Data Characteristics
Documents related to respiratory system diseases, including regulations and Standard Operating Procedures (SOPs), originate from various sources. These sources include the National Health Commission, the National Medical Products Administration, internal regulations from medical institutions, and clinical practice guidelines from professional societies. Documents are typically in PDF, Word, or scanned image formats.
Update frequency varies. National-level laws, regulations, and clinical guidelines are usually revised annually or biennially. Internal SOPs within medical institutions may update quarterly or semi-annually, based on new guidelines or clinical feedback.
Document structure generally features clear chapter divisions, heading levels, and tables of contents. Common fields and units include diagnostic criteria, treatment plans, drug dosages (e.g., mg/kg, IU), examination indicators (e.g., PaO2, FEV1%), and time periods (e.g., hours, days, weeks). These fields often have strict numerical ranges and unit requirements.
Constraints Imposed by Data Characteristics on Workflow Orchestration
The update frequency and multiple sources of respiratory system regulation documents require the workflow to support flexible data source ingestion and version management. PDF and scanned images as primary document formats demand high accuracy in OCR and document parsing, especially for extracting critical data from tables and charts.
Specific medical terminology, abbreviations, and numerical units within documents necessitate specialized model support for semantic understanding and information extraction. The rigorous nature of regulations requires accuracy and traceability in question-answering results; errors could lead to medical risks. This mandates strong validation steps in workflow design. Precise understanding of numerical fields like drug dosages and examination indicators adds complexity to workflow logic for numerical comparisons and range evaluations.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
chunk_size | 500-800 characters | Balances semantic completeness and recall efficiency. Prevents excessive segmentation of regulation clauses. |
overlap_size | 50 characters | Ensures contextual continuity, especially for key information spanning paragraphs. |
max_tokens | 4096 tokens | Provides sufficient context for the model to process complex regulation clauses and multi-turn conversations. |
similarity_top_k | 5 | Increases the probability of recalling relevant regulation snippets. Balances recall quantity and computational resources. |
ocr_accuracy_mode | high-accuracy mode | Scanned documents and complex layouts are common. High-accuracy OCR mode improves text recognition rate and reduces errors. |
file_parse_timeout | 300 seconds | Large PDF files require longer parsing times. A longer timeout prevents parsing interruptions. |
Three Common Mistakes
- Symptom: After uploading a file via API, the workflow fails to recognize file content, returning "file content is empty" or "unable to parse file." Reason: The
filefield ormime_typewas not specified correctly during file upload, preventing the system from identifying the file type or content. - Symptom: When processing regulation documents containing tables, table data is misidentified or omitted. Reason: The default document parser has limited support for complex table structures. A specialized table recognition plugin was not enabled or configured.
- Symptom: When users ask about numerical ranges for drug dosages or examination indicators, the AI provides inaccurate answers or incorrect units. Reason: Numerical fields in the knowledge base were not extracted and stored structurally, preventing the model from performing precise numerical comparisons and unit conversions.
How to Verify Configuration
- Upload a respiratory system SOP document that includes complex tables and multi-page scanned images. Verify the workflow correctly parses and extracts key information.
- Query precise numerical ranges for drug dosages and examination indicators within the document. Check the accuracy of the AI's response.
- Simulate user queries about specific regulation clauses. Verify that the workflow recalls relevant passages completely and with logical context. Confirm the answer aligns with the original regulation text.
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.