Data Characteristics
siRNA nucleic acid drug pharmacovigilance data comes primarily from clinical trial reports, real-world evidence (RWE) data, case reports, medical literature, and regulatory safety updates. This data exists as unstructured text (e.g., clinical study reports, case narratives), semi-structured tables (e.g., adverse event lists, laboratory test results), and structured databases (e.g., MedDRA coding). Data updates frequently, especially during initial drug launch and Phase IV clinical stages, when regulatory bodies regularly issue safety summary reports. Document structures are complex, often containing extensive specialized terminology, abbreviations, and dosage units (e.g., mg/kg, µg/mL). Adverse event descriptions typically include the event name, onset time, duration, severity, outcome, and drug-relatedness assessment.
Constraints Imposed by Data Characteristics on Multiturn Conversation and Prompts
The high update frequency of siRNA nucleic acid drug data requires the knowledge base to support rapid updates and incremental synchronization. This ensures the conversational model accesses the latest drug safety information. The coexistence of unstructured and semi-structured data necessitates more refined text parsing and information extraction techniques during data preprocessing. This ensures accurate identification of key adverse events, dosages, and timelines, transforming them into queryable knowledge. The use of specialized terminology and abbreviations demands robust prompts and strong domain vocabulary understanding from the model, preventing retrieval failures due to unrecognized terms. In multiturn conversations, remembering and associating contextual information such as drug dosage, administration route, and concomitant medications is crucial for accurately assessing adverse event drug-relatedness. The model must effectively track and utilize entity information from the conversation history.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 6 rounds | Balances contextual coherence with computational resources, covering common pharmacovigilance query scenarios. |
Chunk size | 800–1200 characters | Accommodates longer paragraphs in clinical reports and literature, ensuring complete adverse event descriptions. |
Recall count | Top 5 entries | Balances recall rate with model processing burden, prioritizing the most relevant safety information. |
Similarity threshold | 0.75–0.85 | Ensures retrieved knowledge snippets are highly relevant to the query, reducing noise. |
Rerank result count | Top 3 entries | Further refines results, placing the most critical adverse events or related evidence prominently. |
UPLOAD_FILE_MAX_SIZE | 100 MB | Meets the size requirements for clinical study report PDFs or structured data files. |
Common Pitfalls
- The conversation states "no relevant adverse event information found" or "specific dosage not mentioned." This indicates insufficient extraction of adverse event details from unstructured text or that the knowledge base did not effectively index this information.
- After a user query, the model replies "please provide more context." This occurs when the model fails to effectively remember or associate critical contextual information (e.g., patient comorbidities, concomitant medications) from previous turns in a multiturn conversation.
- Uploading a large safety report results in a processing timeout or incomplete content. This may be due to
PARSE_FILE_TIMEOUT_SECONDSbeing set too short or insufficient resources for the file parsing module'sembeddingprocess.
Verification of Configuration
- Test typical siRNA nucleic acid drug adverse event queries. Verify if multiturn conversations accurately track patient information (e.g., age, underlying diseases) and drug exposure information (e.g., dosage, duration of treatment), and provide detailed descriptions of relevant adverse events.
- Upload a clinical study report PDF containing complex medical terminology and abbreviations. Verify if the system accurately parses and extracts key fields such as adverse event occurrences, relatedness assessments, and severity.
- Simulate a query for "incidence of liver function abnormalities for a certain siRNA drug." Check if the retrieved knowledge snippets include the latest safety updates or revised data issued by regulatory agencies, and verify the data source and publication date.
- Check if drug dosage units (e.g., mg/kg, µg/mL) are correctly recognized and indexed in the knowledge base. This ensures accurate retrieval when querying specific dosage-related adverse events.
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.