Data Characteristics
Telemedicine regulation data originates from policies and regulations issued by national and local health commissions, internal rules and standard operating procedures (SOPs) from medical institutions, and relevant medical guidelines and expert consensuses. These documents are typically in PDF, Word, or structured text formats. Update frequency is relatively low, usually quarterly or annually, with temporary revisions for major policy changes. Document structures commonly include policy background, scope, specific clauses, operating steps, and responsibility assignments. Fields and units involve medical service item codes, fee standards, approval process nodes, time limits (e.g., 审批时限 in Working Day), and qualification requirements (e.g., 执业医师资格证编号).
Constraints on Deployment and Upgrade
The low update frequency of telemedicine regulation documents allows for thorough text preprocessing and vectorization during initial knowledge base construction. Subsequent incremental updates require attention to version control and precise identification of revised content. The structured nature of documents means that knowledge chunking should prioritize semantic boundaries like chapters and clauses, avoiding splitting key information. The extensive use of specialized terminology and codes requires the model to have a strong understanding of medical vocabulary and to distinguish between synonyms and near-synonyms. Fields for time limits and process nodes require the system to parse and provide clear values or steps, which demands high recall accuracy and precise answer generation logic from the knowledge base. The authoritative and rigorous nature of these documents necessitates emphasizing original sources and policy bases in question-answering results to prevent misleading information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters | Adapts to chapter and clause granularity in policy documents, maintaining semantic integrity. |
Chunk Overlap Length (Overlap Length) | 150 characters | Ensures context continuity across chunks, improving recall accuracy. |
Recall count (Recall Count) | 8–12 items | Covers multiple relevant clauses and details potentially involved in regulation Q&A. |
Similarity threshold (Similarity Threshold) | Calibrate by testing | Ensures precision of recalled content, excluding irrelevant clauses. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accommodates parsing time for large regulation documents or SOPs. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Accommodates file sizes of large policy texts or operation manuals. |
Common Mistakes
- Knowledge base document chunks exceed length limits after splitting by delimiters, leading to missed key information during Q&A. This occurs when
Chunk sizeis not configured appropriately or multi-strategy chunking is not used. - API Key fails after upgrading to a new version, resulting in an
Invalid API Keyerror. This happens because old versiongeneral keysmay need regeneration or configuration asapplication-specific keysin the new version. - Q&A results contain errors in process steps or time limits. This occurs when the knowledge base fails to correctly identify and extract numbers and units from text during document processing, or does not store this information in a structured format.
Verification Steps
- Upload a telemedicine SOP document with complex processes and multiple chapters. Check if the knowledge base correctly chunks the document and if chunk content maintains semantic integrity.
- Use questions containing medical service codes or specific time limits. Verify that the system's answers accurately mention relevant codes and time units and align with the original text.
- Simulate a version upgrade. Verify that existing knowledge base and application configurations run smoothly in the new version, especially API calls and knowledge retrieval functions.
The values provided are common starting points and 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.