Data Characteristics
Quality documents for respiratory system diseases originate from clinical trial reports, drug monographs, treatment guidelines, pharmacovigilance data, and pharmaceutical company internal SOPs (Standard Operating Procedures) and quality management system files. Regulatory changes, new drug approvals, and clinical research advancements affect document update frequency, typically quarterly or annually. However, severe adverse event reports may be generated in real-time. Document structures vary, including structured tabular data (e.g., adverse event lists, dose-response data), semi-structured text (e.g., clinical trial protocols, investigator brochures), and unstructured long descriptions (e.g., case reports, expert consensus). Fields and units involve dosage (mg, μg), frequency (times/day, q.d.), duration (days, weeks), biomarker indicators (e.g., FEV1 L, SaO2 %), and disease staging (e.g., GOLD classification). These documents often contain complex medical terminology, abbreviations, and specialized formulas.
Constraints from Data Characteristics on Model Integration and Configuration
The complex data structure and specialized nature of respiratory system quality documents impose specific requirements on model integration and configuration. The high frequency of medical terminology, abbreviations, and formulas in documents demands strong professional semantic understanding from the base model. General models may struggle with accurate identification and interpretation. The mixture of structured data and unstructured text makes a single text segmentation strategy ineffective, requiring differentiated processing for various content types. Additionally, the coexistence of periodic and real-time document updates challenges the knowledge base update mechanism. It must support both batch periodic updates and rapid responses to urgent information. The presence of specific fields and units like disease staging and biomarkers requires vector models to effectively distinguish this specialized information during embedding, preventing critical information loss due to generalized processing.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances context completeness and vector model processing efficiency, accommodating long sentences common in medical documents. |
Chunk overlap (Segment Overlap) | 100–150 characters | Ensures contextual continuity across segments, especially when describing complex pathophysiological processes. |
Recall count (Recall Count) | Top 8–12 items | Increases recall quantity to improve accuracy, considering the rigor of medical knowledge and the need for multi-angle verification. |
Similarity threshold (Similarity Threshold) | Calibrated by measurement | Adjusts based on the semantic similarity distribution of the specific dataset to ensure recall relevance. |
Rerank result count (Rerank Return Count) | Top 5 items | Refines the key information presented to the user while maintaining information richness. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Limits the size of most clinical trial reports or large guidelines, balancing upload performance. |
Common Pitfalls
- Knowledge base query results fail to correctly parse medical formulas. Formulas appear as garbled text or plain text characters. The model is not specifically trained for LaTeX or MathML formula formats, or front-end rendering support is insufficient.
- Model responses contain errors in critical drug dosages or biological indicator units. Values and units mismatch or units are missing. The vector model fails to effectively distinguish the semantic association between numbers and units during embedding.
- After importing a large number of PDF documents, some document content is not retrievable. Relevant queries yield no results or incomplete results. The PDF parser fails to correctly extract table or image content from the documents.
Verification of Configuration
- Upload and query documents containing complex medical formulas. Verify the rendering effect and accuracy of formulas in the model's response.
- Ask questions about documents containing specific dosage, frequency, or biomarker units. Check the matching degree between values and units in the model's response.
- Import a batch of respiratory system quality documents with various structures (plain text, tables, embedded images). Verify that all content types are effectively retrieved and cited through precise questioning.
- After a knowledge base update, compare query results between old and new versions of documents. Confirm that incremental or changed content has been correctly incorporated into the knowledge system.
Note: The values provided are common starting points. Measure performance against specific samples to determine optimal settings.
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.