Knowledge Base Retrieval and Recall for Pharmacovigilance

Data Characteristics

Pharmacovigilance quality documents include regulatory guidelines from drug regulatory agencies, internal Standard Operating Procedures (SOPs), Risk Management Plans (RMPs), and Post-Market Safety Update Reports (PSURs). Update frequencies vary. Regulatory guidelines may revise annually. Internal SOPs and RMPs typically update every 1-3 years or when significant changes occur.

Document structures are often hierarchical with distinct sections. They contain extensive specialized terminology, acronyms, and citations. Fields and units frequently involve drug names, active ingredients, adverse event codes (e.g., MedDRA), dosage units (mg, µg), and time units (days, months, years). Strict requirements exist for numerical precision and unit consistency.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The update cycle and hierarchical structure of pharmacovigilance documents require the knowledge base to support efficient incremental updates and fine-grained chunking. This ensures the timeliness and relevance of retrieval results.

The widespread use of specialized terminology and acronyms demands that the embedding model accurately understands contextual semantics. This prevents recall failures due to vocabulary differences. For example, a search for "AE" (Adverse Event) should link to a detailed description of "Adverse Event."

Documents contain numerical values and units, such as "daily dose not exceeding 20 mg." Retrieval must match text and understand numerical ranges and unit conversions to support queries on specific dose-related risks.

Mandatory clauses in regulations and SOPs require retrieval results to be highly accurate and authoritative. Ambiguous or misleading information is unacceptable.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk Size`	`500–800 characters`	Most pharmacovigilance document paragraphs have moderate length. This range ensures contextual completeness and minimizes semantic loss from splitting.
`Recall Count`	`5–8 items`	This ensures sufficient coverage of potentially relevant information while avoiding excessive irrelevant noise.
`Similarity Threshold`	`Calibrate by measurement`	The pharmacovigilance domain is highly specialized. Multiple rounds of testing are necessary to ensure high-relevance documents are recalled and low-relevance documents are filtered out.
`Rerank Return Count`	`3–5 items`	This further refines recall results, prioritizing the most core and authoritative regulatory or SOP clauses.
`maxContext`	`4096 tokens`	Pharmacovigilance queries often involve complex scenarios. Sufficient context window is needed to support in-depth analysis and multi-turn conversations.

Three Common Mistakes

Retrieval results include many irrelevant or outdated documents. This typically occurs when the knowledge base does not perform timely incremental updates, leading to the recall of old versions of regulations or SOPs.
When a user queries specific drug dose-related risks, the system fails to recall clauses containing numerical values and units. This happens because the text chunking strategy does not effectively preserve the association between numerical values and units.
When a user asks a question unrelated to the knowledge base content, the system still attempts to provide seemingly relevant answers from the knowledge base. This indicates a lack of an effective "no relevant results" handling mechanism or a minimum relevance threshold.

How to Confirm Correct Configuration

Select typical pharmacovigilance query scenarios, such as "adverse event reporting process for a certain drug" or "contraindications for a specific indication." Verify that recall results include all key regulatory and SOP clauses. Check if the Recall Count is reasonable.
Simulate queries completely unrelated to the knowledge base content. Observe if the system correctly identifies this and provides a "no relevant information found" prompt or triggers a predefined fallback response. This confirms the effectiveness of the Similarity Threshold.
Submit questions related to recently updated regulations or SOPs. Cross-reference the recall results to ensure they are the latest versions. This verifies the knowledge base's update mechanism and the impact of Chunk Size on timeliness.

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.