Data Characteristics for This Category
mRNA vaccine registration dossiers primarily include clinical trial reports, non-clinical study reports, manufacturing process and quality control documents, and stability study data. This data typically exists in various formats such as PDF, Word, and Excel. PDF documents are prevalent and often contain numerous charts, graphs, and scanned images. Data update frequency is high during the clinical trial phase and stabilizes once the submission phase begins, mainly involving supplementary materials. Document structures adhere to regulatory body guidelines, such as ICH M4E, with strict hierarchies and section divisions. Fields involve dosage units like μg, time units such as days, weeks, months, years, and complex biological indicators like antibody titer and T-cell response.
Constraints on "Multiturn Conversations and Prompts" Imposed by These Characteristics
Charts, graphs, and scanned images within PDF documents challenge text extraction, requiring enhanced OCR capabilities to ensure data completeness. Strict document structures demand that recall understands contextual hierarchies to avoid erroneous cross-section associations. High data update frequency, especially in early clinical trials, requires the knowledge base to support rapid updates and version management, ensuring multiturn conversations are based on the latest information. Complex biological indicators and specialized terminology place higher demands on the large model's understanding and generation capabilities, necessitating precise prompt guidance to focus the model on factual answers. In multiturn conversations, users may refer to earlier data versions, requiring the system to locate and cite historical information.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Chunk Length | 800–1200 characters | Accommodates the contextual integrity of complex medical texts, preventing semantic fragmentation. |
Recall Count | Top 5 | Ensures coverage of multiple information sources, balancing relevance and computational overhead. |
Similarity Threshold | 0.78–0.85 | Filters out low-relevance content, improving retrieval accuracy. |
Rerank Return Count | 3 | Prioritizes the most relevant and high-quality passages. |
maxContext | 4000 tokens | Balances long text input with model processing efficiency. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Addresses processing time for large PDF documents or scanned images containing complex charts. |
Common Pitfalls
- Multiturn conversations terminate midway, returning incomplete answers. This occurs due to large model API connection timeouts or internal workflow configurations not covering all branches.
- AI response word limits fail, producing output exceeding expectations. This typically happens when the word limit instruction in the prompt has insufficient weight and is diluted by other instructions.
- After a user selects a plugin, the prompt repeatedly triggers in subsequent conversations. This occurs when the plugin trigger logic is not associated with the single session state, lacking effective context management.
How to Verify Correct Configuration
- Upload multiple mRNA vaccine dossier PDFs containing scanned images. Check the consistency between the OCR-extracted text in the knowledge base and the original documents.
- Ask multiturn questions about specific biological indicators or experimental data. Verify that the AI's answers accurately cite specific values and units from the documents.
- Simulate users asking questions about the same document at different times. Confirm the system can cite the latest data version and explain data update circumstances.
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.