Model Integration and Configuration for siRNA Nucleic Acid

Data Characteristics for this Category

siRNA nucleic acid drug pharmacovigilance data originates primarily from clinical trial reports, real-world evidence (RWE) studies, post-market surveillance, and adverse event databases from global drug regulatory agencies. This data exists in both structured and unstructured forms. Structured data includes patient demographics, Adverse Event (AE) codes (e.g., MedDRA codes), dosage, onset time, and outcomes, often found in CSV or XML reports. Unstructured data contains detailed clinical descriptions, physician notes, and patient interview texts, typically in PDF, DOCX, or plain text formats. Data updates frequently, especially during initial drug launch and critical clinical phases; regulatory databases may update weekly or monthly. Beyond standard drug names and batch numbers, fields also include siRNA-specific target information, delivery system types, and descriptions of adverse events related to specific administration routes (e.g., subcutaneous injection, intravenous injection). Units are standard medical units like milligrams (mg), milliliters (mL), and days, while adverse event severity uses internationally recognized grading scales.

Constraints on Model Integration and Configuration

The diverse sources and frequent updates of siRNA nucleic acid drug data require model integration with high concurrent processing capability and flexible data source adaptability. Structured data maps directly to knowledge base fields. However, the complexity of unstructured text demands robust text parsing, especially for accurate extraction of medical terminology and adverse event descriptions. Due to the hierarchical structure of MedDRA coding, the model must support multi-level classification recognition and handle compatibility issues across different MedDRA code versions. High update frequency means the knowledge base needs to support incremental updates and rapid index rebuilding to ensure real-time pharmacovigilance information. Furthermore, siRNA drug-specific target and delivery system information requires the model to accurately differentiate these specialized terms during entity recognition and relationship extraction, avoiding confusion with traditional small molecule drugs. Abbreviations and colloquialisms in the text also necessitate advanced preprocessing and normalization capabilities from the model.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical reports and RWE documents can contain numerous charts and detailed descriptions, leading to large individual file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing complex PDF and DOCX files, especially those with tables and embedded objects, requires significant time.
`Chunk size` (Segment Length)	`800–1200 characters`	Balances the completeness of adverse event descriptions with retrieval efficiency, ensuring sufficient context within a single segment.
`Recall count` (Recall Count)	`Top 10`	Pharmacovigilance analysis requires comprehensive access to relevant information; increasing the recall count covers more potential associations.
`Similarity threshold` (Similarity Threshold)	`0.75` or calibrated by actual measurements	Ensures recalled adverse event descriptions are highly relevant to the query, filtering out low-quality matches.
`Rerank result count` (Reranked Return Count)	`Top 5`	Refines the initial recall results, prioritizing the most critical adverse event reports to improve analysis efficiency.

Three Common Pitfalls

ValueError or TypeError when calling local models due to non-text content in message: This typically indicates improper input data preprocessing, where the model interface expects pure text but receives JSON objects or binary data.
Excessive vector or hybrid retrieval time leading to timeout: This may be due to high network latency between the embedding_model service deployment and the FastGPT service, or an excessively large Recall count (Recall Count) parameter, increasing the load on the vector database.
Empty or incomplete adverse event field values in the knowledge base: This occurs when the file parser fails to correctly identify and extract specific fields from unstructured documents, especially complex table structures or non-standard medical text formats.

Configuration Validation

Upload representative siRNA nucleic acid drug adverse event report documents. Check if key fields such as drug name, target, delivery system, MedDRA codes, and adverse event descriptions are successfully extracted into the knowledge base. Verify field values against the original document content for consistency.
Test typical pharmacovigilance queries (e.g., "liver function abnormalities for a specific siRNA drug"). Evaluate the relevance of the model's recall results and assess if recalled items include information from multiple sources (e.g., clinical trials, RWE). Confirm the reasonableness of Similarity threshold (Similarity Threshold) and Recall count (Recall Count).
Monitor model response times, especially when handling complex queries and multi-document retrieval. Ensure response times are within an acceptable range to validate the effectiveness of PARSE_FILE_TIMEOUT_SECONDS and Rerank result count (Reranked Return Count) configurations.

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.