HTTP Interface and External Systems for Smart Triage Regulations

Data Characteristics

Smart triage data primarily originates from internal medical institution regulations, treatment processes, Standard Operating Procedure (SOP) documents, and national/local medical laws. This data typically exists as unstructured text (e.g., PDF, Word documents) or semi-structured data (e.g., Excel spreadsheets, database records). The data update frequency is relatively low, usually occurring quarterly or annually in response to policy adjustments or internal process optimizations. Document structures mainly consist of chapters and articles, containing numerous specialized terms and cross-references. Fields include departments, disease types, symptom descriptions, diagnostic bases, treatment plans, contraindications, and precautions. Some fields may contain medical units (e.g., mg, ml, mmol/L).

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The low update frequency of smart triage data means initial data synchronization can use bulk import. Subsequent incremental updates require attention to version control and change management. The large volume of unstructured text necessitates robust document parsing capabilities from external systems to accurately extract key information and handle complex hierarchical relationships and cross-references. The presence of specialized terms and medical units demands high model comprehension, potentially requiring customized dictionaries or domain-specific model enhancements. The rigorous nature of regulatory documents makes answer accuracy critical, thus requiring high confidence evaluation and traceability capabilities from external systems. HTTP interface design must consider the stability of large file uploads and long text transfers, as well as data consistency during high-concurrency queries.

Configuration Guidelines

Common Pitfalls

• Calling the api/v1/chat/completions interface returns an HTTP 400 Bad Request error, with a message like invalid image format. This may occur when attempting to input non-text content (e.g., images) as plain text, or if the multimodal model is not correctly configured or enabled.
• After uploading a large regulatory document, parsing takes an extended time or results in a 504 Gateway Timeout error. This typically happens when the PARSE_FILE_TIMEOUT_SECONDS parameter is set too low, and file parsing does not complete within the allotted time.
• Answer results lack specific references to regulatory clauses or provide vague answers. This may be due to a low Recall count setting, preventing the model from acquiring sufficient relevant context, or excessively fragmented document segmentation, which compromises semantic integrity.

Verification of Configuration

• Upload representative regulatory documents of various types (PDF, Word). Verify that files are successfully parsed and included in the knowledge base. Check the knowledge base file list and status in the management interface.
• For complex multi-turn Q&A scenarios, simulate user queries. Check if the model accurately understands the intent and provides answers highly consistent with regulatory content.
• In the Q&A results, verify that cited original text snippets match the content of the original regulatory document and can be traced to specific chapters or clauses, confirming traceability.
• Test extreme cases, such as uploading very large files or documents containing many charts. Check if the system handles them stably and provides expected parsing results or error messages.

The values provided are common starting points and should be measured against specific 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.