Data Characteristics
Smart triage regulations data primarily originates from internal hospital documents. These include regulations, operational procedures, medical service guidelines, departmental responsibility documents, and various diagnostic and treatment protocols. These documents have a relatively stable update frequency, typically revised annually or updated periodically based on policy changes.
Document structures are predominantly unstructured text, such as PDF regulation files or Word SOP documents. They may contain numerous tables, images, and flowcharts. Text content fields include, but are not limited to: regulation number, publication date, effective date, scope of application, responsible department, specific procedural steps, risk warnings, and exception handling. Common units include time units like "days," "hours," "minutes," and process-related quantity units like "times" or "cases."
Constraints on Knowledge Base Retrieval and Recall
The characteristics of smart triage regulation data impose several constraints on knowledge base retrieval and recall. First, regulation documents are often lengthy and logically rigorous. An overly short segment might lead to a loss of context, affecting semantic understanding. Second, information like regulation numbers and dates requires precise matching during retrieval; fuzzy matching can introduce irrelevant results. Third, the relatively low update frequency means the knowledge base must support version management to ensure retrieval of the latest effective version. Additionally, procedural steps and conditional judgments within regulations require the retrieval system to understand complex logical relationships and accurately extract key information. Finally, the unstructured nature of regulation documents, especially the presence of tables and flowcharts, demands higher requirements for text extraction and segmentation strategies to avoid information loss or misalignment.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Regulation documents are logically dense. Longer contexts maintain semantic integrity and prevent truncation of critical information. |
Chunk overlap (Segment Overlap) | 100 characters | Ensures sufficient overlap between adjacent paragraphs to handle cross-paragraph queries and reduce information loss. |
Recall count (Recall Count) | Top 5–8 entries | Given the complexity of regulatory content, increasing the recall count helps cover more relevant clauses and improves hit rates. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Regulation Q&A demands high accuracy. Raising the threshold filters out low-relevance results and reduces noise. |
Rerank result count (Reranked Return Count) | Top 3 entries | After reranking, the top few results typically have the highest accuracy and relevance, so they are prioritized for display. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Regulation files can be large, requiring longer parsing times. Sufficient timeout prevents parsing failures. |
Common Pitfalls
- Symptom: When a user asks about a regulation's update cycle, the system returns outdated regulation content. Reason: The knowledge base was not updated promptly, or document version management was not enabled, leading to retrieval of expired data.
- Symptom: An uploaded regulation file contains images or complex tables, but the retrieval results do not include text from images, or table content is parsed incorrectly. Reason: The document parser lacks sufficient support for non-text content (e.g., image OCR, table structure recognition), causing critical information to be inadequately ingested.
- Symptom: A user asks, "What is the approval process for regulation XX?", and the system's answer is fragmented, failing to form a complete process. Reason: The
Chunk size(Segment Length) setting is too short, causing a complete process description to be split across multiple segments, preventing aggregation of full information during retrieval.
Verification Steps
- Select several typical questions, including queries about regulation clauses, procedural steps, and responsible departments. Verify that the recall results include correct and complete regulation entries.
- Upload two versions (old and new) of the same regulation file. Query relevant content and confirm the system prioritizes recalling the latest effective version.
- Test regulation files containing complex tables or flowcharts. Verify that retrieval results accurately extract key data from tables or process descriptions.
- Simulate user queries for precise information such as regulation numbers or dates. Check that retrieval results accurately match and return the corresponding regulation files or segments.
The values provided are common starting points and should be measured 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.