Workflow Orchestration for Medical Device Intelligent Due

What the data for this category looks like

Data for medical device intelligent due diligence reports comes primarily from the National Medical Products Administration official database, public qualification files of medical device manufacturers, clinical research filing platforms, and local centralized procurement bidding announcement platforms. Update frequency varies by data type:

Registration certificate information updates every 5 years upon renewal
Clinical research data updates irregularly with new trial results
Centralized procurement bidding data updates quarterly

Document formats include official announcements in PDF format and structured database export tables. Core fields include:

Registration certificate number (with the 国械注准 prefix)
Unified social credit code of the manufacturer
Applicable clinical departments
Model and specification
Validity period (unit: year)
Clinical sample size (unit: case)

What constraints these characteristics impose on workflow orchestration

The multi-source, scattered nature, format differences, and exclusive field rules of medical device due diligence data create multiple constraints for workflow orchestration. Cross-platform pull nodes must be configured to adapt to pull logic for different data sources, including official database APIs and public PDF documents. The fixed prefix format of registration certificate numbers requires a regular expression validation node to filter invalid entries. Validity period fields measured in years need a format conversion node to unify handling with date-type fields from other categories. A compliance check link must be added to confirm registration certificates are still within their validity period, preventing inclusion of expired qualification data. Quarterly updated centralized procurement data requires a scheduled trigger node to synchronize latest information quarterly, ensuring data timeliness.

How to set the configurations

Configuration Item	Suggested Value	Basis for This Setting
`workflow_node_timeout`	`600 seconds`	Medical device due diligence reports contain long-text clinical evaluation content. Sufficient duration must be reserved for single-node processing to avoid process interruption due to timeout
`rag_chunk_size`	`800–1200 characters`	Medical devices have dense professional terminology. Too long segments will split terminology associations, while too short segments will lose key information. This interval balances completeness and retrieval accuracy
`rag_top_k`	`Top 8 entries`	Due diligence reports need to cover multiple types of data including registration certificates, clinical data, and centralized procurement data. Too many recalls will increase the model's processing burden, while too few will miss core information
`regex_validation_rule`	`^China Medical Device Registration Permit [0-9]{4}-[0-9]{4}-[0-9]{6}$`	Matches the standard format of the National Medical Products Administration's official registration certificate number, filtering invalid qualification data
`file_parse_max_size`	`500 MB`	Medical device clinical evaluation reports are usually multi-page PDFs with large single-file sizes, requiring adaptation to large-file parsing requirements
`workflow_auto_save_interval`	`Every 300 seconds`	Medical device due diligence workflows have many nodes. Frequent automatic saves can avoid lost configurations due to abnormal interruptions. This interval balances performance and security

The parameter values provided on this page are all conventional recommendations used as starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require specific analysis. It is recommended to test on your own samples before finalizing settings.

Three common mistakes

Symptom: Workflow export function cannot trigger normally, and the exported file is empty. Cause: The workflow_export_enable parameter is not configured as true, or the data source bound to the export node has not completed the pre-check link.
Symptom: The question-and-answer results returned by the workflow include content wrapped in unrequested <think> tags. Cause: The disable_thinking_tag parameter is not enabled in the model configuration, or the used model does not fully support this switch setting.
Symptom: Retrieval results do not combine historical context information, only returning independent medical device data entries. Cause: The context injection switch is not enabled in the RAG configuration, or the output of the historical conversation node is not bound to the context input port of the retrieval node.

How to confirm the configuration is complete

Run a single full-process test, check that the output results cover core due diligence fields including medical device registration certificates, clinical evaluations, and centralized procurement bidding results, and that the format conforms to preset rules.
View workflow node execution logs, confirm that no timeout or error messages appear in all pull, parsing, and verification links, verifying the rationality of the node timeout configuration.
Adjust the thinking tag switch in the model configuration, trigger the question-and-answer process, confirm that the output results have no additional <think> wrapped content, verifying that the model configuration parameters take effect.
Import a single multi-page medical device clinical report PDF, test the parsing function, confirm that the file can be parsed normally, verifying that the file size configuration adapts to business requirements.

Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-14.