HTTP Interface and External Systems for Medical Affairs

Data Characteristics in this Category

Medical affairs pharmacovigilance data originates primarily from clinical trial reports, real-world studies, spontaneous adverse event reporting systems, and medical literature. This data updates frequently, especially post-market adverse drug reaction reports, which may see daily additions. Data document structures are complex, often containing large amounts of unstructured text like patient histories, symptom descriptions, diagnostic results, and medication details. Structured data includes fields such as drug generic names, batch numbers, dosages, administration routes, adverse event codes (e.g., MedDRA codes), severity, and outcomes. Units involve dosage (milligrams, grams), frequency (times/day, week), and time (days, hours), and may have multiple representations.

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

High-frequency updates necessitate that HTTP interfaces have efficient data ingestion capabilities, supporting batch uploads and incremental updates to prevent data latency. The prevalence of unstructured text means interface design must consider text length limits and reserve sufficient fields for storage. The complexity and diversity of structured fields require interfaces to flexibly handle various data types and support standardized medical coding systems. Inconsistent units require interfaces to have data preprocessing or conversion capabilities, or normalization before data ingestion. Furthermore, due to the sensitive nature of pharmacovigilance data, interfaces must include strict authentication and authorization mechanisms to ensure data transmission security.

Configuration Recommendations

Configuration Item	Suggested Value	Rationale
`maxContext`	8000 tokens	Accommodates detailed descriptions in pharmacovigilance reports, ensuring context completeness.
`UPLOAD_FILE_MAX_SIZE`	200 MB	Handles report files containing large amounts of text or embedded images.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Processes large PDF documents or complex report parsing, preventing failures due to timeouts.
`Chunk size` (Segment Length)	800 characters	Balances semantic integrity with retrieval efficiency, ensuring critical information is not truncated.
`Similarity threshold` (Similarity Threshold)	0.78	Filters for highly relevant adverse events or medical literature, reducing false positives.
`Knowledge Base Refresh Frequency`	Daily	Incorporates the latest adverse reaction reports and medical updates promptly.

Three Common Pitfalls

File upload failure or garbled characters when filenames contain Chinese: This typically results from improper Content-Disposition encoding in HTTP request headers or the server not correctly processing UTF-8 encoded path information.
Model call returns a 503 error: This may occur if the oneAPI configured downstream model service instances are insufficient, or if the model's resources within the specified group are exceeded, preventing timely request processing.
Query results remain outdated after knowledge base updates: This indicates that the knowledge base's caching mechanism did not invalidate in time or index rebuilding failed. Check the Knowledge Base Refresh Frequency setting and backend logs.

How to Verify Configuration

Upload a simulated pharmacovigilance report via API that includes long text, various structured fields, and a Chinese filename. Verify that the file is successfully ingested, its content is complete and not garbled, and fields like MedDRA codes are correctly parsed.
Conduct multiple rounds of question-answering tests against newly uploaded data in the knowledge base. Verify that the model accurately retrieves relevant information and evaluate the retrieval effectiveness of the Similarity threshold (Similarity Threshold).
Monitor system logs to confirm that all file processing tasks complete normally without timeout errors under the PARSE_FILE_TIMEOUT_SECONDS setting.

The values given 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.