Data Characteristics for this Category
Medical e-commerce registration and declaration data primarily originates from regulations, guidelines, technical review requirements issued by regulatory bodies, and internal enterprise documents such as product development reports, clinical trial data, manufacturing process files, quality standards, and stability study reports. This data is highly standardized and specialized. It typically exists in various document formats like PDF, Word, and Excel, containing both structured and unstructured information. Data update frequency is influenced by policy and regulatory adjustments and product lifecycles, such as new drug registration classification policies or post-market change requirements. Documents often contain pharmaceutical terms, chemical structures, statistical symbols, units of measurement (e.g., mg/mL, IU, %), and complex tables and charts.
Constraints Imposed by these Characteristics on "Multi-turn Conversation and Prompts"
The specialized and standardized nature of medical e-commerce registration and declaration data requires multi-turn conversation systems to be highly sensitive to domain-specific vocabulary when interpreting user intent, preventing generalized understanding that could lead to information deviation. Complex tables and charts in documents necessitate that knowledge base segmentation and retrieval maintain contextual integrity, preventing the loss of critical information due to overly granular segmentation. The high frequency of policy and regulatory updates means the knowledge base requires an efficient incremental update mechanism to ensure the timeliness of conversational content. Furthermore, users preparing declaration documents often need to compare and cross-verify information across multiple documents and sections. This demands that the conversation system supports multi-source information integration and that prompt design guides users to ask precise questions, avoiding vague searches.
Configuration Strategy
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 8 | Ensures sufficient historical context is retained in multi-turn conversations to understand complex follow-up questions and comparison needs, while avoiding overly long contexts that could cause the model to deviate from the topic. |
Chunk size | 800–1200 characters | Paragraphs in medical declaration documents are typically long and contain complete technical descriptions. This length helps preserve the integrity of core information and reduces semantic fragmentation. |
Recall count | Top 5 entries | Given the rigor required for declaration documents, retrieving more relevant items helps provide comprehensive information and reduces the risk of overlooking critical details. |
Similarity threshold | Calibrate by actual measurement | Ensures the professional relevance of retrieved content, preventing matching general vocabulary to inaccurate regulations or technical requirements. |
Rerank result count | Top 3 entries | Among the retrieved results, re-ranking further filters for the most relevant items that directly answer the user's question, improving answer accuracy. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Medical declaration documents often contain many pages and complex layouts. Extending the parsing time ensures that large PDF or Word documents can be fully processed. |
Three Common Pitfalls
- The "Knowledge base reference is empty" prompt appears in the conversation because the document parsing module failed to correctly extract table or chart content from the document, leading to critical information not being indexed.
- After a user uploads an attachment, the conversation fails to effectively utilize the attachment's content for answering, manifesting as the system only providing general information. This occurs because the file upload did not trigger the corresponding knowledge base update or specific parsing process.
- The system cites outdated or revoked regulatory provisions in its answers. This happens because the knowledge base's incremental update mechanism did not cover all relevant file types, resulting in some old version information remaining in the index.
How to Verify Correct Configuration
- Select complex questions from typical declaration documents and conduct multi-turn conversation tests. Verify whether the system's answers accurately cite relevant regulatory provisions and technical details, and check if the cited regulatory versions are current.
- Upload PDF documents containing complex tables and charts. Test whether the system can accurately extract and interpret table data or chart conclusions during the conversation, verifying the completeness of document parsing.
- Simulate a regulatory update scenario by updating some files in the knowledge base. Then, ask related questions to confirm that the system prioritizes retrieving and citing the latest version of the content, verifying the effectiveness of the knowledge base update mechanism.
Note: The values provided are common starting points. They should be measured 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.