Data Characteristics
Quality documentation data in the infectious disease domain originates from guidelines published by disease prevention and control agencies, clinical treatment protocols, pathogen detection reports, drug susceptibility test results, and epidemiological survey data. This data updates relatively quickly. New infectious diseases or drug-resistant variants can lead to guideline and recommendation updates within weeks. Document structures typically include fixed fields such as disease definition, epidemiological characteristics, clinical manifestations, diagnostic criteria, treatment plans, and prevention and control measures. Specific content varies significantly based on disease type (e.g., bacterial, viral, fungal infections). For example, bacterial infection documents may emphasize antibiotic selection and resistance profiles, while viral infection documents may focus on nucleic acid testing and antiviral drugs. Common units in the data include case numbers, incidence rates (‰), mortality rates (%), detection rates (%), drug concentrations (mg/L), and incubation periods (days).
Constraints Imposed by Data Characteristics on Deployment and Upgrade
The rapid update nature of infectious disease data requires deployment solutions to support efficient document version management and incremental updates. This ensures the knowledge base remains current. Document structure diversity, particularly field differences due to various pathogen types, necessitates flexible parsing strategies during data preprocessing. This ensures accurate extraction of key information. For instance, documents on viral infections may require specific attention to gene sequences or variant strain information. Numerical units such as percentages, per mille rates, and drug concentrations in the data require additional processing during vectorization and retrieval. This prevents semantic understanding deviations caused by unit or dimension differences. Furthermore, high-quality inspection readiness requires the knowledge base to provide traceability to original documents. Deployment must ensure document integrity and traceability.
Configuration Settings
| Configuration Item | Suggested Value | Rationale
What this category's data looks like
Infectious disease quality documentation data primarily comes from guidelines issued by disease prevention and control agencies, clinical diagnosis and treatment protocols, pathogen detection reports, drug sensitivity test results, and epidemiological investigation data. Its update frequency is relatively fast. Especially with new infectious diseases or drug-resistant variants, relevant guidelines and recommendations can update within weeks. Document structures typically include fixed fields such as disease definition, epidemiological characteristics, clinical manifestations, diagnostic criteria, treatment plans, and prevention and control measures. However, specific content varies significantly based on disease type (e.g., bacterial, viral, fungal infections). For example, bacterial infection documents may emphasize antibiotic selection and resistance profiles, while viral infection documents may focus on nucleic acid testing and antiviral drugs. Data often involves units like case numbers, incidence rates (‰), mortality rates (%), detection rates (%), drug concentrations (mg/L), and incubation periods (days).
Constraints on "Deployment and Upgrade" from these Characteristics
The rapid update nature of infectious disease data requires deployment solutions to support efficient document version management and incremental updates. This ensures the knowledge base remains current. Document structure diversity, especially field differences due to various pathogen types, necessitates flexible parsing strategies during data preprocessing. This ensures accurate extraction of key information. For instance, documents on viral infections may require specific attention to gene sequences or variant strain information. Numerical units such as percentages, per mille rates, and drug concentrations in the data require additional processing during vectorization and retrieval. This prevents semantic understanding deviations caused by unit or dimension differences. Additionally, high-quality inspection readiness requires the knowledge base to provide traceability to original documents. Deployment must ensure document integrity and traceability.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 200 MB | Accommodates large guideline or report files, especially those with charts. |
Chunk size (Chunk Length) | 500–800 characters (characters) | Balances context completeness and retrieval efficiency, adapting to medical terminology density. |
maxContext | 4000 characters (characters) | Ensures coverage of key information like disease definitions and diagnostic criteria. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Improves retrieval accuracy, reducing interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Allows for parsing complex structures or scanned documents. |
Rerank result count (Reranked Results) | Top 8 entries ( |
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.