Data Characteristics in this Category
Stem cell therapy R&D documents come from diverse sources. These include clinical trial reports, patent literature, research papers, internal experimental records, and regulatory agency submissions. Documents are typically in PDF, Word, or structured database export formats. Update cycles depend on R&D timelines and regulatory approvals. New data and revisions usually appear after phased clinical trial reports or regulatory policy changes. Document structures are complex. They contain extensive specialized terminology, abbreviations, charts, flowcharts, and experimental data. Fields and units are highly specific. Examples include cell line names, culture medium components, differentiation protocols, cell viability percentages (%), cell counts (cells/mL), gene expression levels (e.g., FPKM, TPM), biomarker concentrations (ng/mL, nM), and clinical indicators (e.g., lesion area mm², serum indicators mg/dL).
Constraints Imposed by these Characteristics on "Multi-turn Conversation and Prompts"
The complex structure and high specialization of stem cell therapy R&D documents demand accuracy in multi-turn conversations and precision in prompts. Specialized terms and abbreviations require exact entity recognition and disambiguation. This avoids misunderstandings due to missing context. For example, "PSC" can refer to pluripotent stem cells or a specific protein; the dialogue system must use context for judgment. Extensive experimental data and charts mean pure text analysis is insufficient to capture all information. Image recognition and table parsing capabilities are necessary. Unpredictable update frequencies require the system to quickly index and update the knowledge base, ensuring conversations are based on the latest information. Furthermore, diverse units and numerical formats in the field necessitate prompt design that guides the model to maintain unit consistency and accurately present answers. Lengthy and information-dense documents challenge context window management and information extraction efficiency. The dialogue system must effectively filter and integrate key information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale for this Value |
|---|---|---|
maxContext | 4096 | Handles common lengthy descriptions and complex logic in stem cell R&D documents, ensuring context completeness. |
Chunk size (Chunk Length) | 500–800 characters (characters) | Balances semantic integrity of text with retrieval efficiency, preventing long paragraphs from diluting key information. |
Recall count (Retrieval Count) | Top 8–12 entries (top 8–12 items) | The specialized nature of stem cell research requires more relevant snippets for matching, improving accuracy. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Domain terminology has high similarity; a higher threshold is needed to filter out generalized information and focus on the core. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 items) | After reranking model processing, retains the most relevant few items, reducing noise. |
Prompt Template | Contains "根据提供的干细胞治疗研发文档，分析细胞系、培养条件、分化方案和临床前数据" (Based on the provided stem cell therapy R&D documents, analyze cell lines, culture conditions, differentiation protocols, and preclinical data) | Clearly defines the model's answer scope, focusing on core elements of stem cell R&D, reducing the generation of irrelevant information. |
Three Common Mistakes
- Symptom: The model frequently makes errors or confuses specialized terminology during conversations. Reason: The knowledge base inadequately includes or updates the latest stem cell domain terminology and abbreviation definitions, leading to the model's lack of precise vocabulary understanding.
- Symptom: When users ask about specific experimental data, the model cannot provide concrete values and replies "No relevant information found." Reason: The document parsing stage failed to effectively extract structured data from charts or complex tables, or the extracted data was not correctly linked to the knowledge graph.
- Symptom: When calling the API to retrieve conversation history, the returned data volume is too large, including content from non-current user sessions. Reason: When designing the history retrieval interface,
customUidor other user identifiers were not correctly used for filtering, resulting in too broad a data scope.
How to Confirm Proper Configuration
- Conduct multi-turn simulated conversations. Ask questions about different cell lines, culture protocols, and clinical indicators. Check the accuracy of terminology and precision of values in the model's responses.
- For documents in the knowledge base that contain charts and tables, perform data extraction validation. Ensure the model can accurately extract and cite key data from them, and verify unit consistency.
- Use the API interface to initiate conversations with different
customUidvalues and retrieve history. Verify that each retrieved result only contains session content corresponding to the respectivecustomUid.
The values provided are common starting points and 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.