Data Characteristics
Attenuated inactivated vaccine regulations and SOP documents originate from national and provincial drug administration agencies. These include relevant laws, technical guidelines, registration application materials, and internal quality management system documents from manufacturers. Documents are typically in PDF, Word, or scanned image formats. Updates are infrequent, occurring primarily when new regulations are issued or technical guidelines are revised. Document structures are complex, containing extensive specialized terminology, charts, flowcharts, and tables. Fields cover vaccine production processes, quality control, batch release, storage and transportation, and adverse event monitoring. Units include specific biomedical measurements such as dosage (TCID50, PFU), concentration (μg/ml), temperature (℃), and time (hours, days).
Constraints on Tool Calling and Plugins
The complexity of attenuated inactivated vaccine regulatory documents imposes specific requirements on tool calling and plugins. First, charts and flowcharts are difficult for standard text parsers to extract effectively, leading to information loss and impacting answer accuracy. Second, specialized terminology and biomedical units require RAG retrieval and generation to correctly understand and convey context, preventing misinterpretation or confusion. Third, the rigor of regulations and SOPs demands that the model cite original text or provide exact sources in its answers, requiring high traceability from plugins. Finally, low document update frequency means more manual review is needed during initial knowledge base construction, but subsequent maintenance costs are relatively low.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 Token | Accommodates long paragraphs and multi-step descriptions in complex SOPs, reducing semantic loss from truncation. |
Chunk size (Segment Length) | 500 characters (characters) | Adapts to longer sentence structures in regulatory texts, ensuring semantic integrity and preventing critical information loss at split points. |
Recall count (Recall Count) | Top 5 entries (top 5) | Given the rigor of regulatory documents, increasing the recall count provides more comprehensive context for the model. |
Similarity threshold (Similarity Threshold) | 0.75–0.85 | Balances precise matching of specialized terms with recall of relevant background information, avoiding retrieval of irrelevant content due to overly loose criteria. |
Rerank result count (Rerank Return Count) | 3 entries (3 items) | Selects the most relevant snippets from recall results, improving the focus and accuracy of the final answer. |
PARSE_FILE_TIMEOUT_SECONDS | 300 seconds (seconds) | Handles longer parsing times for large PDF or Word documents, preventing parsing failures due to timeouts. |
Common Pitfalls
- AI output lacks accurate citation of specialized terminology, instead using vague phrasing. This typically occurs when RAG-recalled text snippets do not sufficiently contain the required specialized terms or their context, preventing the model from precise restatement.
- Uploaded regulatory documents fail to parse or have incomplete content, especially when processing scanned images or PDFs with complex charts. This may be due to limited recognition capabilities of file parsing plugins for non-textual content, failing to extract information effectively.
- Units or numerical values are incorrect after tool invocation, such as vaccine dosage unit confusion or numerical calculation discrepancies. This stems from the model's insufficient understanding of specific units and values in the biomedical field, or the plugin's failure to accurately process this information.
Validation Steps
- Upload an SOP document containing various biomedical units (e.g.,
TCID50,μg/ml). Ask questions about relevant values and check if the AI's answer accurately cites the original values and units. - Select a registration application PDF with complex flowcharts. Ask about process details and confirm if the AI can provide coherent answers by combining text descriptions and potential chart information.
- For questions regarding specific production process steps in regulatory documents, check if the AI's answer includes corresponding clause numbers or section references to verify the plugin's traceability.
- Test uploading regulatory files of different sizes and formats (PDF, Word). Observe if file parsing is consistently successful, paying particular attention to parsing time and result completeness for large files.
Note: The values provided are common starting points. Measure performance 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.