HTTP API and External Systems for Clinical Decision Support

Data Characteristics

Clinical Decision Support (CDS) regulation data originates from internal medical institution rules, Standard Operating Procedures (SOPs), treatment guidelines, drug inserts, and various medical literature. This data typically exists as unstructured documents (e.g., PDFs, Word documents) or semi-structured data (e.g., JSON, XML drug inserts). Update frequency is relatively stable. However, concentrated updates can occur when regulations or guidelines are revised, or new drugs are launched. Document content is rigorous, containing extensive professional terminology, abbreviations, and units of measurement, such as drug dosage (mg/kg), treatment duration (days - days), and laboratory indicator ranges (mmol/L). Documents often include cross-references and version control information, emphasizing timeliness and authority.

Constraints on HTTP API and External Systems

The rigor of CDS regulation data requires HTTP APIs to have high reliability and error handling mechanisms to ensure data transmission integrity. The nature of unstructured documents means that after upload via HTTP API, robust parsing capabilities are necessary to extract key information and standardize professional terminology. The stable update frequency allows for incremental update strategies in API design, reducing resource consumption from full synchronization. Cross-references and version control information in documents require retaining this metadata during data indexing to provide traceability in question-answering. Accurate identification and conversion of units of measurement are critical to avoid clinical misjudgments; APIs must support unit parsing and validation during data processing to prevent errors caused by unit confusion. For large file uploads, support for chunked uploads and resume capabilities is necessary to handle transmission interruptions.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Regulation documents can include many charts and attachments, requiring support for larger file uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents is complex, requiring a longer timeout to prevent parsing interruptions.
`maxContext`	`3000 characters`	CDS regulation clauses are long and logically rigorous, requiring more context for accurate understanding.
`Chunk size`	`800-1200 characters`	Ensures a single segment contains complete regulation clauses or logical units, avoiding semantic fragmentation.
`Recall count`	`Top 10 entries`	Regulation Q&A demands high accuracy; increasing recall count improves the hit rate of relevant documents.
`Similarity threshold`	`0.75-0.85`	Strictly controls similarity to ensure highly relevant recall results and reduce the risk of incorrect answers.

Common Pitfalls

The HTTP API returns 400 Bad Request with the message Multimodal file size is too large. This occurs when the uploaded regulation document size exceeds the UPLOAD_FILE_MAX_SIZE parameter limit.
After an API call, MongoDB Change Streams disconnect and fail to reconnect. This happens when the MongoDB replica set primary node shifts, and the old connection is not updated promptly.
Measurement unit errors or omissions appear in Q&A results. This occurs when the document parsing fails to accurately identify or standardize professional measurement units in the regulation document.

How to Verify Configuration

Upload multiple typical regulation documents of different sizes and formats (e.g., PDF, DOCX) via the HTTP API. Check if files are successfully parsed and stored, and observe if PARSE_FILE_TIMEOUT_SECONDS is sufficient.
For uploaded regulation documents, construct complex questions containing professional terminology and units of measurement. Verify the accuracy and completeness of Q&A results, and check if measurement units are correct.
Simulate a MongoDB replica set primary node switch. Observe if the system automatically reconnects and continues processing data streams, ensuring continuous data synchronization.

The values given 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.