HTTP Interface and External Systems for Real-World Study

Data Characteristics

Real-World Study (RWS) registration and declaration data originates from multiple sources. These include electronic health record systems from multi-center, long-term follow-up medical institutions, patient-reported outcome (PRO) databases, national medical insurance databases, and disease registries. Data update frequencies vary from daily, weekly, monthly, to quarterly, depending on the study protocol and data source. Document structures are complex. They contain unstructured clinical notes, semi-structured medical record summaries, and highly structured Case Report Form (CRF) data. Field types are diverse, covering patient demographics, diagnoses, treatment plans, laboratory results, imaging reports, and adverse events. This data includes extensive medical terminology, abbreviations, and specific coding systems like ICD-10 and SNOMED CT. Units must adhere to clinical standards, for example, blood pressure in mmHg, blood glucose in mmol/L, and drug dosage in mg/kg.

Constraints on HTTP Interfaces and External Systems

RWS data's multi-source and heterogeneous nature requires HTTP interfaces with robust data integration capabilities. These interfaces must handle API requests from various databases and systems. The presence of unstructured and semi-structured data means large text blocks need transmission via POST requests, potentially involving JSON or XML body formats. Uncertain update frequencies demand real-time and stable interfaces, possibly requiring Webhook callback mechanisms. Standardization of medical terminology and coding restricts data transmission formats. Interfaces must support custom data mapping rules and explicitly specify encoding standards in request headers or bodies. Furthermore, bulk import and incremental updates of large historical datasets challenge interface concurrency and timeout settings.

Configuration Guide

Configuration Item	Recommended Value	Rationale
`AIPROXY_API_ENDPOINT`	`http://your-ai-proxy-service:port/v1/chat/completions`	Points to an internal AI proxy service for unified authentication and routing, preventing direct exposure of external AI service interfaces.
`AIPROXY_API_TOKEN`	`your_long_generated_token`	Used for AI proxy service authentication, ensuring the security of internal API calls.
`HTTP_REQUEST_TIMEOUT_SECONDS`	`600` seconds	RWS data processing may involve complex text parsing and large data transfers, requiring a longer timeout.
`MAX_BODY_SIZE_MB`	`200` MB	Unstructured data like clinical reports and medical record summaries can be large, requiring support for large request bodies.
`RETRY_ATTEMPTS`	`3` times	Given the instability of multi-source data interfaces, an appropriate retry mechanism improves data retrieval success rates.
`CONTENT_TYPE_HEADER`	`application/json; charset=UTF-8`	Ensures correct encoding when transmitting data containing medical terminology and multilingual characters.

Common Pitfalls

HTTP requests return 401 or 403 error codes, indicating authentication failure. This usually results from an incorrect or expired AIPROXY_API_TOKEN configuration.
HTTP request body parameters in workflows fail to generate dynamically. Variables in the request body remain fixed values. This occurs due to incorrect workflow variable syntax or when the variable scope does not cover the HTTP request node.
Frequent connection timeouts appear during interface calls, with a 504 Gateway Timeout status code. This may be due to HTTP_REQUEST_TIMEOUT_SECONDS being set too short, or the backend service taking too long to process RWS data.

Verification Steps

In the FastGPT console workflow, create a simple HTTP GET request to a known accessible public API. Verify successful data retrieval and a 200 status code.
After configuring AIPROXY_API_ENDPOINT and AIPROXY_API_TOKEN, attempt to call an AI service that requires authentication. Confirm successful AI response retrieval. Check the AI proxy service logs to confirm correct request forwarding.
Construct an HTTP POST request containing a large file or complex JSON structure. Send it via the workflow. Verify the request sends successfully without triggering the MAX_BODY_SIZE_MB limit. Confirm the receiving end fully receives the data.

The values provided are common starting points. Measure them 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.