Data Characteristics for This Category
Attenuated inactivated vaccine regulations and SOP documents originate primarily from the National Medical Products Administration, World Health Organization (WHO) guidelines, internal enterprise quality management system documents, and production batch records. These documents typically have a low update frequency, usually revised annually or when significant regulatory policy changes occur. Document structures are mainly PDF or Word formats, containing numerous tables, diagrams, and flowcharts. Common fields include batch number, production date, expiration date, storage conditions, inspection results, operation step number, and risk level. Units involve temperature (℃), time (hours/days), concentration (IU/mL), and volume (L/mL), with extremely high precision requirements, often accompanied by specific error ranges or deviation limits.
Constraints Imposed by These Characteristics on "Workflow Orchestration"
The low update frequency of attenuated inactivated vaccine regulatory documents means a large initial data import during knowledge base construction, with less pressure for subsequent incremental updates. However, version management requires attention. Complex tables and flowcharts in documents demand high recognition capabilities from parsing nodes, ensuring accurate OCR for images and structured extraction of tables to prevent critical information loss. Highly precise fields and units necessitate the model's strict numerical comparison and unit conversion capabilities during Q&A to avoid safety risks from fuzzy matching. Furthermore, the numerous operation step numbers and risk level fields require retrieval strategies within the workflow to precisely match specific numbers or levels, supporting engineers' queries on specific operational details and risk assessments.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale for Recommendation |
|---|---|---|
Chunk size (Segment Length) | 500-800 characters | Vaccine regulatory documents often have long paragraphs containing multiple operational instructions or detailed descriptions, ensuring contextual completeness. |
Recall count (Number of Retrieved Items) | Top 8 | Ensures coverage of sufficient relevant regulatory clauses in complex Q&A, improving accuracy. |
Similarity threshold (Similarity Threshold) | 0.78-0.85 | Regulatory Q&A demands high precision; a threshold that is too low may introduce irrelevant information, while one that is too high may lead to omissions. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Large file parsing is time-consuming; sufficient time prevents parsing interruptions. |
Image OCR Accuracy | High | Ensures accurate recognition of text information in flowcharts and diagrams, avoiding omission of critical steps. |
maxContext | 8192 tokens | Processes lengthy regulatory texts, ensuring the model receives complete contextual information for reasoning. |
Three Common Mistakes
- The model does not respond to uploaded attachments or cannot summarize them: This may be due to improper configuration of the document parsing node, failing to correctly identify and extract text content from PDFs or Word documents, especially scanned images or tables in image format.
- The model's answer contains numerical deviations or unit errors: This occurs when numerical fields are not standardized during knowledge base construction, or the model fails to strictly follow unit conversion rules during inference.
- Database connection tool variables in the workflow cause errors: This typically happens when the variable type or format passed to the database query does not match expectations, for example, passing a string to a field requiring an integer, or triggering SQL injection risk filtering mechanisms.
How to Confirm Proper Configuration
- Upload regulatory documents in various formats (PDF, Word, scanned images). Check document parsing logs to confirm all text, tables, and text within images are successfully extracted.
- Conduct multiple rounds of Q&A tests for regulatory clauses containing specific numerical values and units. Verify that the numerical values and units returned by the model match the original text and that any deviations are within an acceptable range.
- Simulate scenarios where engineers query specific operation step numbers or risk levels. Check if the retrieval results precisely match the relevant clauses and verify that the workflow's logical branches execute as expected.
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.