Data Characteristics for This Category
Regulatory submission documents in the biopharmaceutical domain primarily originate from regulatory bodies such as the National Medical Products Administration (NMPA), European Medicines Agency (EMA), and U.S. Food and Drug Administration (FDA). These sources publish regulations, guidelines, technical requirements, approval notices, and case studies. Document updates typically occur quarterly or annually, with unscheduled updates during special circumstances or policy changes. Documents have a rigorous structure, often in PDF, Word, or XML formats, containing numerous hierarchical headings, charts, appendices, and cross-references. Fields and units are highly specialized, for example, dosage units (mg/kg), concentration (μg/mL), formulation types (injections, tablets), indication descriptions (e.g., "for the treatment of adult locally advanced or metastatic non-small cell lung cancer"), and strict terminology definitions.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
The rigorous structure and specialized terminology of regulatory submission documents require the knowledge base to maintain semantic integrity during document chunking. This avoids loss of critical information or context breaks due to excessively fine-grained chunking. Frequent update cycles mean the knowledge base must support efficient incremental updates and version management to ensure retrieval timeliness and accuracy. Numerous cross-references and appendix content in documents place higher demands on the retrieval system, requiring the ability to identify and link related information across different documents. The precision of specialized fields and units means that keyword-based fuzzy matching may be insufficient. Stronger semantic understanding is necessary to identify synonyms, near-synonyms, and professional abbreviations, ensuring the accuracy of recall results and avoiding misinterpretation of specialized terminology.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for This Value |
|---|---|---|
Chunk size (Chunk Size) | 500–800 characters (characters) | Balances semantic integrity with single-chunk information density, accommodating the paragraph length of regulatory texts. |
Chunk Overlap Length (Chunk Overlap) | 100–150 characters (characters) | Ensures contextual continuity between paragraphs, handling specialized terms or descriptions that span across chunks. |
Recall count (Recall Count) | 8–12 entries (items) | Controls the context length processed by the model while ensuring coverage, reducing interference from irrelevant information. |
Similarity threshold (Similarity Threshold) | Calibrate based on actual measurements | Adjusts based on actual retrieval performance to ensure high relevance and exclude low-relevance results. |
Rerank result count (Reranked Return Count) | 3–5 entries (items) | Focuses on the most relevant few results, improving the precision of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds (seconds) | Addresses the time-consuming nature of parsing large regulatory documents, preventing parsing timeouts. |
Three Common Mistakes
- Symptom: AI answers contain content inconsistent with regulations or "confidently make up facts." Reason:
Recall count(Recall Count) is set too high, introducing a large amount of low-relevance or interfering information, causing the model to deviate from core facts. - Symptom: After uploading
.docxor.pdfdocuments, retrieval results do not include chapter heading information. Reason: The document parser failed to correctly identify and extract the document's hierarchical structure, leading to a lack of this metadata in the knowledge base. - Symptom: System response time for queries is too long, or even times out. Reason:
Chunk size(Chunk Size) is set too small, leading to the generation of too many fragmented knowledge chunks, increasing the computational burden during retrieval.
How to Confirm Proper Configuration
- Select typical regulatory submission questions and verify the AI's answer accuracy, completeness, and correct citation of original regulatory text.
- Upload regulatory documents containing complex charts, appendices, and cross-references. Check if the knowledge base can correctly parse and index key information within them.
- Test queries containing specialized terms, abbreviations, and synonyms. Evaluate whether recall results include all relevant document fragments and examine their similarity score distribution.
- Monitor retrieval performance after incremental knowledge base updates. Ensure that newly added or modified regulatory provisions are recalled promptly and accurately.
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.