Data Characteristics
Health management quality documents typically originate from various sources. These include physical examination reports, health assessment questionnaires, diagnostic and treatment records, chronic disease management plans, and compliance review documents. Document update frequencies vary. For example, physical examination reports might update annually, while chronic disease management plans could update in real-time based on condition changes.
Document structures often contain both structured data (e.g., physiological indicators, medication dosages) and unstructured text (e.g., doctor's diagnoses, health advice). Fields and units are standardized for physiological indicators like blood pressure (mmHg), blood glucose (mmol/L), and weight (kg). Health advice or risk assessments are usually described in natural language. Common file formats include PDF, Word, and structured JSON.
Constraints on Workflow Orchestration
The data characteristics of health management quality documents impose specific requirements on workflow orchestration.
First, diverse data sources necessitate pre-processing capabilities that support multiple file formats. The system must effectively extract key structured information from unstructured text.
Second, some documents update frequently. This requires incremental update and version management capabilities within the workflow to ensure knowledge base timeliness.
Third, the precision and unit consistency of health indicators demand strict data extraction and validation. This prevents misinterpretation due to unit confusion.
Finally, privacy compliance requires anonymization or de-identification of sensitive information during document processing. Access control must also be considered during knowledge base construction.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 characters | Covers the context length required for most health management document queries, balancing recall efficiency. |
Chunk size | 500 characters | Ensures each segment contains sufficient semantic information, avoiding redundancy from excessive length. |
Recall count | Top 8 | Balances recall breadth and processing efficiency, covering common query scenarios. |
Similarity threshold | 0.78 | Ensures high relevance of recalled content to the query intent, reducing interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 120 seconds | The time required to parse most large health report files; adjust based on actual file size. |
LLM_MODEL_NAME | gpt-4o | Ensures accuracy in understanding complex medical terminology and providing professional responses. |
Common Mistakes
- Empty or inaccurate knowledge base retrieval results after document upload. This occurs when document parsing fails or the segmentation strategy is inappropriate, leading to incorrect indexing of key information.
- Missing or incorrectly formatted data fields in API workflow responses. This likely indicates an incomplete workflow output node configuration, failing to map expected processing results to the API response.
- Workflow execution timeouts or interruptions. This often happens when processing large files if the
PARSE_FILE_TIMEOUT_SECONDSparameter is set too low, causing the system to terminate file parsing prematurely.
Verification Steps
- Upload health management documents in various formats (e.g., PDF, Word). Verify that the knowledge base successfully indexes them and that keyword searches retrieve the original document content.
- Simulate typical health consultation scenarios. Query the workflow and check if the returned results include key physiological indicators, diagnostic advice, and other information. Validate their accuracy.
- Call the workflow via API. Compare the returned JSON structure with expectations. Check if all required fields are present and data types are correct.
- Randomly select several segments from the knowledge base. Check if their content is complete and semantically coherent. Evaluate if the segmentation quality meets retrieval requirements.
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.