Data Characteristics
Rare disease regulations and Standard Operating Procedure (SOP) data originates from official documents published by national, provincial, and international medical organizations. These documents include, but are not limited to, rare disease catalogs, diagnostic and treatment guidelines, medication specifications, medical insurance policy details, and clinical research ethics approval processes. Data update frequency is relatively low, typically annually, or specifically updated during significant policy changes. Document formats are primarily PDF policy texts, Word clinical pathways, or Excel drug lists, with varying degrees of structure. Core fields include disease name, ICD code, drug name, indications, reimbursement ratio, approval process nodes, effective date, and expiration date. Disease and drug names may include a mix of Chinese and English or aliases. Dosage units often involve milligrams (mg), micrograms (μg), and milliliters (mL), requiring high numerical precision.
Constraints Imposed by These Characteristics on "Model Integration and Configuration"
The low update frequency of rare disease regulations and SOP data means that once a model is trained or fine-tuned, its knowledge remains valid for an extended period, eliminating the need for frequent full updates. Diverse document formats and varying degrees of structure challenge document parsing and preprocessing modules. These modules must effectively extract table information from PDFs, multi-level heading structures from Word documents, and cell data from Excel files, then clean and standardize this information. The mixed use of Chinese and English, along with aliases for disease and drug names, requires configuring a synonym dictionary or entity linking component during model integration to ensure recall accuracy. Dosage unit and numerical precision requirements necessitate that the model accurately restates or calculates values during answer generation, paying attention to unit correctness to avoid misinformation due to unit confusion. Due to the serious nature of policy documents, model answers require higher accuracy and traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale
Data Characteristics for this Category
Rare disease regulations and SOP data primarily come from official documents issued by national, provincial, and international medical organizations. These include, but are not limited to, "Rare Disease Catalogs," diagnosis and treatment guidelines, medication specifications, medical insurance policy details, and clinical research ethics approval processes. The data update frequency is relatively low, typically on an annual basis, or specifically updated during major policy adjustments. Document formats are mainly PDF policy texts, Word clinical pathways, or Excel drug lists, with varying degrees of structure. Core fields include disease name, ICD code, drug name, indications, reimbursement ratio, approval process nodes, effective date, and expiration date. Disease and drug names may involve a mix of Chinese and English or aliases. Dosage units often include milligrams (mg), micrograms (μg), and milliliters (mL), and require high numerical precision.
Constraints from These Characteristics on "Model Integration and Configuration"
The low update frequency of rare disease regulations and SOP data means that once a model is trained or fine-tuned, its knowledge can remain effective for a long time, eliminating the need for frequent full updates. Diverse document formats and varying degrees of structure challenge document parsing and preprocessing modules. These modules must effectively extract table information from PDFs, multi-level heading structures from Word documents, and cell data from Excel files, then clean and standardize this information. The mixed use of Chinese and English, along with aliases for disease and drug names, requires configuring a synonym dictionary or entity linking component during model integration to ensure recall accuracy. Dosage unit and numerical precision requirements necessitate that the model accurately restates or calculates values during answer generation, paying attention to unit correctness to avoid misinformation due to unit confusion. Due to the serious nature of policy documents, model answers require higher accuracy and traceability.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Policy document paragraphs are long; ensures contextual completeness. |
Recall count (Recall Count) | top 8 items | Ensures coverage of relevant policy provisions, improving recall rate. |
Similarity threshold (Similarity Threshold) | 0.78 | Balances precise matching with semantic relevance, avoiding irrelevant information interference. |
Rerank result count (Rerank Return Count) | 5 items | Filters for the most relevant items, reducing the model's processing burden. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Allows sufficient parsing time for large PDF or Word files. |
maxContext | 16384 | Handles lengthy policy documents, ensuring |
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.