Data Profile for This Category
Data sources for dairy product due diligence include raw milk test reports, production batch traceability archives, third-party regulatory sampling announcements, and supply chain supplier qualification documents. Data update rhythms fall into three categories:
- Production batch data updates alongside daily production plans
- Third-party sampling announcements are released irregularly alongside regulatory batches
- Supplier qualification documents are updated quarterly
Single due diligence data documents use batch number as their core identifier. They include fields such as milk source origin, fat content, protein content, total bacterial count, production time, and inspector ID number. Total bacterial count uses CFU/mL as its unit. Fat content uses g/100g. Shelf life uses days as its unit.
Constraints on Workflow Orchestration
Multiple scattered data sources require workflows to integrate multiple nodes to pull data from different channels. This prevents missing critical information across supply chain, production, and regulatory dimensions. Different update rhythms require separate incremental and full sync logic. For example, production batch data needs daily incremental pulls, while third-party sampling data needs trigger-based pulls. Fixed fields and units require workflows to configure standardized nodes. These unify field formats and units from different data sources. This avoids unit mismatches in later analysis. Batch-centric association logic requires workflows to use batch number nodes to complete multi-source data matching. This ensures production, test, and traceability data for the same batch can be integrated into a single due diligence report.
Configuration Settings
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Knowledge Base Search Node Authentication Configuration | Enabled and bound to specified department roles | Dairy product due diligence data involves supply chain sensitive information. Access must be restricted to authorized roles only |
Variable Reference Field Binding | Associated with output fields of batch number output node | Dairy product data uses batches as core association units. Batch numbers must be used to match full-link data |
Workflow Model Task Adaptation Configuration | Enabled long text parsing and multi-field verification mode | Support for parsing multi-dimensional test indicators and completing compliance verification is required |
Stream Reply Trigger Timing Configuration | Set to trigger after reply generation is complete | Dairy product due diligence reports require integration of multi-source data before unified output. Segmented returns will disrupt reading |
Knowledge Base Retrieval Similarity Threshold | 0.75–0.85 | Dairy product test indicators require strict matching. Low-similarity irrelevant test data must be filtered out |
Knowledge Base Retrieval Number | Top 8 entries | Multi-dimensional data including batch testing, traceability, and sampling must be covered. Critical information must not be missed |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require case-by-case analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: When selecting a knowledge base in the knowledge base search node, there are no optional values for variable references. Cause: Upstream node output fields that can be referenced are not configured, or the global visibility permission for variable references is not enabled.
- Phenomenon: The AI model options for the workflow problem classification node are empty. Cause: Available large language models are not bound in the workflow configuration center, or the model does not have classification task adaptation enabled.
- Phenomenon: Subsequent nodes do not execute as expected after stream reply is triggered. Cause: The trigger logic for
Stream Reply Trigger Timing Configurationis not set correctly. This causes subsequent data integration nodes to execute early.
How to Verify Proper Configuration
- Trigger workflow execution. Check the input parameter panel of the knowledge base search node. Confirm that variable reference fields can be selected normally and match corresponding batch data.
- Go to the workflow model configuration page. Confirm that AI model options have loaded and the corresponding task type can be selected normally.
- Use a non-authorized role account to trigger the workflow. Confirm that due diligence data in the target knowledge base cannot be accessed.
- Enable stream reply debug mode. Check the reply generation log. Confirm that subsequent nodes trigger execution after reply is complete.
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.