What the Data for This Category Looks Like
Data sources for other comprehensive yield rates include public market data APIs, compliant registered institutional data interfaces, and structured files exported from internal trading systems. Data is synchronized to full datasets from the previous trading day at fixed daily times, with no real-time push mechanism. Document structures are multi-sheet structured files or hierarchical JSON structures, where each sheet or layer corresponds to a product category. Core fields include product unique identifier, full product name, statistical cycle, benchmark yield, actual yield, data source channel, and update timestamp. The unit of statistical cycle is natural day, natural week, or natural month, with no unified fixed unit.
What Constraints Do These Characteristics Impose on Workflow Orchestration
Multi-source heterogeneous data sources require workflows to use parallel pull nodes to connect to data interfaces from different channels, preventing overall task interruptions caused by single-source failures. Fixed daily update schedules require workflows to use scheduled triggers set to run after daily market close, ensuring the latest data is pulled. Multi-sheet or hierarchical structured documents require workflows to use sheet-by-sheet or layer-by-layer traversal nodes to process product data for different categories separately. Data sources with inconsistent field naming require workflows to include built-in field mapping rules to unify identical business fields from different sources into standard names, avoiding errors in subsequent verification and aggregation links. Additionally, the timeliness requirements of daily report broadcasting require workflows to set reasonable node timeout thresholds, preventing task timeout interruptions caused by large data volumes.
How to Configure Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Scheduled Trigger Time | 17:30–18:00 daily | Adapts to post-market data update times of most financial institutions, ensuring full latest data is pulled |
Parallel Data Source Node Count | 2–3 | Matches parallel pull requirements for three common data source types: public market data, institutional reports, and internal ledgers, preventing single-node timeouts |
Knowledge Base Search Node Variable Reference | Reference global variables | Addresses configuration needs for dynamically specifying knowledge bases. Knowledge base IDs must be registered in global variables in advance to avoid blank variable references |
AI Chat Node Output Mode | Silent Execution | Prevents execution results from the AI chat node from being appended to the final chat response content, meeting the requirement of only obtaining model results |
Global Variable Persistence | Enabled | Ensures context and variables from multi-turn sessions are retained across sessions, resolving issues with lost global variables |
Node Timeout Duration | 600 seconds | Adapts to the time required for multi-source data pulling and field processing, preventing task interruptions caused by large data volumes |
The parameter values provided on this page are conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: No optional variables appear in the variable reference dropdown of the knowledge base search node. Cause: The knowledge base ID variable was not registered in the workflow's global variable management in advance, or the variable was not bound to the current workflow's context.
- Phenomenon: Execution results from the AI chat node are appended to the final chat response content. Cause: The AI chat node was not set to silent execution mode. The node enables chat context appending logic by default.
- Phenomenon: An error occurs when the workflow runs to the data verification node, with logs showing that core fields are empty. Cause: No data source field mapping rules were configured, and field naming from different sources was not unified, causing the verification node to fail to identify required fields.
How to Confirm Proper Configuration
- Trigger a test execution, review the workflow's node execution logs, and confirm that all data source nodes successfully pulled data.
- Check the global variable list, confirm that the registered knowledge base ID variable is bound to the current workflow, and that the variable reference in the knowledge base search node is correctly selected.
- Review the AI chat node configuration, confirm that silent execution mode is enabled to avoid output interfering with final results.
- Trigger manual execution of the scheduled task, wait for the task to complete, and check the generated broadcast content to confirm that all core fields are correctly populated.
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.