Data Characteristics for This Category
mRNA vaccine product data primarily originates from clinical trial reports, drug regulatory approval documents, academic journal papers, and pharmaceutical company public instructions. This data updates frequently, especially clinical trial progress and pharmacovigilance information. Document structures are complex, often containing extensive unstructured text such as adverse event descriptions, immunogenicity data, and manufacturing process details. Structured data appears in fields like dosage, administration route, batch information, and expiration dates. Units include dosage (ug), concentration (mg/mL), temperature (℃), and specific immunological indicators (e.g., neutralizing antibody titers). The multimodal nature of the data, including charts, tables, and lengthy text descriptions, presents challenges for information extraction and tool calling.
Constraints Imposed by These Characteristics on Tool Calling and Plugins
The highly unstructured nature of mRNA vaccine data requires tool calling to have robust text parsing and information extraction capabilities to accurately identify key fields from vast documents. High-frequency data updates, particularly for safety information, necessitate tools that support periodic or event-driven data synchronization mechanisms to ensure knowledge base timeliness. Multimodal data structures mean that relying solely on a single text processing tool is insufficient; a combination of image recognition or table parsing plugins may be necessary. Furthermore, the strictness of critical numerical fields like dosage and concentration demands that tools clearly differentiate and handle units during parameter passing and result validation to prevent discrepancies caused by unit confusion. Processing sensitive information such as adverse events requires tool calling to have high reliability in data cleansing and anonymization.
Configuration Strategy
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 3000 | Ensures accommodation of critical information segments from clinical trial reports, preventing truncation of important context. |
Chunk size | 500 characters | Adapts to the characteristic of academic papers and regulatory documents having many long sentences, ensuring semantic completeness. |
Recall count | Top 10 entries | Covers a broader range of potentially relevant information, increasing the hit rate for complex queries. |
Similarity threshold | Calibrate by actual measurement | Adjusts based on specific dataset characteristics, balancing recall and precision, avoiding interference from irrelevant information. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds | Addresses situations where parsing large PDF documents or complex charts may take a long time. |
max_tokens | 1024 | Allows generation of detailed consultation responses, including multi-faceted information, meeting professional requirements. |
Common Pitfalls
- API call returns links that do not navigate correctly but instead overwrite the current page. This occurs because the frontend does not correctly handle the opening method for external links returned by the API, lacking attributes like
target="_blank". - Discrepancies exist between the "view details" in the conversation logs and the actual response content. This usually happens when the logging mechanism fails to fully synchronize the final complete content of streaming output, recording intermediate states or partial information.
- Streaming output returns too slowly, failing to meet real-time interaction requirements. This can be due to complex backend API processing, high network latency, or a lack of optimization for data chunking and transmission.
Validation Steps
- Test key parameter queries (e.g., dosage, side effects) for various mRNA vaccine products to verify that the tool accurately extracts and presents relevant information.
- Simulate user questions to check the tool's response speed and the smoothness of streaming output, ensuring the user experience meets expectations.
- Randomly sample and review dozens of conversation logs, comparing log details with actual responses to confirm information recording completeness and consistency.
- Use PDF documents containing complex tables and charts for information extraction tests to validate the tool's ability to process multimodal data.
The values provided 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.