HTTP Interface and External Systems for siRNA Nucleic Acid

Data Characteristics

siRNA nucleic acid drug registration and declaration documents originate from various sources. These include clinical trial reports, non-clinical study reports, manufacturing process documents, quality standards, stability study data, pharmacology and toxicology study data, and regulatory guidelines. Documents are often in multiple formats such as PDF, Word, and Excel. Some raw data may be stored in CSV or JSON format.

Data updates are driven by research and development progress and regulatory requirements. For example, clinical trial results update periodically, and revisions to manufacturing processes or quality standards lead to document version iterations. Document structures are typically highly standardized, adhering to ICH guidelines or specific templates from national drug regulatory agencies, such as the CTD (Common Technical Document) format.

Fields and units are highly specialized. Examples include pharmacokinetic parameters (AUC, Cmax, in ng·h/mL), pharmacodynamic indicators (gene silencing efficiency, in %), impurity content (in % or ppm), formulation component ratios (in mg/mL), batch information, production dates, and expiration dates.

Constraints Imposed by These Characteristics on "HTTP Interface and External Systems"

The multi-source and diverse nature of siRNA nucleic acid drug documents requires HTTP interfaces to have robust file type recognition and parsing capabilities.

The periodic data updates and version iterations mean external systems must support incremental synchronization or version management. This avoids redundant processing and data duplication.

Strict document structures and specialized fields demand high-level data extraction and structuring capabilities from the interface. It must accurately identify and extract key information, such as parsing specific pharmacokinetic tables or gene silencing efficiency chart data from PDF reports.

Specialized fields and units require external systems to correctly handle floating-point precision, unit conversion, and data validation during data transmission and storage. This prevents parsing errors or information loss due to data format mismatches.

Furthermore, the transmission efficiency and stability of large files, such as clinical trial report PDFs, are critical considerations.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Clinical trial reports or manufacturing process documents may contain numerous charts and attachments, leading to large file sizes.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Parsing large PDFs or documents with complex structures can be time-consuming, requiring sufficient time for processing.
`maxContext`	`3000–4000 characters`	The mechanism of action of siRNA and registration requirements involve extensive specialized terminology and context, necessitating a longer context window.
`Chunk size`	`800–1200 characters`	Ensures each segment contains complete professional concepts or sentences, preventing semantic truncation.
`Similarity threshold`	`0.75–0.85`	Ensures retrieval results are highly relevant to the queried professional terms and concepts, reducing interference from irrelevant information.
`Rerank result count`	`Top 10–15 entries`	Queries for registration and declaration documents often require deeply related information; increasing the number of recalled items helps cover a more comprehensive background.

Common Pitfalls

The HTTP interface returns a 413 Payload Too Large error. This occurs because the uploaded file size exceeds the server's configured limit.
After importing a file, the external system remains in an "indexing" state for an extended period, and logs show a timeout error. This is due to large file parsing exceeding the timeout limit.
Key fields like AUC or Cmax values are missing or incorrectly formatted in structured data synchronized from the external system. This happens because data extraction rules fail to accurately match specific table structures or floating-point formats in PDFs or Excel files.

Verification Steps

Upload multiple siRNA nucleic acid drug registration and declaration documents of different types and sizes (e.g., clinical trial report PDFs, manufacturing process Word documents). Check if the files upload successfully and begin parsing.
Using the external system's logs or status interface, verify that the file parsing process completes normally. Confirm no timeout or parsing failure error messages appear.
Perform keyword queries on the indexed documents, specifically for specialized terms like pharmacokinetic parameters and gene silencing efficiency. Verify the accuracy and completeness of the returned results and confirm the number of recalled items matches the configuration.
Inspect the structured data returned from the external system. Validate that key fields (e.g., batch number, test indicator values, and units) are accurately identified and extracted, and that the data format meets expectations.

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.