HTTP Interface and External Systems for Ophthalmology

Data Characteristics in this Category

Ophthalmology registration and declaration data are typically highly structured. This data primarily originates from clinical trial reports, non-clinical study reports, manufacturing process documents, and pharmaceutical research data. The update frequency for this data is relatively low, focusing on interim R&D results and post-regulatory review feedback. Document formats are diverse, including PDF clinical study summaries, Word expert opinions, Excel statistical data tables, and specific XML or HL7 electronic submission files. Data fields cover patient demographics, disease diagnostic codes (e.g., ICD-10 H series), drug dosage and administration routes, vision examination results (e.g., Snellen acuity, ETDRS letter count), intraocular pressure (IOP) measurements (in mmHg), OCT imaging reports, and adverse event codes (e.g., MedDRA terms). Some data involves specialized medical imaging features and biomarkers.

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

The highly structured nature of ophthalmology registration and declaration data requires HTTP interfaces to strictly adhere to predefined data models and field specifications during data transmission. Unstructured documents like PDFs and Word files necessitate efficient content extraction and parsing capabilities, which limits external system choices for content recognition and semantic understanding. The low data update frequency means real-time requirements for interface design are not high, but data completeness and version control are critical. Special data types, such as medical images and biomarkers, may require binary data transfer through file upload interfaces and rely on external specialized image processing services for analysis, increasing interface call complexity. Standardized terminology like disease diagnostic codes and adverse event codes require interfaces to integrate with external medical terminology services for validation and conversion, ensuring data accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`maxContext`	`20000 characters`	Accommodates lengthy background information descriptions found in ophthalmology clinical reports.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Ensures sufficient time for parsing large PDF or complex Word documents containing intricate diagrams.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Covers submission files that include high-resolution medical images or numerous attachments.
`similarity_threshold`	`0.85`	Increases the accuracy requirement for matching ophthalmology terminology and clinical details.
`rerank_top_n`	`10 items`	Provides enough relevant passages for the reranking model to perform fine-grained sorting after initial screening.
`document_parser_type`	`PDF_OCR_AND_LAYOUT`	Ensures accurate text and table extraction from scanned documents and complex layouts.

Common Pitfalls

Calling an external reranking model returns empty content. The rerank_result field appears empty. This can happen if the text length transmitted to the reranking model exceeds its processing limit or if the provided token is invalid.
Uploaded files are not correctly embedded into the knowledge base. Relevant content cannot be found in the knowledge base. This can happen if the file size exceeds the UPLOAD_FILE_MAX_SIZE limit, causing the file upload to fail or be truncated.
HTTP interface calls to external medical terminology services result in a 500 error. Logs show HTTP Status Code 500. This can happen if the disease diagnostic code format in the request body does not conform to service requirements or if necessary authentication information is missing.

Verification Steps

Upload a PDF document containing ophthalmology clinical trial data. Check if the knowledge base accurately extracts and indexes key vision and IOP data fields.
Use an API call to input typical ophthalmology disease symptom descriptions. Check if the retrieval results include relevant clinical research reports and treatment plans.
Simulate an external system sending a request to the interface with an ICD-10 H series disease code. Check if the interface successfully calls the external terminology service and returns correct conversion or validation results.

Note: 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.