Data Characteristics
Attenuated live vaccine quality documents primarily include production batch records, inspection reports, stability study data, and deviation and change control files. Data sources typically come from production workshops, the LIMS systems of quality inspection departments, and document management systems. The update frequency correlates with batch production and inspection cycles, usually monthly or quarterly. Some critical parameters may be recorded in real-time. Document structures often follow fixed templates, containing numerous tables, chromatograms, and images, such as high-performance liquid chromatography (HPLC) graphs, gel electrophoresis images, and virus titer assay graphs. Fields include batch number, production date, expiration date, virus strain, media components, purification process parameters, test items, test results, and units (e.g., TCID50/mL, PFU/mL, µg/mL, OD value).
Constraints Imposed by These Characteristics on Workflow Orchestration
The data characteristics of attenuated live vaccine quality documents impose specific requirements on workflow orchestration. Documents containing images and chromatograms require the workflow to support image recognition and extraction, converting visual information into processable text or numerical data. The periodic nature of document updates necessitates workflows that can trigger on schedule to automatically retrieve new batch data. The fixed template structure allows for designing precise field extraction rules, using regular expressions or position-based matching. The standardization of fields and units demands strict unit validation and standardization during data preprocessing to avoid data ambiguity. Furthermore, the complexity and cross-referencing in production batch records require the workflow to handle multi-document associations, ensuring data consistency.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
UPLOAD_FILE_MAX_SIZE | 500 MB | Quality documents containing numerous chromatograms and scanned images can be large. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Complex document parsing takes longer, preventing timeouts. |
Image_Recognition_API_Key | Key obtained from visual model provider | Enables image recognition to process data within chromatograms and images. |
Extraction_Pattern_BatchNo | BatchNo:\s*([A-Za-z0-9-]+) | Batch number format is relatively fixed, allowing for precise extraction using regular expressions. |
Data_Update_Frequency | Every months 1 Number 02:00 | Synchronizes with the monthly update cycle of batch production and inspection reports. |
Knowledge_Base_Scope | Current Workflow Only | Ensures the knowledge base content is strongly associated with specific vaccine product quality documents. |
Common Pitfalls
- Calling the workflow returns a 400 error, indicating
Invalid image data format. This occurs when image data is not correctly encoded as a Base64 string, or the Base64 string contains invalid characters. - Some critical numerical fields in the document parsing results are empty. This happens when the field extraction rules configured in the workflow do not cover all possible format variations in the document, such as inconsistent unit representations.
- Knowledge base retrieval results do not match expectations, failing to link to the latest batch data. This occurs when the knowledge base is set as a global variable in the workflow but the latest batch number or product model is not passed as a filter condition during each execution.
Verification Steps
- Upload a typical attenuated live vaccine quality document containing images and chromatograms. Check the workflow execution logs to confirm that the image recognition module was successfully called without errors.
- Manually compare the parsed data. Extract at least 10 key fields and verify that their values, including units and numerical precision, are identical to the data in the original document.
- Simulate uploading documents with different batch numbers and update dates. Check if the workflow triggers as expected and correctly processes new and old data, ensuring the data update mechanism functions properly.
Note: The values provided are common starting points. Measure them against your own samples to determine optimal configurations.
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.