Data Characteristics in this Category
Health management quality document data comes from multiple sources. These include health assessment reports, personalized intervention plans, health education materials, follow-up records, and compliance audit files. Document update frequencies vary. Assessment reports and follow-up records may update quarterly or annually. Health education materials update when policies or medical guidelines change. Document structure varies. Assessment reports often contain structured physical examination data, questionnaire results, and doctor's diagnostic advice. Intervention plans combine structured indicators with unstructured text descriptions. Fields and units involve physiological indicators like blood pressure (mmHg), blood glucose (mmol/L), and Body Mass Index (BMI). They also include intervention measures such as medication dosage and exercise duration (minutes). Data types are diverse and often include medical terminology and abbreviations.
Constraints Imposed by these Characteristics on Knowledge Base Retrieval and Recall
The varying update frequency of health management documents requires the knowledge base to support incremental updates and version management. This ensures the timeliness of retrieval results. The coexistence of structured and unstructured data means a single text chunking strategy is insufficient for efficient recall. It requires combining semantic understanding with field recognition. For example, keywords alone may not distinguish between "dietary advice for hypertensive patients" and "dietary advice for diabetic patients," even if both contain "dietary advice." The specialized nature of fields and units requires vector models to accurately identify medical terminology. This avoids recall bias due to synonyms or abbreviations. Key information scattered in long documents can lead to context loss with traditional chunking methods, affecting recall accuracy. Additionally, user queries often contain multi-dimensional information, such as "find a patient's blood glucose fluctuations and corresponding dietary adjustments over the past year." This requires the knowledge base to handle complex queries and perform multi-document association.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size (Chunk Length) | 500–800 characters | Balances context completeness with vector embedding efficiency. Suitable for documents with mixed text and structured data. |
Chunk Overlap Length (Chunk Overlap Length) | 100–150 characters | Ensures contextual continuity at chunk boundaries, reducing the risk of critical information being split. |
Recall count (Recall Count) | Top 8–12 items | Increases recall coverage, capturing more potentially relevant document segments. Provides sufficient candidates for reranking. |
Similarity threshold (Similarity Threshold) | 0.78–0.82 | Empirical value. Balances recall precision and recall rate. Prevents low-relevance documents from entering subsequent processing. |
Rerank result count (Rerank Return Count) | Top 3–5 items | Focuses on highly relevant results most likely needed by the user. Reduces the processing burden on subsequent models. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles large health assessment reports or follow-up record files. Prevents parsing timeouts. |
Three Common Mistakes
- After uploading to the knowledge base, when users query specific health indicators (e.g., "fasting blood glucose"), the retrieval results frequently show document segments of other unrelated physiological indicators. The chunking strategy is too general. It fails to semantically recognize and independently chunk structured data, leading to confusion between indicator data and unrelated descriptions.
- When users query historical health management plans, the retrieved content is too long. This causes the reranking model to not be effectively invoked, resulting in poor document sorting. The recalled raw document segments exceed the reranking model's processing length limit, or the reranking configuration is not correctly enabled.
- When attempting to precisely extract specific fields (e.g., "recommended exercise duration") from health management plans via workflow, the returned results are empty or inaccurate. The knowledge base's chunking fails to preserve the correspondence between field names and field values. Alternatively, the prompt in the workflow does not clearly instruct the AI model to identify specific fields and their units.
How to Confirm Correct Configuration
- Upload a batch of health assessment reports containing structured data. Randomly select different types of queries. Observe whether the recall results accurately identify and return specific physiological indicator values and their corresponding descriptions.
- Query a long document containing a multi-stage health management plan. Check if the returned recall items cover the key stages of the plan. Confirm whether the reranked results' order meets expectations through user feedback or manual evaluation.
- Design test queries containing specific fields (e.g., "target weight," "medication dosage"). Observe if the workflow can precisely extract these field values. Verify the completeness and accuracy of the extracted results.
- Simulate concurrent query scenarios. Monitor the knowledge base's response time and resource utilization. This ensures no parsing timeouts or performance bottlenecks occur in actual use.
The values provided are common starting points. They 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.