Data Characteristics
Market access regulatory documents are typically formal documents issued by national or regional regulatory bodies. Examples include regulations, guidelines, technical review requirements, application forms, and appendices. These documents are updated infrequently. However, updates often have a global impact and require timely synchronization. Document structures are highly standardized, usually in PDF or Word format, containing numerous clauses, definitions, flowcharts, and examples. Fields and units are highly industry-specific. Examples include drug registration numbers, medical device filing certificate numbers, review and approval timelines (in working days), and clinical trial approval numbers. These documents often include complex citations and cross-references.
Constraints on Knowledge Base Retrieval and Recall
The low update frequency of market access regulatory documents means initial knowledge base construction requires a one-time, high-quality document import and parsing. Subsequent maintenance focuses on incremental updates and version management. The standardized document structure and complex references require the knowledge base to have strong semantic understanding capabilities. It must accurately identify and link logical relationships between different clauses to avoid fragmented recall. Industry-specific fields and units, especially critical information like approval timelines, require the retrieval model to understand the contextual meaning of these specialized terms and highlight or extract them in retrieval results. Furthermore, due to the formal and rigorous nature of the documents, the accuracy and completeness of recall results are critical. Any misinterpretation or omission can lead to serious compliance risks.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 800–1200 characters (characters) | Ensures each text segment contains complete regulatory clauses or explanations, preventing semantic fragmentation. |
Recall count (Recall Count) | Top 5 entries (top 5) | Covers core relevant information while controlling context window size and reducing inference costs. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Balances recall rate and accuracy, ensuring retrieval results are highly relevant to the query. |
Rerank result count (Reranked Return Count) | 3 entries (3 items) | Further refines recall results, prioritizing the most critical and matching regulatory provisions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses long parsing times for complex PDF files, preventing parsing timeouts. |
maxContext | 8192 | Accommodates potentially long regulatory clauses, requiring a larger context window for complete semantic understanding. |
Common Pitfalls
- Knowledge base training status remains "training": This usually indicates document parsing failure or task accumulation due to insufficient backend computing resources.
- Retrieval results contain many irrelevant or duplicate paragraphs: This may be due to an overly coarse chunking strategy that mixes unrelated content into the same paragraph, or a similarity threshold set too low.
- Key regulatory clauses or approval timeline information are not recalled: This occurs when document parsing fails to correctly identify these specific fields, or the vector embedding model does not fully understand their semantic importance.
Validation
- Manually verify retrieval results for typical market access questions to ensure they include all relevant regulatory clauses and key information.
- Select a set of test questions with clear answers. Check if the correct answers are present in the recall results and evaluate their ranking.
- Monitor backend logs to confirm all document parsing tasks complete successfully without timeouts or errors.
- Adjust the similarity threshold and observe changes in recall count and relevance to determine an appropriate balance point.
The values provided are common starting points and should be measured against specific 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.