Deployment and Upgrades for Pharmacovigilance Clinical Trial

Data Characteristics

Pharmacovigilance clinical trial pre-screening data originates from clinical trial reports, adverse drug reaction (ADR) reports, case report forms (CRFs), electronic health records (EHRs), and global pharmacovigilance databases. This data updates frequently. During clinical trials, ADRs and safety updates can occur daily or hourly. Document structures are complex, containing unstructured text (e.g., physician notes, patient descriptions) and structured data (e.g., ICD-10 diagnostic codes, ATC drug codes, mg/kg dosages, YYYY-MM-DD occurrence dates). Fields are diverse, including patient demographics, medical history, concomitant medications, adverse event descriptions, severity assessments, outcomes, and causality assessments related to the trial drug. Units require precise handling, such as dosage units (mg, g, ml, IU), frequency units (times/day), and time units (hours, days, weeks).

Constraints on Deployment and Upgrades

High-frequency data updates require FastGPT's knowledge base synchronization mechanism to support real-time or near real-time processing. This ensures pre-screening results rely on the latest information. Complex document structures, especially large volumes of unstructured text, demand advanced text segmentation strategies and embedding model selection to effectively extract key information. Diverse, heterogeneous data sources necessitate flexible data ingestion interfaces and robust data cleaning capabilities to unify field formats and handle missing values. The variety of fields and the need for unit standardization impact knowledge base indexing and recall accuracy, requiring predefined or dynamic entity recognition. Specialized coding systems like ICD-10 and ATC require focused entity recognition and knowledge graph construction. Additionally, ARM64 architecture deployment, such as adaptation on Huawei OpenEuler systems, requires considering container image compatibility and performance optimization.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`CHUNK_SIZE`	800–1200 characters	Balances semantic completeness for long texts with recall efficiency for short texts, accommodating the mix of descriptive paragraphs and key information sentences in clinical reports.
`OVERLAP_SIZE`	100–200 characters	Ensures contextual continuity across segment boundaries, reducing the risk of missing critical information due to segmentation cuts, especially for the coherence of adverse event descriptions.
`UPLOAD_FILE_MAX_SIZE`	1000 MB	Clinical trial reports and case reports may contain numerous images or detailed attachments; this value must be sufficient.
`PARSE_FILE_TIMEOUT_SECONDS`	600 seconds	Processing large PDF or Word clinical documents can be time-consuming; this prevents parsing failures due to timeouts.
`MODEL_CHANNEL_STRATEGY`	`round-robin` or `weighted-random`	Addresses high concurrent queries. Balances load across model channels based on model provider stability and latency, ensuring availability and response speed of the pre-screening service.
`KNOWLEDGE_BASE_UPDATE_INTERVAL`	1 hour or less	Pharmacovigilance data updates frequently. New adverse event reports and safety information must be rapidly incorporated into the knowledge base to meet real-time requirements.

Common Pitfalls

Skipping intermediate version manual steps during FastGPT upgrades can lead to incomplete database migration scripts or missing configurations. This manifests as service startup failures or partial feature malfunctions, for example, the KNOWLEDGE_BASE_UPDATE_INTERVAL configuration from an older version not migrating.
Uploading large clinical trial report files may result in prolonged file parsing or 504 Gateway Timeout errors. This occurs when PARSE_FILE_TIMEOUT_SECONDS is too small or the reverse proxy's timeout setting is insufficient.
Deployment on ARM64 architecture OpenEuler systems may encounter container image pull failures or exec format error during startup. This indicates the use of an x86 architecture image; an image specifically built for arm64v8 architecture must be specified or built.

Verification Steps

Upload a simulated clinical trial report containing various formats (e.g., PDF, DOCX) via the FastGPT administration interface. Verify successful file parsing and knowledge base segment generation.
Simulate high-frequency data updates. Observe if knowledge base update tasks execute according to the preset KNOWLEDGE_BASE_UPDATE_INTERVAL and verify that newly added data is retrievable.
Test with different embedding models. Query medical terms including ICD-10 or ATC codes to evaluate the relevance of recall results and determine a reasonable range for the similarity threshold.

The values provided are common starting points. Measure them against your 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.