Data Characteristics
Infectious disease protocols and Standard Operating Procedures (SOPs) originate from regulations, treatment guidelines, and prevention plans issued by national health commissions and disease control centers. They also include infection control manuals, emergency plans, and operational details developed by medical institutions. These documents typically update annually or undergo temporary revisions based on epidemic changes. They are primarily in PDF and Word formats, containing extensive medical terminology, abbreviations, drug names, and pathogen names. Specific fields and units often involve biosafety levels (e.g., BSL-2, BSL-3), microbial culture times (e.g., hours, days), drug concentrations (e.g., mg/L, μg/mL), isolation periods (e.g., days, weeks), and various diagnostic indicator thresholds.
Constraints on Vector Models and Indexing
The specialized nature and high information density of infectious disease protocol documents demand advanced semantic understanding from vector models. Generic models may struggle to accurately capture the deep meaning of medical terms. The moderate update frequency, coupled with the possibility of temporary revisions, requires the indexing system to support efficient incremental updates and version management. Nested tables, images, and complex layouts in PDF and Word formats pose challenges for text extraction and segmentation, potentially leading to critical information loss or fragmented context. Quantitative information, such as biosafety levels and drug dosages, must retain its numerical or range semantics during vectorization. This avoids losing critical decision-making criteria if only using a bag-of-words model. Vector models must differentiate between similar but semantically distinct professional terms and recognize specific numerical ranges.
Configuration Guidelines
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk Size | 800–1200 characters | Balances contextual completeness with vector model processing efficiency, preventing excessively long chunks from diluting key information. |
Recall Count | Top 5–8 entries | Ensures coverage of relevant protocol clauses while controlling retrieval burden and avoiding interference from irrelevant results. |
Similarity Threshold | Calibrated by actual measurement | Adjusts based on actual question-answering effectiveness, balancing precision and recall. Typically 0.7–0.85. |
Rerank Return Count | 3 entries | Further refines the most relevant segments from the recalled results, optimizing final answer quality. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large PDF or Word files, especially those with complex tables and charts. |
Embedding Model | BGE-M3 or text-embedding-ada-002 | Adapts to medical terminology and long text comprehension. Prioritize domestic models. |
Common Pitfalls
- Symptom: System returns answers with incorrect or missing critical drug dosages or isolation times. Cause: During document parsing, numerical values in tables or phrases with units are incorrectly segmented or ignored, leading to incomplete semantics during vectorization.
- Symptom: Querying for a specific disease's SOP returns an unrelated general infection control guideline. Cause: The vector model lacks sufficient discrimination for specialized terms, treating document segments with similar meaning but different application scenarios as highly similar.
- Symptom: Newly published policy content is not retrievable, or retrieval results remain on old versions for extended periods. Cause: The index update mechanism does not trigger in time or incremental update configuration is incorrect, leading to the vector database being out of sync with the latest documents.
Verification Steps
- Select a batch of test questions containing specialized terms, numerical units, and complex sentences. Verify if the model accurately recalls relevant document segments and assess the completeness of the recalled segments.
- Ask targeted questions about recently published protocols or revised SOPs. Confirm the system retrieves the latest content and verify information timeliness by comparison.
- Randomly extract key numerical information from documents, such as biosafety levels or drug concentrations. Construct questions and verify if the system provides precise values or ranges, and check for consistency with the original text.
- Through the FastGPT backend's "Knowledge Base Management" interface, check if newly added or updated documents are successfully chunked and vectorized. Observe if the chunked content is reasonable.
The values provided are common starting points and should be measured against the reader's 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.