Data Characteristics
Data for medical affairs regulatory submissions comes from various sources. These include reports from drug development, clinical trial data, regulatory guidelines, package inserts for marketed drugs, and review opinions from domestic and international regulatory bodies. This data updates infrequently, typically with new drug development milestones or regulatory policy changes. Documents are primarily in PDF, Word, and Excel formats. They contain extensive structured and semi-structured text, such as detailed experimental methods, statistical results, adverse event reports, pharmacological and toxicological data, and clinical summaries. Fields and units are highly specialized, for example, dose (mg/kg), concentration (μg/mL), P-value, and confidence intervals. Specific medical terminology and abbreviations are common.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval
The specialized nature of the data sources and the complexity of document structures require the knowledge base to accurately identify and parse various medical terms and data formats. Infrequent updates mean the knowledge base content is relatively stable, but each update requires data integrity and version control. Large numbers of PDF and Word documents, especially scanned copies, demand high-quality text extraction and OCR capabilities to ensure all text content is retrievable. The presence of specialized fields and units means simple keyword matching is insufficient. More advanced semantic understanding and entity recognition capabilities are necessary to distinguish similar terms or words with different meanings in different contexts. Furthermore, due to the rigorous nature of submission documents, the accuracy and traceability of retrieval results are critical to avoid misinterpreting or omitting information.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters | Balances semantic integrity of paragraphs with model processing efficiency, preventing information fragmentation. |
Maximum Paragraph Depth (Max Paragraph Depth) | 3 | Ensures the parser can delve into document structures, capturing key nested information like subheadings within sections. |
Recall count (Recall Count) | 5-8 items | Balances recall breadth with model processing load, covering multiple potentially relevant knowledge points. |
Similarity threshold (Similarity Threshold) | 0.75-0.85 | Improves recall precision, filtering out low-relevance results and reducing noise interference. |
Rerank result count (Reranked Return Count) | 3-5 items | Further optimizes recall results, placing the most relevant few pieces of information at the forefront for quicker decision-making. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles long parsing times for large PDF or Word documents, preventing parsing interruptions. |
Three Common Mistakes
- Knowledge base retrieval results contain a large amount of irrelevant preclinical toxicology data. This occurs because segment length is too long, causing individual segments to include excessive unrelated information and reducing retrieval precision.
- Some critical drug interaction information is not recalled. This manifests as retrieval results lacking data for specific fields. This happens when the knowledge base fails to correctly identify and extract text content from tables or images during creation.
- Custom knowledge base selection variables cannot be correctly assigned in the workflow, leading to retrieval failure. This is due to a mismatch between the variable data type and the expected type, preventing correct transmission of the knowledge base ID.
How to Verify Configuration
- Select a typical regulatory submission document. Manually extract key questions from it. Then, perform a search in the knowledge base. Check if the recall results include the expected information and evaluate the completeness of the recalled information.
- For documents containing complex tables or images, upload them and then check the text content parsed by the knowledge base. Confirm that table data and image descriptions are correctly identified and converted into retrievable text.
- Simulate questions from actual submission scenarios. Observe the retrieved document snippets. Evaluate whether these snippets directly support medical affairs personnel's decisions. Check if the recalled snippets can be traced back to the exact location in the original document.
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.