Data Characteristics
Regulatory and standard documents for siRNA nucleic acid drugs originate primarily from guidelines, technical review requirements, and review reports published by national drug regulatory agencies (e.g., FDA, EMA, NMPA). International standardization organizations (e.g., ICH) also provide relevant guidelines. These documents are typically in PDF format, with some Word or HTML pages. Content covers the entire lifecycle, from R&D, manufacturing, and quality control to clinical trials and market approval.
Document update frequency varies. Regulatory files are usually revised every few months to several years, while technical guidelines may iterate faster based on scientific advancements. Document structures commonly include chapters, sub-sections, and appendices, with extensive use of specialized terminology, acronyms, diagrams, and tables. Field and unit specificity is evident in the precise description of nucleic acid sequences, modification types, delivery systems, pharmacokinetic parameters (e.g., half-life, distribution volume), and toxicology indicators. Common biological and pharmaceutical units include nM, μg/kg, and mg/mL.
Constraints on "Citing and Tracing Sources"
The authoritative and specialized nature of siRNA nucleic acid drug regulatory documents requires precise citation of original files and specific paragraphs. This ensures compliance and credibility. Varying document update frequencies necessitate careful attention to version numbers and publication dates to avoid citing outdated information.
Complex document structures (multi-level headings, diagrams, tables) challenge text segmentation and information extraction. Intelligent parsing capabilities are needed to identify effective contextual boundaries. The widespread use of specialized terminology, acronyms, and specific units means the model requires high domain knowledge to accurately recall relevant paragraphs and trace sources when understanding and matching queries. Furthermore, citing specific data like nucleic acid sequences may involve special characters or formats, requiring the system to handle these correctly and present them completely during source tracing.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk size (Segment Length) | 500–800 characters | Balances paragraph completeness and retrieval granularity. Avoids long paragraphs diluting key information and short paragraphs losing context. |
Recall count (Recall Count) | Top 8–12 entries | Increases coverage and the probability of capturing relevant information from multiple authoritative sources, addressing multi-dimensional queries. |
Similarity threshold (Similarity Threshold) | 0.78 | Ensures high relevance of recall results, reducing interference from low-quality or irrelevant content. Suitable for specialized fields. |
Rerank result count (Rerank Return Count) | Top 5 entries | Improves the ranking position of the most relevant results through reranking algorithms, while maintaining recall quantity. |
maxContext | 3000 characters | Allows the model to process sufficient contextual information to understand complex regulatory provisions and technical details of siRNA drugs. |
Citation template (Citation Template) | Original Link：{source.url}\nDocument：{source.title}\ntablets Paragraph：{source.content} | Provides clear, clickable links to original documents and file titles, allowing users to quickly navigate for verification. |
Three Common Mistakes
- Symptom: The answer does not cite any sources, or the cited sources do not match the answer content. Reason: The
Similarity threshold(Similarity Threshold) is set too high, failing to recall enough relevant document snippets. The model tends to generate answers when no effective recall occurs. - Symptom: The system encounters parsing errors when processing PDF documents, leading to missing content or format inconsistencies in the knowledge base. Reason: PDF documents contain many complex diagrams, special characters, or scanned images.
PARSE_FILE_TIMEOUT_SECONDSis too short, or the parser fails to effectively identify and extract these non-text elements. - Symptom: Queries regarding siRNA sequences or specific pharmacokinetic parameters do not accurately cite corresponding data. Reason: The
Chunk size(Segment Length) is too long, causing critical values or phrases to be buried in long paragraphs, affecting retrieval precision.
How to Confirm Correct Configuration
- Submit complex queries selectively, including siRNA sequences, specific regulatory provisions, or pharmacokinetic parameters. Verify if the answer accurately cites the corresponding content from the original document.
- Check if the citation source links provided in the answer are clickable and correctly navigate to the specified location in the original document or display the file name.
- Randomly select documents from the knowledge base. Use keyword search to verify if the document can be recalled and its content snippets are displayed correctly, especially for paragraphs containing diagrams or tables.
- Upload and query different document types (PDF, Word, HTML) to ensure all formats are parsed and cited correctly.
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.