HTTP API and External Systems for Pharmacovigilance

Data Characteristics

Pharmacovigilance (PV) regulation data originates from adverse drug reaction (ADR) reports, safety update reports, risk management plans, and relevant regulatory documents. This data combines structured and unstructured formats. Update frequency is often high, especially during new drug launches or when new safety signals emerge. Document structures are complex, frequently containing medical terminology, clinical data, and legal clauses. Fields include patient information, drug information, adverse reaction descriptions, severity, and causality assessments. Unit notations must strictly adhere to medical and pharmaceutical standards, such as mg, µg for dosage, h, d, year for time, and various laboratory test result units.

Constraints Imposed by HTTP API and External Systems

The complexity and high update frequency of pharmacovigilance regulation data impose specific requirements on HTTP API design and external system integration. First, large data volumes and diverse formats require APIs to support multiple data types, including text, JSON, XML, and attachments. Second, information timeliness demands high throughput and low latency from APIs to ensure prompt processing and analysis of adverse reaction reports. Documents contain substantial unstructured text, necessitating robust text processing capabilities, potentially involving natural language processing (NLP) model integration. Additionally, the strictness and medical specificity of fields require APIs to accurately identify and process specific units and terminology during data transmission and parsing, preventing data distortion due to format or semantic errors. When data validation fails, a clear error code and detailed error message return mechanism are essential.

Configuration Guidelines

Configuration Item	Suggested Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`50 MB`	Pharmacovigilance reports often include images or detailed attachments, requiring support for large file uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Complex PDFs or scanned documents take longer to parse; allocate sufficient processing time.
`maxContext`	`8192 token`	Regulatory documents are lengthy; a larger context window accommodates complete information.
`Chunk size`	`800 characters`	Ensures each text segment contains sufficient semantic information, reducing misinterpretation.
`Similarity threshold`	`0.75`	Medical texts are highly specialized; increase the threshold to ensure recall precision.
`HTTP_REQUEST_TIMEOUT`	`60 seconds`	External systems may respond slowly; set a reasonable timeout to avoid frequent interruptions.

Common Pitfalls

Calling an external API returns a 404 Not Found error. This may indicate an incorrect API endpoint path or version number configuration, or the external system is not deployed correctly.
Uploading a local adverse reaction report file results in a long system unresponsiveness or report parsing failure, with logs showing PARSE_FILE_TIMEOUT. This typically occurs when PARSE_FILE_TIMEOUT_SECONDS is set too low for large or complex files.
Testing an external model connection in FastGPT encounters a 401 Unauthorized or 403 Forbidden error. This often happens when the API Key or authentication credentials configured in config.json do not match settings in the FastGPT interface or environment variables, leading to authorization failure.

Verification Steps

Upload a simulated adverse reaction report containing multiple text pages and image attachments via the HTTP API. Verify successful reception and a 200 OK status code.
Import a typical pharmacovigilance regulation document (e.g., PDF format) into the FastGPT knowledge base. Observe if segmentation is reasonable. Conduct multi-turn Q&A to assess answer accuracy and completeness.
After configuring an external model connection, input a pharmacovigilance-related question in the FastGPT test interface, such as "What are the contraindications for drug XX?". Check if the model responds correctly and provides expected answers. Observe if response latency is within an acceptable range.

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.