Data Characteristics
Infectious disease protocol and SOP data primarily originate from guidelines and standards published by national health commissions and disease control centers, as well as internal diagnostic and treatment processes and prevention manuals from healthcare institutions. These documents are mainly in PDF, Word, or internal system pages. They are updated frequently, especially during new infectious disease outbreaks. Document structures typically include an introduction, definitions, epidemiology, clinical manifestations, diagnostic criteria, treatment plans, and prevention and control measures. Fields often involve pathogen names, hosts, transmission routes, incubation periods, clinical classifications, drug names, dosages, courses of treatment, biosafety levels, and isolation measures. Units cover time (days, hours), dosage (mg, g, IU), concentration (mg/L), and temperature (°C).
Constraints Imposed by These Characteristics on Workflow Orchestration
The rapid update frequency of infectious disease protocol documents requires an efficient knowledge base synchronization mechanism within the workflow to ensure the timeliness of Q&A results. Diverse document formats and complex structures demand more robust document parsing, especially for extracting content from tables and figures. The richness of field types and units necessitates structured output for Q&A results; for example, drug dosages must include units. Due to the sensitive nature of infectious diseases, some protocol Q&A may involve sensitive information, requiring integration of access control or anonymization modules into the workflow. The workflow must handle cross-document knowledge correlation, such as extracting diagnostic criteria from one guideline and then retrieving the corresponding treatment process from another SOP.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale This document provides guidance on workflow orchestration for infectious disease protocols.
Data Characteristics
Infectious disease protocol and SOP data primarily come from guidelines and standards published by national health commissions and disease control centers, as well as internal diagnostic and treatment processes and prevention manuals from healthcare institutions. These documents are mainly in PDF, Word, or internal system pages. They are updated frequently, especially during new infectious disease outbreaks. Document structures typically include an introduction, definitions, epidemiology, clinical manifestations, diagnostic criteria, treatment plans, and prevention and control measures. Fields often involve pathogen names, hosts, transmission routes, incubation periods, clinical classifications, drug names, dosages, courses of treatment, biosafety levels, and isolation measures. Units cover time (days, hours), dosage (mg, g, IU), concentration (mg/L), and temperature (°C).
Constraints Imposed by These Characteristics on Workflow Orchestration
The rapid update frequency of infectious disease protocol documents requires an efficient knowledge base synchronization mechanism within the workflow to ensure the timeliness of Q&A results. Diverse document formats and complex structures demand more robust document parsing, especially for extracting content from tables and figures. The richness of field types and units necessitates structured output for Q&A results; for example, drug dosages must include units. Due to the sensitive nature of infectious diseases, some protocol Q&A may involve sensitive information, requiring integration of access control or anonymization modules into the workflow. The workflow must handle cross-document knowledge correlation, such as extracting diagnostic criteria from one guideline and then retrieving the corresponding treatment process from another SOP.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 800–1200 characters | Ensures knowledge chunks contain sufficient context while avoiding excessive length that could reduce recall efficiency. Paragraphs in infectious disease protocols often contain many specialized terms and logical relationships. |
overlap_size | 100–200 characters | Maintains continuity between knowledge chunks, reducing semantic breaks caused by splitting. |
recall_top_k | 5–8 items | Moderately increases the number of recalled items, enhancing the probability of relevant knowledge being hit in complex queries, especially when protocol documents cover multiple related diseases or treatment pathways. |
rerank_top_n | 3–5 items | Reranks recall results to ensure the most relevant knowledge snippets are displayed first, improving Q&A accuracy. |
parse_timeout | 300 seconds | Addresses the parsing time for large PDF or Word documents, preventing document upload or processing failures due to timeouts, especially for guidelines containing many charts and complex layouts. |
max_tokens_output | 1024–2048 | Ensures the model has enough space to generate detailed answers, particularly when explaining complex diagnostic and treatment processes or listing multiple treatment options. |
##
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.