Data Characteristics
Registration and declaration documents in the biopharmaceutical domain originate from various sources. These include clinical trial reports, non-clinical study reports, manufacturing process files, quality standards, pharmaceutical research data, risk management plans, package inserts, and various regulatory documents and guidelines. Documents typically exist in formats such as PDF, Word, and Excel, with varying degrees of structure. Updates are driven by regulatory revisions, new drug development progress, and approval processes. Some data, like clinical trial progress, may update in real-time, while regulatory documents are usually revised annually or through special notices. Documents often contain extensive specialized terminology, abbreviations, dosage units (e.g., mg/kg, IU), time units (e.g., weeks, months), statistical indicators (e.g., p-value, CI), and complex tables and figures.
Constraints on Knowledge Base Retrieval and Recall
The characteristics of registration and declaration data impose several constraints on knowledge base retrieval and recall. First, heterogeneous data formats from multiple sources require robust file parsing capabilities, especially for recognizing tables and figures within PDFs. Second, the high density of specialized terminology and abbreviations means simple keyword matching can miss critical information, necessitating semantic understanding and term expansion. Frequent updates to regulations and guidelines require the knowledge base to quickly synchronize and differentiate between versions, preventing the recall of outdated or inapplicable information. Dosage units and statistical indicators in documents require precise matching or unit conversion during retrieval to ensure numerical accuracy. Furthermore, lengthy document structures demand higher standards for chunking granularity, context window management, and inter-paragraph relationship processing to ensure the completeness and logical coherence of recalled content.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Ensures individual chunks contain sufficient contextual information while avoiding excessive length that could lead to information redundancy and reduced retrieval efficiency. |
Chunk overlap | 100–200 characters | Maintains contextual continuity between chunks, reduces information fragmentation, and improves recall quality. |
maxContext | 8192 | Accommodates the complexity and information density of registration and declaration documents, supporting longer context windows for reasoning. |
Recall count | Top 5–8 entries | Given the specialized nature of the documents, increasing the number of recalled items covers more potentially relevant information, aiding accurate judgment. |
Similarity threshold | 0.75–0.85 | Balances precision and recall. Avoids introducing excessive irrelevant content due to a low threshold or missing critical information due to a high threshold. |
Rerank result count | Top 3 entries | Further optimizes relevance through a reranking model based on initial recall, focusing on the most core pieces of information. |
Common Pitfalls
- Query results contain numerous irrelevant or outdated regulatory clauses. This occurs when the knowledge base does not effectively manage and index regulatory document versions, leading to confusion between new and old versions.
- Retrieving specific drug dosages or experimental data yields results that do not precisely match values or units. This happens when file parsing fails to accurately identify and extract numerical fields and their accompanying units from tables.
- For complex manufacturing process descriptions, recalled content is fragmented and does not provide complete step-by-step information. This indicates that document chunking granularity is too small, disrupting the integrity of process descriptions.
Validation
- Select a batch of typical registration and declaration questions. Verify if the recall results include all key regulatory clauses, experimental data, and specialized terminology, and assess their version correctness.
- Query documents containing tables and figures. Check if the knowledge base can accurately extract and present numerical values, units, and statistical indicators, and compare them with the original documents.
- For complex concepts or processes described across multiple paragraphs, check if the recalled chunks can present the required information completely and logically, assessing their contextual integrity.
- Simulate multi-turn conversations in actual approval scenarios. Observe the knowledge base's retrieval performance in different contexts to ensure it consistently provides accurate and relevant supporting 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.