Deployment and Upgrade for siRNA Nucleic Acid Drug R&D

Data Characteristics

siRNA nucleic acid drug R&D documents include experiment reports, clinical trial data, patent literature, regulatory files, and internal research notes. These documents are typically in PDF, DOCX, XLSX, TXT, or scanned image formats. Content covers sequence information, target mechanisms, delivery systems, pharmacokinetic data, toxicology reports, and clinical results. Data sources are diverse, including public databases (e.g., NCBI Gene Expression Omnibus, PubChem), internal LIMS systems, electronic lab notebooks (ELN), and CRO reports. Update frequency varies by document type; experiment reports and internal notes may update weekly or daily, clinical trial data updates in phases, and patent/regulatory files have longer cycles. Document structures differ significantly, ranging from highly structured tabular data to extensive unstructured text and figures. Field units include molar concentration, nanomolar, microgram, milliliter, percentage, and other biochemical and pharmaceutical units.

Constraints on Deployment and Upgrade

The heterogeneity and diversity of siRNA nucleic acid drug R&D documents impose specific deployment and upgrade constraints. Large volumes of unstructured text and scanned images require advanced OCR capabilities and multimodal parsing models for complete information extraction. This necessitates integrating high-performance OCR services and multimodal input support during FastGPT deployment. Varying update frequencies demand a flexible incremental update mechanism to efficiently identify and process new document versions, avoiding redundant parsing. Complex data sources mean deployment must consider multi-data source connector configuration and permission management for data security and traceability. Specialized biochemical units and terminology, such as IC50 and Kd values, require the model to have strong domain-specific named entity recognition during training. Upgrades must continuously optimize the model's semantic understanding of these entities to reduce parsing errors and ensure accurate structured output. For offline deployment, local storage capacity for model files and knowledge bases becomes a critical consideration.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`200 MB`	siRNA experiment reports often contain high-resolution figures, requiring support for large file uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	OCR and parsing of large amounts of unstructured text and scanned documents are time-consuming; avoid parsing timeouts.
`Chunk size` (Chunk Length)	`800–1200 characters`	Balances contextual completeness and retrieval efficiency, suitable for the length of siRNA mechanism descriptions.
`Recall count` (Recall Count)	`Top 10 entries`	Ensures comprehensive recall of key information such as siRNA sequences, targets, and delivery systems.
`Similarity threshold` (Similarity Threshold)	`0.78`	Ensures retrieval results are highly relevant to siRNA R&D specific terminology and data, avoiding irrelevant information interference.
`Rerank result count` (Reranked Return Count)	`Top 5 entries`	Focuses on the most relevant siRNA experimental data, conclusions, or patent information, improving efficiency in obtaining valid information.

Three Common Pitfalls

Symptom: System logs show an HTTP 500 error with content Model inference failed. Cause: Insufficient GPU memory or incompatible driver versions in the deployment environment prevent multimodal parsing models from loading or running correctly.
Symptom: After uploading a PDF document, some tabular data or chemical structures are not parsed, and corresponding fields are empty. Cause: The OCR service's capability to handle complex layouts or low-clarity scanned documents is insufficient, failing to correctly identify all visual elements.
Symptom: After a knowledge base update, query results still show old versions or incorrect data, failing to reflect the latest research progress. Cause: The incremental update mechanism did not effectively identify document version differences, or caching policies prevented timely invalidation of old data.

How to Verify Correct Configuration

Select an siRNA experiment report with complex tables and figures. Upload it and check the parsed structured data. Ensure key fields like IC50, Kd values, and sequence information are accurately extracted with correct units.
Choose an siRNA patent document containing the latest research advancements. Upload it and perform queries. Verify the system can recall and utilize new targets, delivery systems, and other information from it.
Simulate high-concurrency upload and query scenarios. Monitor system resources (CPU, memory, GPU utilization) and response times. Confirm the system remains stable under heavy load without timeouts or crashes.
Retrieve information for specific siRNA sequences or compound names. Cross-reference the recalled results with the original document content to ensure the similarity threshold is set appropriately.

Note: The values provided are common starting points. Measure against specific samples to determine optimal settings.

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.