Dermatology Quality Documentation: HTTP API and External

Data Characteristics

Dermatology quality documentation typically includes disease diagnosis and treatment guidelines, drug usage instructions, clinical pathways, operational standards, case reports, and research literature. Data sources are diverse, encompassing authoritative medical journals (domestic and international), drug regulatory approvals, internal hospital regulations, and expert consensus. Update frequency varies from weeks to months, influenced by policies, new drug releases, and clinical research advancements. Document structures are primarily unstructured text, often containing images, tables, and charts. Fields such as disease names, drug ingredients, dosage units (e.g., mg/kg, IU), treatment cycles (e.g., courses of treatment, Day), adverse reactions, and contraindications are highly specialized and precise.

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

The unstructured nature of dermatology documents requires the API to have robust text parsing capabilities, handling various formats (e.g., PDF, DOCX) and accurately extracting key information. The uncertain document update frequency means external systems must support incremental synchronization and version management to avoid redundant processing and data duplication. Specialized fields with units (e.g., 10 mg/kg) must maintain semantic integrity during data extraction to prevent information distortion due to missing units or parsing errors. Furthermore, common images and tabular data in documents demand higher requirements for API file uploads and content recognition, ensuring text within images is recognized and table structures are preserved. Stability in handling large file uploads and processing is also a critical consideration for API performance.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Dermatology documents often contain numerous images and charts, making individual files potentially large. This ensures successful uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large files takes considerable time. Increasing the timeout prevents parsing interruptions.
`maxContext`	`8000 Token`	Dermatology guidelines and literature are often lengthy, requiring a larger context window to maintain information completeness.
`Chunk size`	`500 characters`	Ensures each segment contains sufficient semantic information while avoiding excessive length that could hinder parsing.
`Similarity threshold`	`0.75`	Dermatology terminology is highly specialized. A higher threshold helps retrieve more precise and relevant document snippets.
`API_CORS_ORIGINS`	Calibrate based on actual measurements	External systems often encounter cross-origin issues when calling APIs from the frontend. Configure allowed origin domains.

Common Pitfalls

Symptom: The frontend console displays a CORS error when an external system calls the /api/v1/chat/completions API. Reason: The backend API lacks proper Access-Control-Allow-Origin response header configuration, disallowing cross-origin requests from the frontend domain.
Symptom: Uploading files with special characters (e.g., /, :) results in a 400 Bad Request error. Reason: The filename was not URL-encoded, or the backend file storage system has strict character restrictions for filenames.
Symptom: After uploading a PDF document via the API, some tabular data is not correctly recognized or parsed. Reason: The document parser has insufficient recognition capabilities for complex table structures or embedded tables within images, leading to information loss.

Verification Steps

Use Postman or a similar tool to simulate an external system calling the /api/v1/file/upload API. Upload a dermatology PDF document of approximately 200 MB. Check if the returned status code is 200 OK and confirm the file is successfully stored.
Call the /api/v1/chat/completions API, submitting a query containing dermatology-specific terminology (e.g., Psoriasis, Glucocorticoid). Check if the returned results include relevant document snippets and evaluate their relevance.
Review server logs to confirm that no timeout or out of memory error messages occurred during large file upload and parsing processes.

Note: The values provided are common starting points. Measure against specific samples to determine optimal configurations.

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.