Knowledge Base Retrieval and Recall for Regulatory

Data Characteristics in this Category

Biopharmaceutical regulatory submission documents originate from guidelines and regulations published by drug regulatory agencies, as well as internal submission materials from pharmaceutical companies. These include clinical trial reports, non-clinical study reports, and manufacturing process documents. Document updates are relatively stable, typically occurring during regulatory revisions or when new drug development reaches a milestone. Document structures are highly standardized, often following international formats like ICH E3 and M4. They include sections such as abstracts, introductions, methods, results, and discussions. Data is frequently presented in tables and figures. Fields involve drug names, indications, dosages, adverse reactions, and statistical indicators. Units are precise (e.g., mg, mL, %), and specific medical terminology and abbreviations are common.

Constraints from these Characteristics on Knowledge Base Retrieval and Recall

The highly structured and standardized nature of regulatory submission documents facilitates knowledge base retrieval but also introduces challenges. Precise field and unit requirements necessitate effective semantic differentiation during vectorization to avoid incorrect recalls. The extensive use of tables and figures in documents requires specialized parsing strategies to ensure information is not lost and can be effectively retrieved. Stable update frequencies mean the knowledge base needs an efficient incremental update mechanism to incorporate the latest regulations or clinical data without affecting existing retrieval performance. Reliance on specific medical terminology and abbreviations requires the retrieval model to have strong domain vocabulary understanding to ensure the professionalism and accuracy of retrieval results.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`500–800 characters` (characters)	Regulatory submission document paragraphs are often long and contain multiple related information points. This length helps maintain contextual completeness.
`Chunk overlap` (Chunk Overlap)	`50–100 characters` (characters)	Ensures semantic continuity between paragraphs, especially when describing content across tables or figures, preventing information fragmentation.
`Recall count` (Recall Count)	`Top 5–8 entries` (top 5–8)	Submission document content is highly interconnected. Increasing the recall count helps cover more comprehensive related information.
`Similarity threshold` (Similarity Threshold)	Calibrate by actual measurement	Adjust based on actual recall effectiveness and false positive rate to ensure high relevance and low noise.
`Rerank result count` (Rerank Return Count)	`Top 3 entries` (top 3)	Considering the input length limitations of large models, selecting the most relevant entries improves model response efficiency and quality.
`chunk_strategy`	`by_title_and_text`	Regulatory submission documents have clear multi-level headings. Chunking by title better preserves semantic boundaries.

Three Common Mistakes

Knowledge base retrieval results are empty or return irrelevant paragraphs. This occurs when document parsing fails to correctly process tables or nested structures, leading to critical information not being vectorized or having poor vectorization quality.
Key information is missing from the knowledge base content sent to the AI, even though it exists in the knowledge base. This can happen if Recall count (Recall Count) or Rerank result count (Rerank Return Count) are set too low, filtering out relevant but not top-scoring entries.
Retrieval results do not reflect the latest content after a knowledge base update. This may be due to incorrect configuration of the incremental update mechanism or an incomplete index rebuilding strategy, leading to stale data or new data not being indexed promptly.

How to Confirm Correct Configuration

For typical queries, examine the raw paragraph content returned by knowledge base retrieval. Verify that it includes query keywords and relevant contextual information.
In a test environment, simulate different types of regulatory submission documents (e.g., clinical reports, regulatory provisions). Verify that the knowledge base can accurately recall corresponding content.
By comparing new and old document versions, confirm whether the knowledge base's retrieval performance for new content meets expectations after updates and reflects regulatory changes in a timely manner.
Monitor parameters like similarity_score in logs to evaluate the similarity distribution of retrieval results. This assists in adjusting the Similarity threshold (Similarity Threshold).

Note: The values provided are common starting points. Measure them against your 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.