Knowledge Base Retrieval and Recall for Registration and

Data Characteristics

Registration and declaration data in the biomedical field originates from regulatory documents, guidelines, technical review requirements published by drug regulatory agencies, and internal corporate submission materials, communication records, and deficiency letters. This data updates infrequently, primarily when regulations are issued or revised, typically quarterly or annually. Document structures are mostly unstructured text, such as PDF legal regulations, Word document templates for submission materials, and plain text from email correspondence. The data contains extensive specialized terminology, dosage units (e.g., mg/kg, IU), timelines (e.g., 120-day review period), and complex tables and figures.

Constraints on Knowledge Base Retrieval and Recall

The regulatory and specialized nature of registration and declaration data demands high accuracy and strong semantic understanding from knowledge base retrieval. This prevents misinterpreting terms and avoids compliance risks. The unstructured nature of documents and the abundance of specialized terminology render traditional keyword search ineffective, requiring advanced semantic vector retrieval techniques. Low update frequency means initial knowledge base construction can allocate more resources to fine-grained segmentation and annotation. However, subsequent incremental updates and version management require efficient synchronization mechanisms. The presence of complex tables and figures challenges document parsing capabilities; simple text slicing may lose critical information. The strictness of fields and units requires precise matching in retrieval results to prevent errors due to unit confusion.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk Length`	`300–500 characters`	Preserves semantic completeness, avoids noise from overly long chunks
`Retrieval Count`	`Top 8–12`	Covers more potential relevant information, provides rich input for reranking
`Similarity Threshold`	Calibrate with actual samples	Ensures result relevance, filters low-quality recalls
`Reranked Return Count`	`Top 3–5`	Selects the most relevant results, improves final answer quality
`maxContext`	`4000 tokens`	Accommodates the complexity and information density of declaration documents
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles the parsing time required for large regulatory and submission documents

Common Pitfalls

Symptom: Retrieval results include irrelevant regulatory provisions. Reason: Chunk Length is set too large, leading to the inclusion of excessive irrelevant information during vectorization, which reduces semantic precision.
Symptom: Querying a specific drug dosage form or indication returns results missing critical information. Reason: Document parsing failed to effectively identify and extract data from tables or figures, resulting in incomplete data in the knowledge base.
Symptom: New regulatory content is not retrievable after a data update. Reason: The knowledge base synchronization mechanism is not configured or executed promptly, preventing new data from being imported into the vector database in a timely manner.

Verification Steps

Select multiple typical query questions. Check the similarity score distribution of retrieval results. Ensure relevant documents score higher than irrelevant ones.
For documents containing tables and figures, execute specific queries. Verify that key data points are effectively retrieved and referenced.
Simulate the latest regulatory update process. Upload new files and execute queries. Confirm that new content is successfully retrieved within the expected timeframe.

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.