Data Characteristics
Quality document management in biopharmaceuticals, especially concerning pharmacovigilance and adverse drug reactions (ADRs), involves diverse data sources. These include regulatory documents from health authorities, ICH guidelines, internal Standard Operating Procedures (SOPs), product inserts, Risk Management Plans (RMPs), and archived ADR reports. Update frequencies vary: regulatory documents may be revised annually, SOPs updated periodically due to internal process optimization or external audit requirements, and ADR reports continuously generated and archived.
Document structures also differ. Regulatory documents typically have clear section divisions and clause numbers. SOPs follow fixed templates, including objectives, scope, responsibilities, processes, and records. ADR reports are semi-structured, containing fields like patient information, drug information, adverse reaction descriptions, and severity assessments. Field names and units adhere to industry standards, such as dosage units (mg, µg), time units (hours, days), severity classifications (e.g., "mild," "moderate," "severe"), and medical terminology like MedDRA codes.
Constraints on Knowledge Base Retrieval and Recall
The diversity and structural characteristics of quality document data impose specific requirements on knowledge base retrieval and recall.
The strict hierarchical structure and clause numbering of regulatory documents demand precise matching of specific clauses over vague semantic searches. The templated structure and process descriptions of SOPs require the knowledge base to identify and extract key steps, responsible parties, and operational details, supporting process-stage-based retrieval. The semi-structured and continuously updated nature of ADR reports necessitates handling large volumes of short texts or summaries, and rapidly integrating the latest data for quick risk assessment.
The use of medical and industry-specific terminology requires high accuracy in word segmentation and entity recognition. Given the authoritative and rigorous nature of these documents, accuracy, completeness, and traceability of retrieval results are core considerations. Any incorrect recall could lead to severe compliance risks. The ability to trace historical versions and update records also acts as an implicit constraint on retrieval.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters | Ensures the completeness of key clauses or steps, preventing semantic loss due to truncation. |
Recall count (Recall Count) | 8–12 items | Balances retrieval breadth with the efficiency of subsequent language model processing, increasing relevant information coverage. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Improves matching precision for professional terminology and regulatory clauses, reducing irrelevant results. |
Rerank result count (Reranked Return Count) | 5 items | Focuses on the most relevant document snippets, preventing the language model from processing too much low-relevance information. |
maxContext | 32000 tokens | Accommodates the context requirements of lengthy regulations and complex SOPs, ensuring complete semantic understanding. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Supports the upload of large PDF documents, such as RMP files containing charts. |
Common Pitfalls
- Retrieval results contain numerous irrelevant regulatory clauses because the
Similarity threshold(Similarity Threshold) is set too low, leading to generalized recall. - Failure to retrieve the latest revised SOP content because the knowledge base update mechanism is not synchronized with the enterprise document management system, resulting in outdated data.
- Key information is missing from specific adverse event reports because the
Chunk size(Chunk Size) is set too short, causing report content to be truncated and critical fields not fully captured.
How to Validate Configuration
- For a series of typical regulatory queries, check if all relevant clauses are included within the
Recall count(Recall Count) and evaluate recall accuracy. - Randomly select 5 recently revised SOPs. Retrieve their main content through the knowledge base to confirm that the retrieval results match the latest versions.
- For various adverse event types, construct queries containing key medical terminology. Verify that the recall results accurately extract core information such as patient, drug, and reaction details.
- Compare retrieval results across different
Similarity threshold(Similarity Threshold) values. Select the threshold that ensures high recall while minimizing the amount of irrelevant information.
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.