Data Characteristics in this Category
Site Management Organizations (SMOs) handle diverse data types during clinical trial pre-screening. Primary data sources include patient medical records from research centers, laboratory test results, imaging reports, and prior medication histories. This data typically exists as unstructured text, semi-structured documents (e.g., PDFs, DICOM image metadata), and structured data (e.g., CSV files or HL7 messages exported from LIMS systems). Data updates frequently, especially during subject screening and visits, when new test results and symptom descriptions are generated in real-time. Document structures are complex; for example, medical record text may contain various medical terms, abbreviations, and free-text descriptions. Field and unit accuracy are critical for medical indicators (e.g., blood pressure mmHg, blood glucose mmol/L, complete blood count /L) and are often accompanied by clinical judgments and diagnostic descriptions.
Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"
The high update frequency and complex structure of SMO clinical trial pre-screening data necessitate specific HTTP interface designs. The system must support high-concurrency data ingestion to handle large volumes of subject data uploaded simultaneously from multiple research centers. Non-structured and semi-structured data require pre-processing or structural conversion via API. This includes using Natural Language Processing (NLP) techniques to extract key entities and values from medical record text. The precision required for medical indicators demands strict data type definitions and validation rules in interface design to prevent unit confusion or parsing errors. When integrating external systems, the design must accommodate varying data output formats from different research centers or LIMS systems, using a flexible adaptation layer for unification. Furthermore, data sensitivity (e.g., patient privacy) mandates robust authentication and authorization mechanisms and data transmission encryption for APIs. Examples include OAuth 2.0 or API Keys for access control and mandatory HTTPS protocol usage.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxRequestSize | 50MB | Accommodates the transfer of imaging reports and large medical record PDF files. |
timeoutSeconds | 600 seconds | Allows sufficient time for uploading and initial parsing of complex documents. |
authTokenExpiry | 1 hours | Balances security with user experience, avoiding frequent re-authentication. |
concurrentConnections | Calibrate by actual measurement | Ensures stable server operation under high concurrency, preventing resource exhaustion. |
responseSchema | JSON Schema v7 | Clearly defines the return data structure, facilitating parsing and validation by downstream systems. |
errorHandlingStrategy | Retry3 times，Interval5 seconds | Provides fault tolerance for network fluctuations or temporary service unavailability. |
Three Common Pitfalls
- API key configuration appears to be perpetually loading after setup: This usually indicates local server network policy restrictions preventing FastGPT from accessing external API services or performing necessary key verification.
- HTTP network search requests are not triggered or return empty results when the knowledge base fails to match: The
http_requesttool's trigger conditions in the Agent configuration may be set incorrectly, failing to capture knowledge base miss events, or the URL and request parameters might be wrong. - Online simple application API calls experience slow responses or errors as concurrency increases: This often points to insufficient backend service or database connection pool configurations, which fail to handle a large volume of concurrent requests, leading to resource bottlenecks.
How to Verify Correct Configuration
- Use POSTMAN or other API testing tools to simulate data upload and query requests under high concurrency. Observe response times and success rates.
- Check the external HTTP request logs in FastGPT's logging system. Confirm that request parameters, return status codes, and response content meet expectations.
- Test the combined Agent, including knowledge base Q&A and HTTP network search, using actual clinical trial pre-screening data. Verify its network search capability when the knowledge base misses.
- Monitor server resource usage (CPU, memory, network I/O). Ensure all metrics remain within a healthy range under high load and adjust concurrency thresholds based on business requirements.
The values provided are common starting points and should be measured against the reader's 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.