mRNA Vaccine Regulation Deployment and Upgrade

Data Characteristics for this Category

mRNA vaccine-related regulations and Standard Operating Procedure (SOP) documents typically originate from technical guidelines published by drug regulatory agencies, internal quality management system documents from pharmaceutical companies, clinical trial protocols, manufacturing process documents, and post-market surveillance reports. Document updates are relatively stable, primarily occurring during regulatory revisions, new product launches, or process optimizations. Documents are mainly in PDF, Word, or structured XML formats, containing extensive technical terms, professional acronyms, diagrams, and flowcharts. Fields include dosage units (e.g., μg), purity indicators (e.g., > 95%), storage conditions (e.g., -80°C), expiration dates (e.g., month/year), batch information, and adverse reaction classification codes.

Constraints from these Characteristics on "Deployment and Upgrade"

The specialized nature and varied formats of mRNA vaccine regulation documents demand high document parsing capabilities during deployment, especially for extracting text embedded within diagrams and flowcharts. The stable update rhythm means that incremental knowledge base updates can be performed periodically, without frequent full rebuilds. Specific units of measurement and professional terms in documents, such as μg, °C, and batch number, require special handling during vectorization and retrieval to prevent semantic loss due to unit or format differences. Additionally, the need for precise matching of batch information and adverse reaction codes dictates that retrieval must support exact matching and multi-field combined queries.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800–1200 characters`	Ensures semantic completeness of regulatory clauses and prevents truncation of critical information.
`Overlap Length`	`100–200 characters`	Maintains contextual coherence and improves accuracy of cross-paragraph information retrieval.
`Similarity threshold` (Similarity Threshold)	`0.75` (based on `cos` similarity)	Accurately matches professional terms and regulatory clauses, filtering out irrelevant content.
`maxContext`	`8000` (tokens)	Accommodates the complexity and depth of regulatory documents, allowing for more contextual information.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds`	Handles parsing time for large PDF or Word documents.
`Recall count` (Recall Count)	`Top 8`	Controls the input length processed by the LLM while ensuring recall rate.

Common Pitfalls

Symptom: System prompts "file parsing failed" or some document content is missing. Reason: Complex diagrams or scanned images embedded in PDF documents are not correctly recognized and text-extracted by the OCR module.
Symptom: User queries for specific batch numbers or adverse reaction codes return inaccurate results. Reason: These specific fields were not specially processed during index building, or synonym expansion for acronyms is lacking.
Symptom: FastGPT fails to start normally after a power outage and restart, with database connection failures. Reason: In a Docker deployment environment, PostgreSQL or MongoDB database containers experience data volume corruption or loss of connection configuration after an abnormal shutdown.

How to Verify Correct Configuration

Upload an mRNA vaccine SOP document containing complex diagrams and specific units of measurement. Check if its content is fully and accurately indexed.
Perform precise queries for a batch number, storage temperature (e.g., -70°C), or expiration date mentioned in the document. Verify the accuracy of the recalled results.
Simulate an abnormal system shutdown or container restart. Check if the FastGPT service and its dependent database services can automatically recover and operate normally.
Compare queries of different lengths and complexities. Evaluate the relevance and completeness of the returned results to ensure that Similarity threshold (Similarity Threshold) and Recall count (Recall Count) are appropriately configured.

The values provided are common starting points. They 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.