Data Characteristics
IVD diagnostic reagent registration document data sources are highly structured. They primarily come from product development reports, clinical trial reports, quality management system documents, production process documents, and instructions for use. Data update frequency is relatively low, occurring mainly during product iterations, regulatory updates, or supplementary applications. Document formats are diverse, including Word documents, PDF files, Excel spreadsheets, images, and scanned copies. Many contain technical drawings, experimental data curves, and original regulatory texts. Field types include batch numbers, expiration dates, detection limits, specificity, sensitivity, accuracy, and stability data. Units strictly follow international standards and NMPA regulations, such as IU/mL, ng/mL, %CV, and kPa. Precision and consistency requirements are extremely high.
Constraints Imposed by These Characteristics on Workflow Orchestration
The highly structured nature and strict unit specifications of IVD diagnostic reagent data require high-precision and robust validation capabilities in workflow data extraction and validation modules. Diverse document formats, especially those including images and scanned copies, make Optical Character Recognition (OCR) and image processing critical prerequisite steps. Workflows must seamlessly integrate these capabilities and process their structured or semi-structured text outputs. The low but impactful data update frequency means workflows need version management and traceability features when processing historical data, ensuring the accuracy and compliance of each submission. Furthermore, due to the sensitive nature of submission documents, workflows must incorporate strict access control and audit logs to ensure data security and traceable operations. The high demand for precision and consistency makes data comparison and conflict resolution mechanisms particularly important in workflows to prevent submission failures caused by data discrepancies.
Configuration Guidelines
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000 Token | Addresses context requirements for long documents like regulatory texts and clinical reports, ensuring semantic completeness. |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Accounts for OCR processing of scanned documents and parsing time for large PDF documents, preventing parsing interruptions. |
Chunk size (Segment Length) | 800–1200 characters | Balances semantic integrity and recall efficiency, adapting to text structures like technical specifications and instructions. |
Recall count (Recall Count) | Top 5–8 items | Ensures coverage of relevant regulatory clauses, experimental data, and other critical information, improving accuracy. |
Similarity threshold (Similarity Threshold) | 0.78–0.85 | Precisely matches subtle regulatory terminology and technical parameters, reducing false recall rates. |
Rerank result count (Rerank Return Count) | Top 3 items | Further filters recall results, focusing on the most relevant and core items. |
Common Mistakes
- Symptom: No results appear on the screen after a tool call, but the workflow seems complete. Reason: A clear output node or message sending node is missing after the tool call node, causing results to be processed internally without external presentation.
- Symptom: File parsing works correctly in the local environment, but fails with a 404 error after deployment to the server. Reason: File storage paths or access permissions on the server environment differ from the local environment, preventing the parser from finding the file.
- Symptom: The workflow fails to automatically select the corresponding knowledge base based on the submission document type. Reason: Global variables are not correctly assigned, or conditional logic has flaws, failing to effectively link document types with knowledge bases.
Verification Steps
- Upload different types of IVD diagnostic reagent-related documents (e.g., clinical reports, instructions for use) to verify if document parsing nodes accurately extract key fields and data.
- Construct a workflow that includes steps such as regulatory clause retrieval, experimental data comparison, and instruction writing. Run it and check if the output content meets expectations, especially the accuracy of numerical values and units.
- Simulate abnormal files (e.g., corrupted PDFs, low-quality scanned copies) and observe if the workflow's error handling mechanism correctly captures errors and provides clear failure prompts.
The values given 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.