HTTP Interface and External Systems for Phase II-III

Data Characteristics

Phase II-III clinical pharmacovigilance data originates from Electronic Data Capture (EDC) systems, Hospital Information Systems (HIS), Laboratory Information Management Systems (LIMS), and subject diaries. Data updates are frequent. Initial reports for adverse events typically occur within 24 hours, with ongoing tracking until resolution. Document structures are complex. They include patient demographics, medical history, comorbidities, adverse event descriptions, severity, causality assessments, interventions, and outcomes. These often appear as structured forms, free-text descriptions, and attachments (e.g., medical imaging reports, lab PDF files). Field naming follows international standards like ICH E2B, but specific implementations vary by sponsor and CRO. Units include dosage (mg, g), frequency (times/day), time (hours, days), and biochemical indicators (mmol/L, U/L).

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

High-frequency data updates require real-time or near real-time processing capabilities to avoid delays in pharmacovigilance signal detection. Diverse data sources and complex document structures mean HTTP interfaces must support multiple data formats, including JSON, XML, and file uploads (multipart/form-data). They must also effectively parse free-text information. The transmission of large volumes of unstructured attachments demands high bandwidth and storage capacity from the interface. External systems require OCR or PDF parsing capabilities to extract key information. Adherence to international standard fields requires the interface to accurately identify and handle field discrepancies from different sources during data mapping and transformation, ensuring data consistency. Standardized unit processing is necessary to prevent data misinterpretation due to inconsistent units. The interface should perform uniform conversion or validation after data reception.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`HTTP_REQUEST_TIMEOUT_SECONDS`	`60 seconds`	Processing complex data parsing and external system responses can take a long time.
`MAX_FILE_UPLOAD_SIZE_MB`	`200 MB`	Accommodates large attachments like medical images and detailed lab reports.
`LLM_MAX_CONTEXT_TOKENS`	`4096`	Adapts to longer inputs from free-text adverse event descriptions and associated medical history.
`CHUNK_SIZE_CHARACTERS`	`800 characters`	Balances long text processing efficiency with semantic integrity, suitable for segmenting medical reports.
`SIMILARITY_THRESHOLD`	`0.75`	Improves the accuracy of recalling similar events in pharmacovigilance reports, reducing false positives.
`RECALL_TOP_K`	`10 entries`	Ensures coverage of more potentially relevant adverse event records in complex queries.

Common Pitfalls

The interface returns an HTTP 504 Gateway Timeout error: This occurs when processing complex data or waiting for external system responses takes too long, exceeding the default timeout settings of proxy servers or the application itself.
Key fields (e.g., AE_TERM adverse event term) are empty or parsed incorrectly in the knowledge base: This can happen if source system data formats are non-standard, or if the HTTP interface does not perform strict field validation and standardized conversion after reception.
A locally deployed FastGPT instance connecting to an external LLM service shows connection refused: This typically indicates that the OneAPI or Ollama service is not running correctly, or the LLM_API_BASE address in the FastGPT configuration is incorrect, preventing network connection establishment.

Verification Steps

Use a simulation tool to send test data packets containing various data types (structured, free-text, attachments) to the configured HTTP interface. Check if the returned status code is HTTP 200 OK and validate if the response body structure meets expectations.
Import a typical Phase II-III clinical adverse event report into the FastGPT knowledge base. After processing, retrieve key information (e.g., drug name, adverse event name, severity) to confirm accurate data parsing and storage.
In the FastGPT application, test with a prompt containing complex medical terminology and lengthy descriptions. Observe if the LLM's response accurately references relevant adverse event information from the knowledge base and evaluate the accuracy of its associative analysis.

The values provided are common starting points. They 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.