Data Characteristics in this Domain
R&D documents for respiratory system diseases cover extensive data, from basic research to clinical trials. Data sources are diverse, including scientific papers, clinical trial reports, drug monographs, patent literature, and disease databases. Document update frequency depends on the R&D stage. Basic research progress may update weekly, clinical trial data publishes in phases, and drug monographs update less frequently after market release but revise quickly for safety information. Document structures are complex. They often contain numerous medical terms, biological pathway diagrams, statistical data tables, dosage information, and adverse event reports. Fields are highly specific, such as "FEV1" for forced expiratory volume in one second and "PaO2" for arterial partial pressure of oxygen. Units like "L/min," "mmHg," and "μg/kg" are common, often with complex abbreviations and unit conversions.
Constraints Imposed by these Characteristics on Citation and Traceability
The complexity of respiratory system R&D documents places specific demands on citation and traceability. Highly specialized medical terms and abbreviations make precise matching and contextual understanding critical. The RAG system must recognize synonyms and related concepts during retrieval to avoid missing important information due to literal mismatches. Extensive statistical and dosage information requires the system to cite original data points and trace back to the source charts or paragraphs, ensuring data citation accuracy. Inconsistent update frequencies mean the system must handle citations from different document versions and distinguish the currently valid information. When documents contain cross-references to studies or external database links, traceability must extend to these external resources to provide a complete chain of evidence.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters (characters) | Respiratory system documents have strong contextual relevance. An moderate length helps capture complete semantics and prevents truncation of key information. |
Recall count (Retrieval Count) | Top 8–12 entries (top 8–12 segments) | Ensures coverage of multiple potentially relevant segments for complex queries, increasing information recall rate and addressing terminology diversity. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Domain terminology has high similarity. A higher threshold ensures retrieval precision and filters out generalized information. |
Rerank result count (Reranked Return Count) | Top 5 entries (top 5 segments) | Reduces redundant information presented to the user, focusing on the most relevant core content and improving traceability efficiency. |
Max Context | 4096 tokens | Ensures the model has sufficient space to process complex medical descriptions and multi-segment citations, maintaining logical coherence. |
Max Knowledge Base Citations | segments | Limits the number of cited segments to avoid overly long responses while ensuring critical supporting information is displayed. |
Common Pitfalls
- Replies contain
\nor other escape characters, failing to implement line breaks. The system does not correctly escape or render specific characters. - Citation sources appear empty or incomplete. The
[QUOTE]tag has no content. This usually occurs when the knowledge base segment granularity is too large or the retrieval strategy fails to precisely match the original passage. - Model output content is inconsistent with the citation source. For example, a study is cited, but the output conclusion contradicts the study's findings. The model over-relies on its own knowledge during generation and does not strictly follow the cited content.
Verification of Configuration
- For typical queries, check if all
[QUOTE]tags in the reply content are clickable and accurately jump to the original passage. Verify that the jump location is highly relevant to the cited content. - Select documents containing tables, figure captions, and dosage information. Verify that the system correctly attributes sources and maintains information integrity when citing these specific content formats.
- Choose documents involving new drug R&D or the latest clinical guidelines. Test if the system can cite the most recent version of information and distinguish between different versions when handling frequently updated data.
Note: 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.