Knowledge Base Retrieval and Recall for mRNA Vaccine

Data Characteristics

mRNA vaccine regulatory submission data primarily includes clinical trial reports, non-clinical study reports, manufacturing process and quality control documents, pharmaceutical research data, and regulatory guidelines. The update frequency of this data is relatively low, occurring mainly when R&D milestones are reached, clinical trial batches are updated, or regulatory policies change. Documents have complex structures, often containing numerous PDF reports and Word documents with embedded tables, images, and charts. Fields and units are highly specialized, for example, dosage units of μg, purity percentages of %, mRNA sequence lengths of nt, lipid nanoparticle (LNP) sizes of nm, and vaccination schedule intervals of days or weeks. The data also contains extensive molecular biology terms, immunology indicators, and pharmacokinetic parameters.

Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall

The complex document structure and specialized fields in mRNA vaccine submission data require specific knowledge base chunking strategies. Pure text chunking may disrupt the context of tables or charts, affecting information integrity. The low update frequency means knowledge base content is relatively stable, with no high demand for real-time synchronization. However, initial construction and major updates require sufficient processing capability. The large volume of specialized terminology and abbreviations necessitates that vector models possess strong domain-specific semantic understanding to avoid low recall due to vocabulary differences. Furthermore, strong cross-references and data correlations between different reports mean that ordinary local recall may not provide complete decision-making information, requiring consideration of multi-paragraph or multi-document associative recall. While the data volume is large, the granularity of individual pieces of information can be fine, such as a specific test result for a particular batch, which challenges retrieval accuracy.

Configuration Guidelines

Configuration Item	Recommended Value	Rationale
`Chunk size` (Chunk Length)	`800–1200 characters` (characters)	Balances context integrity with vector model processing capability, preventing truncation of critical information blocks.
`Chunk overlap` (Chunk Overlap)	`100–200 characters` (characters)	Ensures contextual continuity between paragraphs, reducing information loss due to chunking boundaries.
`Recall count` (Recall Count)	`10–15 entries` (items)	Increases relevant information coverage to meet multi-faceted and cross-referencing query demands.
`Similarity threshold` (Similarity Threshold)	Calibrate based on actual measurements	Requires testing with specific vector models and datasets to ensure high relevance in recall.
`PARSE_FILE_TIMEOUT_SECONDS`	`600 seconds` (seconds)	Accommodates parsing time for large PDFs and complex Word documents, preventing timeout errors.
`UPLOAD_FILE_MAX_SIZE`	`500 MB`	Addresses the need for uploading large files like clinical trial reports, ensuring file integrity.

Common Pitfalls

File upload failure when the file size exceeds the UPLOAD_FILE_MAX_SIZE limit. This occurs because the default file upload limit was not adjusted, or the file contains numerous embedded images and charts, making its actual size much larger than anticipated.
Retrieval results contain many irrelevant or low-relevance paragraphs, leading to user feedback like "clicking the knowledge base always shows this." This might be due to a Similarity threshold (Similarity Threshold) set too low, or overly large text chunking granularity, which introduces too much noise during vectorization.
Retrieval results do not reflect the latest information after partial data updates. This happens when the knowledge base index fails to trigger a complete rebuild or incremental update, causing old data to still be recalled.

Verification of Configuration

For typical queries, manually check if all key information in the recall results is covered and assess the completeness of information segments.
By comparing new and old versions of documents, verify that after a knowledge base update, specific queries accurately recall the latest version of the data, not older versions.
Select complex document snippets containing tables and image descriptions for testing. Confirm that under the configured Chunk size (Chunk Length) and Chunk overlap (Chunk Overlap), table content or image descriptions are effectively preserved with good contextual relevance.
Use a logging system to monitor timeout situations for configuration items like PARSE_FILE_TIMEOUT_SECONDS during actual file processing, ensuring all pending files are successfully parsed and indexed.

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.