Data Characteristics for this Category
mRNA vaccine product data primarily originates from clinical trial reports, drug labels, research papers, regulatory approval documents, and patent literature. This data updates frequently, especially during early development and market launch, as clinical progress and real-world evidence accumulate. Document structures are often standardized; drug labels and regulatory documents typically use sections like pharmacology and toxicology, indications, dosage and administration, and adverse reactions. Research papers follow academic journal conventions. Fields and units are highly specialized. For example, dosages are often in micrograms (μg), antibody titers in international units (IU/mL), adverse event rates in percentages, and half-lives in hours or days.
Constraints on Knowledge Base Retrieval and Recall
The specialized and standardized nature of mRNA vaccine data imposes specific requirements on knowledge base retrieval and recall. Standardized document structures allow for structured parsing during data ingestion. This enables precise extraction and indexing of specific fields, such as directly locating adverse reaction sections. High update frequency necessitates an efficient incremental update mechanism for the knowledge base, ensuring retrieved information is always current. Specialized fields, units, and a large volume of medical terminology and abbreviations require targeted optimization in lexical analysis and semantic understanding. This avoids recall bias due to inaccurate recognition of specialized vocabulary. Additionally, due to diverse data sources and potential redundancies, deduplication and version management are critical. This prevents the recall of redundant or outdated content.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size (Chunk Length) | 500–700 characters | Accommodates the average paragraph length in mRNA vaccine labels and research papers, balancing contextual completeness with retrieval granularity. |
Recall count (Recall Count) | Top 8–12 entries | Increases the number of recalled items to cover a broader range of potentially relevant knowledge points, given the complexity of mRNA vaccine information. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Ensures recalled results are highly semantically relevant to the user's query, filtering out low-quality matches. |
Rerank result count (Reranked Return Count) | Top 5 entries | Further optimizes relevance through a reranking model based on initial recall, focusing on the most core information. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Handles potentially long parsing times for large clinical trial reports or research review documents. |
maxContext | 3000–4000 tokens | Provides a sufficiently long context window for the large language model to understand complex medical concepts and multifaceted information. |
Three Common Mistakes
- Knowledge base search results are unusually sparse or return content unrelated to the query. This often results from a
Similarity threshold(Similarity Threshold) set too high, or from a chunking strategy that splits key information, failing to form effective context. - After a user query, the answer content is too brief and lacks specific clauses or detailed data from the documents. This may relate to a
maxContextsetting that is too small, preventing the large language model from accessing enough original text for summarization. - Saved knowledge base configurations revert to default values after a refresh. This typically indicates an issue with the system's frontend cache or backend configuration persistence mechanism. It may require checking FastGPT's version compatibility or database write permissions.
How to Confirm Proper Configuration
- For typical queries, manually inspect the recalled knowledge base document snippets. Confirm their content is highly relevant to the query intent and covers key information points.
- Test with mRNA vaccine-related questions of varying complexity. Observe whether the large language model's output is detailed and accurate, and includes specific data or clauses.
- Check system logs for
PARSE_FILE_TIMEOUT_SECONDSrelated alerts. Confirm that large document parsing does not time out. - After modifying configurations, refresh the page or restart the service. Confirm that all parameter settings are correctly saved and applied.
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.