Data Characteristics for This Category
Market access regulation data originates primarily from official bodies like the National Medical Products Administration and the National Healthcare Security Administration. It also includes standards and consensuses from industry associations. These documents are typically in PDF, Word, or HTML format. Updates occur quarterly or annually, with more frequent updates during significant policy changes. Document structures are often chapter-based, containing extensive technical terms, definitions, procedural steps, and timelines. The data includes fields such as drug and medical device registration numbers, medical insurance codes, payment standards, and approval time limits. Units are commonly days, months, Yuan, or batches.
Constraints from "Multi-Turn Conversations and Prompts"
The specialized nature and high update frequency of market access regulation documents require multi-turn conversation systems to have precise semantic understanding. This avoids misjudgment due to ambiguous terminology. The chapter-based document structure and procedural content necessitate handling long text context for cross-chapter logical reasoning and step tracing. For example, a user might first ask about "the registration process for a certain medical device" and then follow up with "the time limit for the clinical trial phase." The system must link these two questions to different points within the same process. Rapid regulatory updates also demand efficient content synchronization mechanisms for the knowledge base, ensuring timeliness and accuracy. For questions involving specific numerical values (e.g., approval time limits, payment standards), the system must accurately extract and present them from the original text, avoiding vague answers.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
maxContext | 8000 tokens | Market access documents have strong contextual relevance, requiring support for long-text reasoning. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Ensures retrieved regulatory provisions are highly relevant to user questions, reducing false positives. |
Recall count (Retrieval Count) | Top 5 entries (Top 5) | Balances retrieval breadth with subsequent processing efficiency, reducing interference from irrelevant information. |
Rerank result count (Reranked Return Count) | Top 3 entries (Top 3) | Selects the most relevant regulatory snippets, improving the precision of the final answer. |
Chunk size (Chunk Length) | 500 characters (500 characters) | Adapts to the logical completeness of regulatory provisions, avoiding the truncation of critical information. |
Prompt Template (Prompt Template) | "As a Market Access Expert，Based on the Provided Regulatory Content，Answer User Questions in Detail，And citation Original Text registration number Or Medical Insurance Code。" ("As a market access expert, based on the provided regulatory content, answer user questions in detail and cite the original registration number or medical insurance code.") | Forces the system to act as an expert and provide key information citations, enhancing the professionalism and credibility of the answer. |
Common Mistakes
- AI answers to procedural questions may have missing steps or incorrect sequences. This occurs when the system fails to fully understand the temporal logic in the document, or when
maxContextis insufficient, leading to context truncation. - When users ask for specific numerical values (e.g., approval cycles), the AI provides general answers instead of concrete numbers. This can happen if knowledge chunking is too granular, separating key numbers from descriptions, or if the prompt does not explicitly require extracting specific values.
- After private deployment, the chat interface
http://localhost:3000/api/v1/chat/completionsreports a CORS error. This typically means the frontend request and backend service are not on the same domain. Adjust the backend service's CORS policy configuration to allow cross-origin requests from specific or all sources.
How to Confirm Configuration
- For complex, multi-turn procedural questions, verify the completeness and logical order of AI answers, ensuring consistency with original regulatory documents.
- Randomly select questions involving specific numerical values (e.g., "What is the registration application time limit in days?") and check if the numerical values in the AI's answer, such as registration numbers or medical insurance codes, precisely match the original document.
- Simulate users asking questions related to the latest policies at different times. Check the AI knowledge base's update response speed to ensure it retrieves and cites the most recently published regulatory provisions.
- Test the system's contextual understanding in continuous follow-up scenarios. For example, first ask "what is the registration process for a certain device," then ask "what materials are required for its clinical trial part," and observe if the system maintains topic consistency.
The values provided are common starting points. Measure them 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.