Data Characteristics
Data for stem cell therapy regulations and SOP documents primarily originates from regulatory documents published by the National Medical Products Administration (NMPA), guidelines from industry associations, and clinical operating procedures developed by medical institutions. These documents have a relatively low update frequency, typically revised quarterly or annually, with ad-hoc releases for major policy changes. Document structures are predominantly unstructured text, often in PDF or Word format, containing extensive legal provisions, technical specifications, ethical guidelines, and operational steps. Fields and units frequently involve specific terminology and units such as cell count (e.g., 10^6 cells/kg), culture period (e.g., 7 days), storage temperature (e.g., -196 ℃), and clinical trial phases (e.g., Phase I, Phase II).
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The low update frequency and high authority of stem cell therapy regulatory documents necessitate robust document version management and traceability in knowledge base construction. The unstructured text format requires efficient text segmentation and semantic understanding for effective multiturn conversations, preventing context loss due to long texts. Specific fields like cell count and culture period in the documents mean that prompt design must guide the model to recognize and correctly interpret these specialized terms and units, reducing misinterpretation. Furthermore, the rigor of legal and ethical clauses demands that the dialogue system adheres strictly to the original text during Q&A, avoiding any misleading generalizations or inferences to ensure accuracy and compliance. Multiturn conversations require accurate identification of user intent, distinguishing between queries for specific operational steps, regulatory provisions, or ethical considerations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Accommodates regulatory text paragraph length, balancing semantic completeness and recall efficiency |
Recall count | Top 5 entries | Improves relevance, reduces unnecessary information interference |
Similarity threshold | 0.78 | Balances recall precision and recall rate, filters out low-relevance results |
maxContext | 4096 tokens | Accommodates multiturn conversation history and recalled content, ensuring contextual coherence |
Rerank result count | 3 entries | Further refines the most relevant snippets from recall results, enhancing user experience |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles parsing large PDF documents, preventing timeouts |
Common Pitfalls
- During multiturn conversations, the model fails to accurately associate specialized terms or abbreviations across different turns, leading to responses that deviate from user expectations. This occurs due to insufficient context management, failing to effectively persist the referential relationships of specialized vocabulary.
- When asked about stem cell counts or culture conditions, the model returns incorrect numerical values or units, or even null values. This happens because specific number-unit pairs are not structurally extracted or semantically annotated during document parsing, making it difficult for the model to accurately identify them.
- When users inquire about the latest version or revision history of a regulation, the model cannot provide accurate information. This is due to a lack of effective management and indexing of metadata such as version numbers and publication dates during document import into the knowledge base.
Verification of Configuration
- Randomly select 5 multiturn conversation scenarios involving different specialized terms and units. Verify if the model consistently understands and references this information correctly.
- Test the segmentation and indexing results for 3 different types of stem cell therapy regulatory documents (e.g., national regulations, industry guidelines, institutional SOPs). Check if segmentation boundaries are reasonable and if key information is fully retained.
- Simulate 10 user queries about regulatory revision history or specific version content. Cross-reference the document version information returned by the model with the metadata of the original documents.
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.