Data Characteristics for This Category
Attenuated inactivated vaccine registration documents involve diverse data. Core data sources include preclinical study reports (toxicology, pharmacodynamics), clinical trial data (Phase I, II, III safety, immunogenicity, protective efficacy), manufacturing process validation files, quality standards and testing reports, and stability study reports. This data exists as both structured and unstructured documents. For example, clinical trial reports are often hundreds of pages long in PDF format, containing numerous charts and statistical data. Manufacturing process documents may include flowcharts, equipment parameter tables, and batch production records. Data update frequency is driven by R&D progress and regulatory requirements. Clinical trial data, for instance, is continuously supplemented as study phases advance, and quality standards may be revised due to process optimization or regulatory changes. Fields and units are highly specialized. For example, "Lethal Dose 50 (LD50)" is expressed in "IU/kg" or "PFU/kg," "antibody titer" in "ELISA units/ml" or "neutralizing antibody multiples." "Fermentation tank temperature" during production is in "℃," and "culture medium pH" is precise to one decimal place.
Constraints Imposed by These Characteristics on Multiturn Conversation and Prompts
The data characteristics of attenuated inactivated vaccine submission documents impose specific requirements on multiturn conversation and prompt design. Long document lengths, numerous specialized terms, and diverse data formats mean that a Retrieval-Augmented Generation (RAG) system needs to efficiently process long text chunks and perform entity recognition. This ensures conversations accurately capture context. For example, when querying clinical trial results, the system must differentiate data from various trial phases and identify key safety indicators or immunogenicity data. The specialized nature of the data and the strictness of units require prompts to accurately cite specialized terms and numerical values from the original text when generating responses, avoiding vague statements or unit confusion. The presence of multimodal information (such as charts in PDFs) requires the model to have some chart understanding capability to answer queries based on chart content. Additionally, the non-real-time nature of data updates dictates that knowledge base construction needs to focus on version management and incremental update mechanisms to prevent the AI from citing outdated information. This is particularly important in multiturn conversations, as a conversation might trace back to historical data versions.
Configuration Settings
| Configuration Item | Suggested Value | Rationale |
|---|---|---|
Chunk size | 800–1200 characters | Accommodates longer professional descriptions and paragraphs in clinical reports and manufacturing process documents, ensuring semantic completeness. |
Recall count | Top 5–8 entries | Ensures sufficient contextual information is covered in multiturn conversations, avoiding the omission of critical data points. |
Similarity threshold | 0.75–0.85 | Improves retrieval accuracy, filtering out results with low relevance to biomedical professional queries. |
maxContext | 8192 token | Provides ample context space for conversation history and retrieval results, supporting complex multiturn follow-up questions. |
HTTP_REQUEST_TIMEOUT | 60 seconds | Allows sufficient processing time for external APIs (e.g., structured data parsing services), preventing timeouts. |
STREAM_OUTPUT_MIN_DELAY | 50 ms | Ensures smooth streaming output, enhancing user experience while preventing information overload from excessively fast output. |
Three Common Mistakes
- AI responses contain non-standardized specialized terms or unit errors. This occurs because prompts do not explicitly require the AI to strictly adhere to original terminology, or knowledge base chunking fails to preserve the complete context of terms.
- The AI cannot accurately answer queries involving chart content in multiturn conversations. Responses are based only on text content and do not mention chart data. This happens because the RAG system lacks effective multimodal content parsing capabilities, or prompts do not guide the AI to focus on chart information.
- External HTTP requests return 400 or 404 errors, leading to conversation interruptions or incomplete responses. This is due to API call parameter formats in the workflow not matching target service requirements, or incorrect API endpoint configuration.
How to Confirm Proper Configuration
- Select an attenuated inactivated vaccine registration document containing key data and specialized terms. Conduct multiturn conversation tests to check the accuracy and consistency of specialized terms and numerical values in AI responses.
- Ask questions about chart content within the document. Observe whether the AI can correctly cite data or conclusions from the charts to evaluate multimodal processing capability.
- Simulate various complex query scenarios, including those involving time series, batch differences, and toxicological dose-response. Verify whether the AI can maintain context and provide coherent, accurate responses in multiturn conversations.
- Check external API call logs in the workflow to confirm that HTTP request input parameters and output results meet expectations, with no abnormal status codes.
The values provided are common starting points and should be measured against your 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.