Data Characteristics
Data sources for attenuated inactivated vaccine regulations and SOP documents primarily include guidelines from the National Medical Products Administration (NMPA), the World Health Organization (WHO), and internal Quality Management System (QMS) documents. These documents often exist as PDFs, Word files, or structured databases. Update frequency is typically quarterly or annually, with additional revisions for major policy changes or vaccine technology breakthroughs. Document structures are rigorous, containing numerous clauses, detailed rules, charts, and appendices. Fields and units often involve batch numbers, production dates, expiration dates, storage conditions (e.g., 2–8°C), dosage units (e.g., IU, PFU), testing standards (e.g., ELISA titer), adverse event rates (e.g., per million doses), and detailed operational step codes.
Constraints Imposed by Data Characteristics on Multi-Turn Conversations and Prompts
The rigorous and highly specialized nature of attenuated inactivated vaccine regulation documents requires multi-turn dialogue systems to precisely identify professional terminology and contextual relationships when understanding user intent. For example, when a user mentions "batch," the system must determine if it refers to a production batch or a testing batch based on prior conversation. Document update frequency dictates the real-time nature of knowledge base retrieval; outdated regulations can lead to incorrect answers. Therefore, the knowledge base update mechanism needs close integration with the document release process. The extensive operational steps and codes in SOPs challenge prompt engineering, requiring the model to accurately extract specific steps and parameter values, avoiding vague responses. Furthermore, questions involving specific numerical values like storage conditions and dosages require the model to identify units and compare numerical values, preventing safety risks due to unit confusion. Charts and appendices in documents also demand the system possess some multimodal processing capability or structured information extraction capability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters (characters) | Vaccine regulation documents are logically tight; longer segments help retain contextual completeness and reduce the omission of critical information. |
Recall count (Retrieval Count) | Top 5 entries (top 5 items) | Ensures retrieved segments cover core regulatory clauses, improving relevance. |
Similarity threshold (Similarity Threshold) | 0.78 | Ensures retrieved regulatory clauses are highly relevant to the user query, filtering out vague matches. |
maxContext | 32000 token | Accommodates multi-turn conversation history and retrieved document segments, handling complex regulatory questions. |
temperature | 0.3 | Reduces model divergence, ensuring answers are based on original regulatory text and minimizing hallucinations. |
Rerank result count (Reranked Return Count) | Top 3 entries (top 3 items) | Performs a secondary sort based on initial retrieval, further optimizing answer accuracy. |
Three Common Mistakes
- Symptom: The system provides outdated or incorrect national standards when answering questions about specific vaccine batch disposal procedures. Reason: Knowledge base documents were not updated promptly, or the document version management mechanism failed, leading to the retrieval of outdated regulations.
- Symptom: When a user asks about "vaccine storage conditions," the system's answer lacks specific temperature ranges or time limits. Reason: The prompt was not clear enough in guiding the model to extract numerical information, or knowledge base segmentation failed to effectively retain critical numerical and unit information.
- Symptom: When integrating external plugins, API calls frequently result in
HTTP 400 Bad Requesterrors. Reason: Plugin integration parameter field names or data formats do not match FastGPT requirements. For example, thereasoning_ctag was not correctly parsed into a FastGPT-recognizable inference chain.
How to Confirm Proper Configuration
- Prepare a set of test cases, including multi-turn follow-up questions, for core regulatory clauses and SOPs. Verify the system's ability to consistently provide accurate and coherent answers, and assess whether answers include critical batch numbers, temperatures, and other information.
- Regularly simulate user queries, especially focusing on confusing or frequently updated sections of regulations. Check if the document segments retrieved by the system are the latest versions, and cross-reference answers with original documents for accuracy.
- Monitor the model's recognition rate of professional terminology in dialogue logs. If specific vaccine names, testing methods, or other terms are frequently misunderstood, adjust prompts or knowledge base segmentation strategies to improve understanding accuracy.
The values given 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.