Data Characteristics for this Category
siRNA nucleic acid drug regulations and SOP documents typically exist as PDFs, Word files, or exports from internal knowledge base systems. These documents are highly structured. They cover all stages from R&D and clinical trials to production, quality control, and post-market surveillance. Regulatory requirements, R&D progress, and production process optimizations influence update frequency. Updates may occur quarterly or semi-annually, involving version control and revision history. Document content includes extensive specialized terminology, abbreviations, and specific units of measurement. Examples include molar concentration (nM), sequence length (nt), purity (%), and production-related fields such as batch number and expiration date. The text volume is large; a single file can contain tens to hundreds of pages.
Constraints on "Deployment and Upgrade" from these Characteristics
The structured and specialized nature of siRNA nucleic acid drug regulation documents requires FastGPT to have robust parsing capabilities during data ingestion to accurately extract key information. The document update frequency dictates a strategy for regular knowledge base synchronization and incremental updates, requiring efficient version management. The large number of specialized terms and abbreviations makes model fine-tuning and domain vocabulary building critical for improving answer accuracy. Additionally, specific units of measurement and production fields in the documents impose higher demands on the QA system's data validation and result presentation. Deployment requires configuring specialized entity recognition and unit conversion rules. The large document size challenges file upload and processing performance and timeout settings.
Configuration Settings
| Configuration Item | Recommended Value | Rationale for this Value |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Ensures large SOP or regulatory documents can be uploaded, avoiding 413 errors. |
PARSE_FILE_TIMEOUT_SECONDS | 600 | Handles complex PDF parsing, preventing parsing failures due to excessive time. |
Chunk size (Chunk Length) | 800 characters | Balances context completeness and retrieval efficiency for lengthy regulatory clauses. |
Overlap Length | 100 characters | Ensures necessary contextual continuity between chunks, improving retrieval coherence. |
maxContext | 8192 | Accommodates long-text QA scenarios, especially when multiple regulatory references are involved. |
Recall count (Recall Items) | 8 entries | Increases coverage of relevant regulatory clauses, improving answer accuracy. |
Three Common Mistakes
- A 413 error after file upload usually indicates that the
client_max_body_sizeparameter in Nginx or the FastGPT Docker container is set too low to handle large regulatory documents. - Document parsing progress stalls or fails for an extended period, with logs showing
PARSE_FILE_TIMEOUT. This typically occurs whenPARSE_FILE_TIMEOUT_SECONDSis insufficient for parsing complex PDFs. - QA results lack accurate explanations of specialized terminology or contain incorrect units of measurement. This suggests insufficient model fine-tuning for domain vocabulary or a lack of configured domain entity recognition rules.
How to Verify Correct Configuration
- Upload and parse a 100MB regulatory document containing images and tables. Check if parsing is successful and verify that corresponding chunks are generated in the knowledge base.
- Ask questions using siRNA nucleic acid drug specialized terminology and units of measurement. Check if the model's answers are accurate and if the cited source paragraphs are complete.
- Simulate a regulation update scenario by uploading a revised document. Verify that the knowledge base correctly handles version differences and that the QA system prioritizes information from the latest version.
- Review system logs to ensure no file size or timeout-related error messages occurred during file upload and parsing.
The values given are common starting points and should be measured against the reader's 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.