. Documents have complex structures, containing extensive specialized terminology, dosage units, pharmacological mechanism descriptions, adverse reaction lists, and interaction information. Common fields include: drug generic name, brand name, indications, dosage and administration, contraindications, adverse reactions, drug interactions, storage conditions, manufacturer, approval number, and batch number. Dosage units often involve milligrams (mg), milliliters (mL), and international units (IU). Time units are typically days (days) and hours (hours), frequently accompanied by complex dosing regimen descriptions.
Constraints Imposed by These Characteristics on Multi-Turn Conversations and Prompts
The complexity of DTP pharmacy R&D documents places specific demands on the accuracy of multi-turn conversations and prompt construction. High-density specialized terminology and cross-references within documents require the system to effectively identify and link concepts across different documents. This prevents information distortion due to semantic drift in multi-turn conversations. The precision required for drug dosages and administration schemes means prompts must accurately guide the model to extract numerical values and units, and handle numerical ranges or conditional statements. For example, when a user asks about "pediatric dosage for a certain drug," the model needs to find the corresponding dosage from multiple age groups and weight ranges. Frequent document updates necessitate dynamic synchronization of the knowledge base. The multi-turn conversation system must rapidly index the latest versions to avoid providing outdated information. For patient medication guidance scenarios, the system must avoid providing direct diagnoses or treatment advice. Prompts should reinforce its role as an information auxiliary tool.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 8 | Ensures sufficient historical conversation turns are covered for complex drug interaction queries, maintaining contextual coherence. |
similarityThreshold | 0.75 | For highly specialized medical terminology, increasing the similarity threshold helps recall more precise document segments, reducing interference from irrelevant information. |
top_n | 5 | Recalls the top 5 relevant document segments, balancing recall efficiency and information completeness to meet pharmacists' rapid retrieval needs. |
prompt | Include "Answer only based on the provided documents; avoid inference or providing medical advice." | Constrains model behavior, preventing the generation of speculative content or direct medical guidance beyond the scope of the knowledge base. |
chunkSize | 500-800 characters | Considering the prevalence of long paragraphs and complex tables in R&D documents, appropriately increasing chunk length ensures critical information is not truncated. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Provides sufficient parsing time when processing large clinical trial reports or drug inserts, preventing parsing failures due to excessively large files. |
Common Pitfalls
- Observation: In a multi-turn conversation, a user asks about contraindications for a drug, but the system's answer includes adverse reaction information. Reason: The prompt insufficiently distinguishes between different information types, or contraindication and adverse reaction information were chunked together during document parsing, leading to model confusion.
- Observation: After a user deletes conversation content via a password-less link, the corresponding conversation record still exists in the backend logs. Reason: The
chatIdparameter was not correctly passed in the API call, or the backend logging system was not effectively synchronized with the frontend deletion operation, resulting in inconsistencies between log records and actual user actions. - Observation: A user asks about specific batch information for a drug, and the system cannot provide an accurate answer or indicates the information is outdated. Reason: The knowledge base is not updated in a timely manner, or time-sensitive fields like batch numbers were not correctly extracted and indexed during document parsing.
Verification of Configuration
- Conduct multi-turn conversation tests to verify whether the system can accurately understand and provide consistent answers based on document content when users mention core fields like drug generic name, indications, and dosage and administration in a conversation.
- Simulate scenarios where users query complex drug interactions. Check whether the model maintains contextual coherence in multi-turn conversations and progressively refines its answers based on historical conversation content, avoiding repetition or contradiction.
- Check backend logs to ensure that the
chatIdfor each conversation is correctly recorded, and that corresponding records in the backend logs are correctly identified or removed after a user deletes a conversation.
Note: The values provided are common starting points. They 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.