Data Characteristics
siRNA nucleic acid drug clinical trial pre-screening involves diverse data sources. These include public clinical trial registries (e.g., ClinicalTrials.gov, EudraCT), pharmaceutical company reports, academic journal papers, patent literature, and regulatory approval documents (e.g., FDA, EMA). Data update frequencies vary; clinical trial registries might update weekly, while academic papers and approval documents release in batches. Document structures typically consist of unstructured text (e.g., trial protocols, research report PDFs) and semi-structured data (e.g., ClinicalTrials.gov XML or JSON exports). Key fields include study name, trial phase, target gene, siRNA sequence, administration method, inclusion/exclusion criteria, primary/secondary endpoints, adverse event reports, and sponsor information. siRNA sequences often use IUPAC nucleic acid abbreviations. Dosage units may involve mg/kg or mg, and time units include weeks, months, or years. Precise information extraction requires handling these specific formats.
Constraints from "Citation and Traceability"
The diverse and unstructured nature of siRNA nucleic acid drug data challenges accurate source identification and traceability. Detailed inclusion criteria and biomarker information within clinical trial protocols are often embedded in lengthy PDF documents, requiring robust text parsing capabilities for effective extraction. The specificity of siRNA sequences demands the system accurately identify and link to relevant patents or literature, ensuring the uniqueness and authority of sequence information. Varying update frequencies across different data sources mean citation results must clearly indicate data timestamps to avoid referencing outdated information. For example, an old clinical trial report might contain conclusions superseded by subsequent research. Furthermore, multilingual materials (e.g., some European clinical trial data may offer multilingual versions) require the system to support cross-language retrieval and traceability, ensuring that even a Chinese query can cite original English or German literature. For dosage and time units, the system needs to recognize and standardize them to prevent information confusion or misunderstanding due to inconsistent units.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
Chunk size (Segment Length) | 500 characters | Clinical trial document paragraphs are typically long. 500 characters help maintain contextual completeness and prevent key information from being cut off. |
Chunk Overlap Length (Segment Overlap Length) | 100 characters | Ensures information at segment boundaries is not lost, helping the RAG model understand cross-paragraph relationships, especially for complex inclusion/exclusion criteria. |
Recall count (Recall Count) | top 8 | Given the complexity and multi-dimensional information in siRNA research, increasing the recall count improves retrieval relevance, particularly for trials targeting different targets or phases. |
Similarity threshold (Similarity Threshold) | 0.75 | A higher threshold helps filter out literature less relevant to siRNA sequences, targets, or trial phases, reducing noise. |
Rerank result count (Reranked Return Count) | top 3 | After reranking, selecting the top few most relevant items as final citations reduces user reading burden and improves information focus. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large PDF clinical trial reports and patent files may require longer parsing times to avoid parsing failures due to timeouts. |
Common Misconfigurations
- After entering a Chinese query, the system fails to cite English literature from the knowledge base. This might be due to the knowledge base not being configured for multilingual document processing, or the embedding model lacking cross-language understanding capabilities.
- The knowledge base returns content even when the query is clearly irrelevant. This might be due to a
Similarity Thresholdset too low, leading to the recall of many low-relevance documents. - Citations regarding siRNA sequences are ambiguous or incomplete. This might be due to the original document parsing failing to accurately identify and extract complete sequence information fields.
How to Verify Configuration
- For a specific siRNA target or sequence, use a Chinese query. Check if the returned citations include relevant original English literature and verify if key data (e.g., trial phase, dosage) in the citations matches the original literature.
- Use a query completely unrelated to the knowledge base content. Check if the system returns "No relevant information found" or only a very small number of low-relevance results. This verifies the effectiveness of the
Similarity Threshold. - Randomly select a detailed clinical trial report on siRNA drugs from the knowledge base. Ask FastGPT about its inclusion criteria or primary endpoints. Verify if the citation sources can pinpoint the exact paragraphs in the original report and if extracted key information like sequences and dosages are complete and accurate.
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.