Deployment and Upgrade for siRNA Nucleic Acid Drug

Data Characteristics

siRNA nucleic acid drug pharmacovigilance data comes primarily from clinical trial reports, real-world evidence (RWE) studies, post-market surveillance reports, and public databases from global drug regulatory agencies (e.g., FDA, EMA). Data updates are frequent, especially during the initial launch phase of new drugs, when regulatory agencies regularly publish safety updates. Document structures typically include patient demographics, medication history, adverse event descriptions (including onset time, severity, outcome), relevant laboratory test results, and healthcare professional assessments. Fields and units are specific. Examples include off-target effect indicators related to siRNA mechanisms, immunogenicity reactions unique to nucleic acid drugs, and dynamic changes in liver and kidney function indicators (e.g., ALT, AST, creatinine). Units for these indicators are commonly International Units (IU), molar concentration (mol/L), or mass concentration (mg/dL).

Constraints on Deployment and Upgrade

The high update frequency of siRNA nucleic acid drug data requires FastGPT to support rapid and flexible knowledge base synchronization and updates after deployment, preventing information lag. The complex document structures and specific fields challenge the robustness and field extraction capabilities of the document parser. This requires configuring specialized parsing rules to accurately identify adverse event details and relevant biomarkers. Specifically, specialized terms like immunogenicity or off-target effects unique to nucleic acid drugs necessitate optimization of vector or embedding models to improve retrieval accuracy. When integrating data from multiple sources, data format inconsistencies may arise. This requires FastGPT's data import module to have strong preprocessing and standardization capabilities. Furthermore, patient privacy and sensitive medical information require strict data security and access management. Deployment must ensure access control and data encryption comply with regulatory standards.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`100 MB`	Clinical trial reports and real-world study documents can contain numerous charts and detailed descriptions, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDF documents or reports with complex tables can be time-consuming, preventing parsing interruptions.
`maxContext`	`8000 characters`	Adverse event descriptions for siRNA nucleic acid drugs are often detailed, requiring a longer context window to capture complete information.
`Chunk size` (Segment Length)	`500–800 characters`	Ensures each knowledge block contains sufficient adverse event context while avoiding excessive length that could lead to information redundancy or decreased retrieval efficiency.
`Similarity threshold` (Similarity Threshold)	`0.75–0.85`	The terminology in siRNA pharmacovigilance is highly specialized. Increasing the threshold allows for more precise matching of relevant adverse reaction descriptions.
`Rerank result count` (Reranked Results Count)	`10 entries`	The associations of nucleic acid drug adverse reactions can be complex. Increasing the number of reranked entries helps discover potentially weak associations.

Common Pitfalls

After uploading a document in chat, the system displays "Parsing failed" or returns empty content. This can occur if the PDF document contains many scanned images or non-text layers, preventing the default parser from extracting valid text.
Workflow calls show gpt-4o-mini model-related error logs. This might be due to incompatibility between the locally deployed FastGPT version or model configuration and the preset model in the workflow, or an incorrect BASE_URL configuration preventing access to the model service.
Queries for immunogenicity adverse reactions of specific siRNA drugs return unexpected or missing critical information. This could be because the knowledge base segmentation strategy failed to preserve the complete context of immunogenicity-related terms, or the embedding model's understanding of such specialized terms is insufficient.

Verification Steps

Upload various formats of siRNA nucleic acid drug-related documents (e.g., PDF, DOCX). Verify that the document content is fully and accurately parsed, and that retrievable knowledge blocks are generated.
Through the FastGPT management interface, check that key parameters like UPLOAD_FILE_MAX_SIZE and PARSE_FILE_TIMEOUT_SECONDS match the configured values.
Perform queries for typical siRNA drug adverse reactions. Observe whether the retrieval results include sufficient detailed information and relevant laboratory indicators. Compare these with the original documents to confirm that the recall count and similarity meet expectations.
Validate steps involving model calls within workflows. Ensure the BASE_URL is correctly configured and that the selected model responds normally, without gpt-4o-mini or other model-related call errors.

Note: 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.