Database and Operations for siRNA Nucleic Acid Drug R&D

Data Characteristics

siRNA nucleic acid drug R&D data primarily originates from experimental reports, patent literature, clinical trial records, and academic papers. These documents contain sequence information, modification types, target binding data, and in vitro/in vivo pharmacodynamic and pharmacokinetic results. Data updates frequently, especially experimental data during early R&D stages, with new batches potentially generated weekly or even daily. Document structures vary, ranging from unstructured free text descriptions to semi-structured experimental tables and graphs. Fields include common compound numbers, dosages, and administration routes, alongside unique fields such as "siRNA sequence," "chemical modification site," "target gene expression inhibition rate," and "off-target effects." Units cover molar concentration (nM), inhibition rate (%), half-life (h), and cytotoxicity (IC50).

Constraints Imposed by Data Characteristics on Database and Operations

The precision required for siRNA sequences and modification sites demands a database that supports exact matching and efficient fuzzy querying. High update frequency necessitates a database with good write performance and incremental indexing capabilities. Data version management is also crucial for tracing experimental results across different stages. Diverse document structures require a flexible document model capable of storing unstructured text and semi-structured tabular data, with support for multi-field indexing. The presence of unique fields requires the database to handle complex data types, such as long sequence strings, and support specific bioinformatics operations during queries. Standardized storage and query constraints for units require data cleansing and validation mechanisms to ensure numerical accuracy and consistency, preventing misinterpretation due to unit confusion.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`DATABASE_URL`	`mongodb://user:password@host:port/db`	FastGPT supports MongoDB by default; this is the standard connection string format.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	siRNA R&D documents may contain numerous images and graphs, ensuring large file uploads.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Complex experimental reports and patent documents require longer parsing times; this prevents timeouts.
`Chunk size` (Chunk Length)	`800–1200 characters`	Ensures the integrity of critical biological entities like siRNA sequences and modification information, preventing truncation.
`Recall count` (Recall Count)	`Top 10 entries`	Ensures sufficient relevant experimental data and literature snippets are covered during the initial recall phase.
`Similarity threshold` (Similarity Threshold)	Determined by actual measurement	For siRNA sequence similarity matching, adjust this value through actual testing to balance recall rate and accuracy.

Common Pitfalls

When connecting to the database, pnpm dev showing a connection timeout usually indicates an incorrect host address or port in the DATABASE_URL configuration, or a firewall blocking the connection.
Discrepancies between Token consumption when querying MongoDB via FastGPT and actual API Token consumption may occur because FastGPT internally processes or reconstructs query results, increasing the context length sent to the model.
After uploading files, critical fields such as "siRNA sequence" or "target gene" may be empty. This happens when the parser fails to correctly identify specific biological entities in the document, requiring adjustment of parsing rules or custom extraction logic.

Verification Steps

Upload siRNA R&D documents in various formats (PDF, DOCX, TXT) and observe parsing progress and results to ensure documents are successfully chunked and ingested.
Execute queries containing keywords like siRNA sequence, target, and pharmacodynamic data. Verify that recall results include relevant document snippets and that extracted field information is accurate.
Simulate high-concurrency document uploads and query requests. Monitor database CPU, memory, and I/O usage to confirm system stability under heavy load and that response times meet expected thresholds.

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