HTTP Interface and External Systems for Phase I Clinical

Data Characteristics

Phase I clinical study quality documents originate from Clinical Research Organizations (CROs) or the sponsor's internal clinical operations departments. These documents have a relatively fixed update frequency, typically updated at project milestones (e.g., protocol finalization, first patient dose, safety report submission). Document structures primarily use standardized templates, such as clinical trial protocols, investigator brochures, informed consent forms, and case report forms (CRFs). Data fields within CRFs are particularly critical, containing subject vital signs, laboratory test results, and adverse event records. Units strictly adhere to international standards (e.g., SI units), and numerical ranges are tightly constrained. These documents often exist as PDFs, Word files, or structured data files (e.g., CDISC ODM XML).

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

The standardized templates and fixed update rhythm of Phase I clinical quality documents necessitate that HTTP interface design prioritizes data structure consistency. The strictness of numerical fields and units in documents requires precise type validation and unit conversion during data extraction and transmission to prevent data distortion. Since document updates are not real-time, interface call frequency is typically low. However, updates may involve syncing a large number of files, so the interface needs batch processing capabilities and extended timeout settings. Due to sensitive clinical data, external system integration demands high data security and access control, requiring support for token authentication and encrypted transmission. The complex document structure also requires clear metadata and file indexing in interface responses to facilitate parsing and association by downstream systems.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`300 seconds`	Phase I clinical documents can contain many images and complex tables, making file transfer and parsing time-consuming.
`MAX_FILE_SIZE_MB`	`200 MB`	Single clinical study protocols or investigator brochures often reach tens of MBs; sufficient upper limits are necessary.
`BATCH_UPLOAD_LIMIT`	`20 items`	Periodic updates in clinical trials may involve multiple documents; batch uploading improves efficiency.
`API_KEY_HEADER_NAME`	`X-API-Key`	Follows common industry practice for external system authentication.
`PARSE_FILE_TYPE_LIST`	`pdf, docx, xml`	Covers major Phase I clinical document formats, ensuring comprehensive parsing.
`RETRY_COUNT_ON_FAILURE`	`3 times`	Automatic retries improve success rates during network fluctuations or transient external system failures.

Common Pitfalls

The external system returns an HTTP 504 Gateway Timeout error. This occurs because the backend service does not respond within the specified time when the interface processes large clinical documents (e.g., PDFs with images).
RAG recall results show unit confusion or numerical errors in critical drug dosages or adverse event rates. This happens because the external system data source does not strictly validate units and convert types for fields.
The external system interface is configured, but the document list cannot be retrieved, and logs show HTTP 401 Unauthorized. This typically results from incorrect API_KEY_HEADER_NAME or API_KEY_VALUE configuration, leading to authentication failure.

Verification Steps

Use FastGPT's interface testing function to upload a typical Phase I clinical protocol PDF file. Verify that it parses correctly, generates vector data, and that the response time is acceptable.
After integrating the external system, attempt to pull a Case Report Form (CRF) XML file containing numerical data from the external system. Verify that extracted key fields (e.g., subject weight, complete blood count indicators) match the original data, paying close attention to numerical and unit accuracy.
Simulate a batch document update operation triggered by the external system. Observe whether FastGPT receives and processes all new and old documents. Check logs for any errors or warnings.
After configuration, randomly select several Phase I clinical documents from the FastGPT knowledge base. Verify the accuracy of content recall by asking questions, ensuring critical information (e.g., study objectives, primary endpoints) is correctly retrieved.

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.