HTTP Interface and External Systems for Ophthalmic

Data Characteristics in this Category

Ophthalmic pharmacovigilance data originates from clinical trial reports, real-world studies, spontaneous reports from physicians and patients, and post-market surveillance databases. Data updates frequently, especially during new drug launches or when new adverse event signals are identified. Updates can occur weekly or even daily. Document structures vary, including structured Case Report Forms (CRFs), unstructured free-text descriptions, medical imaging reports, and laboratory test results. Fields include patient demographics, medication history, adverse event (AE) descriptions, severity, outcome, causality assessment, and specific ophthalmic examination indicators such as visual acuity (VA), intraocular pressure (IOP), visual field (VF), and Optical Coherence Tomography (OCT) results. Units typically follow international medical standards, for example, LogMAR or Snellen fractions for visual acuity, and mmHg for intraocular pressure.

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

The high update frequency of ophthalmic pharmacovigilance data requires the HTTP interface to have efficient data synchronization mechanisms. This ensures FastGPT can timely acquire the latest adverse event information. Diverse document structures, particularly large volumes of unstructured text, challenge the text parsing capabilities of external systems. These systems must handle various ophthalmic professional terms and abbreviations. Specific ophthalmic examination indicators like VA and IOP require the interface to accurately transmit these numerical values with their specific units. FastGPT must then correctly parse and index this data to support fine-grained queries based on these indicators. Furthermore, transmitting patient privacy and sensitive medical information demands strict security, data encryption, and access control for the HTTP interface.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`HTTP_TIMEOUT_SECONDS`	`60 seconds`	Ophthalmic report data can be substantial. This timeout allows sufficient time for transmission and initial processing, preventing connection interruptions due to network latency or processing delays.
`MAX_PAYLOAD_SIZE_MB`	`20 MB`	A single adverse event report may include images or detailed text. This value accommodates most upload requirements.
`API_KEY_HEADER_NAME`	`X-FastGPT-API-Key`	Using a custom header field for authentication enhances security.
`PARSING_CONCURRENCY`	`5`	Balances data processing efficiency with system resource consumption, adapting to high-concurrency data updates.
`DATA_UPDATE_INTERVAL_HOURS`	`24 hours`	Balances data real-time requirements with external system load, ensuring at least one full or incremental synchronization daily.
`ERROR_RETRY_COUNT`	`3`	Addresses transient network fluctuations or temporary external system failures, improving data transmission robustness.

Common Pitfalls

HTTP requests return 400/401 status codes. The interface call fails, and logs show insufficient permissions or incorrect request format. This occurs when the API_KEY or Content-Type header field is not set correctly.
After uploading large ophthalmic image files, FastGPT receives incomplete data or fails to parse it. The file size does not match the original, or critical fields are missing. This happens when the MAX_PAYLOAD_SIZE_MB configuration is too small, causing the request to be truncated.
External systems calling the FastGPT interface to create applications or models receive a 500 error. System logs show internal processing exceptions. This occurs when the request's JSON structure does not conform to the FastGPT API's expectations, for example, missing required fields or mismatched field types.

Verification Steps

Check the status of the configured HTTP interface in the FastGPT management interface. Confirm it shows "Connected" with no abnormal alerts.
Manually trigger an HTTP data synchronization containing ophthalmic adverse event reports. Observe if new documents appear in the FastGPT knowledge base. Verify that key fields, such as visual acuity value and intraocular pressure value, are correctly parsed.
Simulate an external system creating a FastGPT application or model via the API. Check that the API returns a 200 status code and that the newly created item appears in the FastGPT application list.

Note: The values provided are common starting points. Measure 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.