Knowledge Base Retrieval and Recall for siRNA Nucleic Acid

Data Characteristics for this Category

siRNA nucleic acid drug registration and declaration documents draw from diverse sources. These include internal research and development reports, clinical trial data, non-clinical study reports, manufacturing process documents, quality standards, pharmaceutical research reports, and regulatory guidelines and review requirements. Documents are typically in PDF, Word, or Excel formats. Content spans molecular design, synthesis, quality control, pharmacology, toxicology, and clinical research. Update frequency depends on R&D progress, clinical trial phases, and regulatory policy changes. For example, clinical trial reports may update semi-annually or annually, while regulatory guidelines release irregularly based on policy shifts. Document structures are complex, often containing numerous figures, chemical structures, sequence information, and specialized terminology. Fields and units involve specific biochemical and pharmaceutical measurement units, such as nucleic acid sequence length (nt), concentration (nM, µg/mL), purity (%), half-life (h), and dose (mg/kg).

Constraints on Knowledge Base Retrieval and Recall from these Characteristics

The complex data characteristics of siRNA nucleic acid drug registration and declaration documents impose specific constraints on knowledge base retrieval and recall. First, multi-format documents require robust file parsing capabilities from the knowledge base, especially for recognizing figures and chemical structures within PDFs. This directly impacts the completeness of text extraction. Second, frequently updated regulatory documents and clinical data necessitate efficient incremental update mechanisms to ensure the timeliness of retrieval results. Complex internal document structures, such as nested headings and multi-level lists, require refined segmentation strategies to avoid semantic fragmentation or information overload. The presence of specialized terminology and measurement units demands domain adaptability from vectorization models; general models may struggle to accurately understand these specific contexts. Finally, high-precision recall for specific sequence information or chemical structure-related content relies on processing non-textual information and fine-grained filtering of retrieval results to ensure only the most relevant snippets return.

Configuration Recommendations

Configuration Item	Recommended Value	Rationale
`Chunk size`	`800–1200 characters`	Balances long descriptions in siRNA pharmaceutical research reports with short regulatory sentences. This avoids semantic units being truncated or single segments containing excessive information.
`Recall count`	`8–12 entries`	Given the knowledge density of siRNA declaration documents, increasing the number of recalled items improves coverage and reduces the risk of missing critical information.
`Similarity threshold`	`0.75–0.85`	Combined with the unique nature of siRNA specialized terminology, this range effectively filters low-relevance results while retaining semantically similar but differently worded document snippets.
`Rerank result count`	`5 entries`	Focuses on core siRNA registration and declaration issues. This refines initial recall results to provide the most direct answer support.
`PARSE_FILE_TIMEOUT_SECONDS`	`300 seconds`	Addresses potentially long parsing times for large clinical trial reports or pharmaceutical research PDF files, preventing parsing interruptions.
`maxContext`	`6000 tokens`	Ensures sufficient context information when processing siRNA pharmaceutical research or clinical data, preventing loss of critical details.

Common Mistakes

Knowledge base search node user authentication fails, preventing access to knowledge base content. This may result from a mismatch between knowledge base permission configurations and user roles, or incorrect API_KEY in the API request.
Online conversations and API calls return significantly different results, even with the same application, model, knowledge base, and prompt. API calls may lack critical information. This can happen if the detail parameter is not set to true during API calls, leading to insufficient context information.
Chinese content appears garbled after importing the latest CSV file into the knowledge base. This usually occurs because the CSV file encoding is not specified as UTF-8, causing character set mismatches during system parsing.

How to Confirm Correct Configuration

Submit actual queries. Check if retrieval results include key information such as siRNA drug nucleic acid sequences, synthesis process steps, and clinical trial phases. Verify the accuracy and completeness of the returned snippets.
Select different document types (e.g., regulatory guidelines, clinical reports). Perform separate retrieval tests to validate the knowledge base's parsing and recall capabilities for multi-format, multi-structured documents.
Simulate user queries. Check the relevance scores and ranking of retrieval results. Evaluate the effectiveness of Similarity threshold and Rerank result count to ensure highly relevant content displays first.
Review system logs. Confirm no abnormal errors during file parsing, especially whether the PARSE_FILE_TIMEOUT_SECONDS parameter is sufficient for processing large PDF files.

The values provided are common starting points and should be measured against specific 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.