Data Characteristics
Medical affairs regulations and Standard Operating Procedures (SOPs) originate from internal quality management systems, policy documents from compliance departments, and industry association guidelines. These documents have a low update frequency, typically quarterly or annually, or when significant regulatory changes occur. Documents are usually in PDF or Word format, with a standardized structure including clear section headings, clause numbers, definitions, responsibility descriptions, and operational steps. Common metadata fields include "Document Number," "Version Number," "Effective Date," and "Revision History." The text content focuses on medical ethics, clinical trial management, pharmacovigilance, and medical information communication, without complex numerical calculations or unit conversions.
Constraints on Source Citation and Traceability
The standardized and stable nature of medical affairs documents requires highly accurate source citations traceable to specific paragraphs in the original text. Due to the low update frequency, the knowledge base must maintain document versions consistent with the latest published versions to avoid citing outdated information. Section headings and clause numbers in the document structure are crucial anchors for precise recall and traceability, requiring segmenting to effectively preserve this structural information. The specialized nature of medical affairs content demands high precision in similarity matching, preventing misinterpretations from leading to irrelevant clause citations. For potential cross-references within documents, the system must identify and provide multi-level traceability paths to ensure users understand the full scope of information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 800–1200 characters | Retains complete clauses or paragraphs, avoids semantic fragmentation, and balances retrieval efficiency. |
Recall count (Number of Retrieved Items) | Top 5–8 items | Given the rigor of medical affairs regulations, increasing the number of retrieved items covers more relevant clauses and improves accuracy. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures high semantic relevance of retrieved content, reduces false positives, and is suitable for scenarios with many specialized terms and high semantic precision requirements. |
Rerank result count (Number of Reranked Items) | Top 3 items | After reranking, prioritize the most relevant and information-dense clauses to reduce user reading burden. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses long parsing times for large regulatory documents, preventing file processing failures due to timeouts. |
maxContext | 3000 characters | Ensures the context sent to the large language model is sufficient to include multiple cited paragraphs and their surrounding information, maintaining semantic integrity. |
Common Mistakes
- CSV file garbling during upload: File encoding format mismatch with the system's default encoding, leading to text parsing errors.
maxContextsetting in knowledge base search is too large, causing context not to be sent: The system has a hard limit on context length; exceeding this limit truncates or prevents sending.- HTTP response data cannot be used as a citation: The response data format does not conform to knowledge base ingestion specifications and requires structured conversion.
Verification Steps
- Upload a regulatory document with multiple sections and clauses. Check if segmentation in the knowledge base is complete and if the original document's structural information is preserved.
- Query specific clauses within the document. Verify if the cited sources accurately point to the corresponding paragraphs in the original text and confirm version numbers.
- Test queries containing specialized terms and abbreviations. Check if the similarity ranking of retrieval results is reasonable and if the most relevant clauses are ranked at the top.
Note: The values provided are common starting points. Measure against your own samples for optimal performance.
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.