Deployment and Upgrade for siRNA Nucleic Acid Drug

Data Characteristics for this Category

siRNA nucleic acid drug registration and declaration data comes from diverse sources. These include clinical trial reports, non-clinical study reports (e.g., toxicology, pharmacokinetics), manufacturing process documents, quality standards, stability study data, and domestic and international regulatory guidelines. This data typically exists as a mix of structured (e.g., clinical trial databases, HPLC/GC chromatogram data) and unstructured formats (e.g., PDF research reports, Word documents of regulatory interpretations, scanned raw records). Update frequency is driven by R&D progress, clinical batches, regulatory changes, and post-market surveillance results. This can involve periodic large-scale updates and daily minor revisions. Document structure is complex, containing numerous technical terms, acronyms, chemical structural formulas, experimental data tables, and chromatograms. Fields and units are highly specific. Examples include nucleic acid sequence information, modification sites, purity percentages (%), degradation product content (ppm), drug concentration (nM/µM), dosage (mg/kg), and various pharmacokinetic parameters (e.g., Cmax, AUC, T1/2).

Constraints Imposed by These Characteristics on "Deployment and Upgrade"

The complex data characteristics of siRNA nucleic acid drug registration and declaration data impose specific constraints on FastGPT's deployment and upgrade. First, a large volume of unstructured documents, especially scanned documents and complex tables, requires the RAG module to have robust OCR and table parsing capabilities. Without this, information extraction can be incomplete or incorrect. Second, the intensive use of technical terms and acronyms means the model requires additional domain knowledge injection to ensure accurate semantic understanding. This impacts the selection and fine-tuning of embedding and retrieval models. The periodic and daily nature of data updates requires the system to smoothly handle incremental data indexing during upgrades, avoiding prolonged downtime. Furthermore, the extremely high demand for data accuracy and consistency means any upgrade must include strict validation processes. This is especially true when updating underlying data processing logic, ensuring correct extraction and comparison of critical values like drug concentration and purity. The characteristic of large files and mixed formats also places higher demands on the stability and compatibility of the file upload and storage system.

Configuration Strategy

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports or manufacturing process documents often contain high-resolution chromatograms and large amounts of data, resulting in large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Processing complex PDF reports and documents with many tables can take a long time.
`Chunk size`	`800–1200 characters`	siRNA drug data is highly specialized with tight contextual relevance. Longer segments help preserve semantic integrity.
`Recall count`	`Top 10 entries`	Ensures broader coverage of potentially relevant information during retrieval, increasing the hit rate for complex queries.
`Similarity threshold`	`0.78–0.85`	For highly specialized terms and data, a balance between precision and recall is necessary.
`Rerank result count`	`Top 5 entries`	Re-ranks retrieved results to ensure the most relevant key information is presented first.

Three Common Pitfalls

After an upgrade, file dataset uploads fail, with logs showing Failed to parse URL from /model/getProviders or 500 Internal Server Error. This typically indicates that model service configuration was not correctly updated or loaded during or after the upgrade, preventing the file parsing service from initializing or communicating with the backend.
The chat window shows abnormal errors, or responses lack professionalism, with misunderstandings of siRNA-specific terminology. This might be due to not synchronously updating or retraining domain-specific embedding models and knowledge bases during the upgrade. This leads to incompatibility between the new model version and the old specialized knowledge base, or outdated knowledge.
After uploading text or files, the system remains unresponsive for a long time or eventually times out. This happens because parameters like PARSE_FILE_TIMEOUT_SECONDS or UPLOAD_FILE_FILE_MAX_SIZE are set too low. They cannot handle the large files or complex parsing tasks common in siRNA declaration data.

How to Verify Correct Configuration

Upload a PDF document containing siRNA sequences, modification information, and pharmacokinetic parameters. Check if the knowledge base correctly extracts and indexes all key fields and values, such as Cmax, T1/2, and purity (%).
Perform a complex query, such as "Summarize the toxicology study results of XX siRNA drug in monkeys and list the main findings." Verify if the model can accurately integrate information from multiple documents and provide a coherent answer containing technical terms.
Check system logs to ensure no 4XX or 5XX error codes appear during file upload, parsing, and knowledge base updates. Also, confirm that PARSE_FILE_TIMEOUT_SECONDS is not frequently triggered.

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.