Data Characteristics in Infection Control Management
Infection control management data primarily originates from internal hospital documents. These include regulations, standard operating procedures (SOPs), emergency plans, and training manuals. Documents are typically in PDF, Word, or scanned image formats, with varying degrees of structure. Core regulations, such as hand hygiene guidelines or isolation techniques, are relatively stable. However, new infectious diseases or policy changes can lead to quarterly or semi-annual updates of related details. Document structures often include a table of contents, chapter headings, body text, figures, and appendices. Fields and units are common, such as "disinfectant concentration (%)", "action time (minutes)", or "room negative pressure value (Pa)". This requires high precision in numerical values and unit recognition.
Constraints on Document Parsing and Chunking
The diverse sources of infection control management documents require parsers to support multiple file formats. The uncertain update frequency means the knowledge base needs incremental updates and version management. This avoids redundant parsing or missing the latest content. Common figures, charts, and scanned images in documents challenge OCR capabilities and image content extraction. Key information in images, such as flowcharts or statistical data, must be understood by the large language model. The specialized nature of fields and units requires careful attention during chunking to maintain the association between numerical values and units. This prevents splitting critical numerical-unit pairs, which impacts subsequent question-answering accuracy. Additionally, cross-references and appendices in long documents require parsing to maintain logical document integrity.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Infection control regulations are often lengthy, requiring sufficient context for complete understanding. |
Overlap Length | 100–200 characters | Ensures semantic continuity at chunk boundaries, especially where rule descriptions connect. |
File Type Whitelist | pdf, docx, doc, txt, xlsx | Covers common infection control document formats, supporting diverse sources. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses long parsing times for large or complex PDF files, preventing timeouts. |
maxContext | 2000 characters | Ensures sufficient context is provided after retrieval, covering complete operational steps or detailed regulations. |
OCR_ENABLED | true | Infection control documents frequently contain scanned images or pictures of procedures and statistical tables, requiring OCR. |
Common Pitfalls
- Parsing online document links, such as those from Yuque, results in an "unrecognized URL type" error. This usually occurs because the link requires public access or its internal rendering mechanism is incompatible with the parser, preventing direct HTML content retrieval.
- After document parsing, image content is not effectively transmitted to the large language model. This leads to missing critical image information in responses. The parser might only extract text, failing to convert images into understandable text descriptions or Base64 encoding.
- Parsing large PDF files results in tasks remaining in a "processing" state for an extended period, eventually failing due to timeout. This can be due to the file's large size, complex internal structure, or insufficient server resources to complete processing within
PARSE_FILE_TIMEOUT_SECONDS.
Verification Steps
- Upload an infection control regulation PDF file containing charts and scanned images. Check if the parsed chunks include image descriptions or OCR-identified text.
- For a document containing specific numerical values and units (e.g., "75% alcohol", "30 minutes"), check if the parsing keeps the numerical value and unit within the same chunk.
- Parse several infection control documents in different formats (Word, PDF). Compare parsing results with the original text to ensure all major sections and key information are accurately extracted.
- Test parsing a document with multi-level headings and a table of contents. Verify that chunking maintains the document's hierarchical structure and logical relationships.
The values provided are common starting points. Measure them against your 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.