What This Type of Data Looks Like
Iron ore research report data originates from domestic futures exchanges, steel industry associations, bulk commodity spot trading platforms, and securities firm research institutions. Update cadences vary: spot market data updates daily after market close, futures contract data updates when trading sessions end, and monthly industry reports and securities firm research reports update in real time upon publication. Most documents combine text analysis with dense tables, and include core fields such as the Platts Iron Ore Price Index (unit: US dollars per dry metric ton), steel mill inventory, port arrival volume, and others. Individual document word counts range from several thousand to tens of thousands.
Constraints on Workflow Orchestration from These Characteristics
Differences in update cadences across data sources require workflows to support independent trigger timing for each data source. Dense tabular documents require parsing nodes to follow specific table splitting rules, to prevent loss of core data fields. Multi-dimensional quantitative fields and inconsistent units require workflows to include a unit normalization step. Irregular research report release cycles require workflows to support dynamic trigger scheduling, to match the update cadences of different data sources.
Configuration Recommendations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
workflow_trigger_mode | Trigger by data source type during specific time windows | Adapts to the varied update cadences of multiple iron ore research report data sources: spot data triggers after 16:00 daily, futures data triggers after 20:00 post-market, and securities firm research reports trigger in real time |
parse_table_max_rows | 50 rows | Adapts to dense supply and demand, inventory tables in iron ore research reports to avoid abnormal table splitting during parsing |
rag_recall_top_k | Top 8 results | Covers multi-dimensional research report content including iron ore market trends, supply and demand, policies, and balances recall accuracy and response speed |
rag_similarity_threshold | 0.72–0.78 | Adapts to the recall accuracy requirements for sensitive data such as iron ore prices, to avoid mixing in low-relevance content |
workflow_timeout | 900 seconds | Addresses the time required for long document parsing and multi-data source aggregation, to avoid runtime timeouts |
global_var_scope | Team-level | Adapts to scenarios where multiple projects share iron ore research report data source configurations, and complies with global variable management rules for version 4.9.1 and above |
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, and it is recommended to test against one's own samples before finalizing settings.
Three Common Configuration Errors
- Symptom: The frontend page becomes unresponsive during concurrent workflow invocations, and the interface returns a 504 timeout status code. Cause: The
workflow_concurrent_limitparameter is not configured, and concurrent requests exceed the system's resource carrying capacity. - Symptom: The global variable configuration entry cannot be found in the workflow. Cause: Version 4.9.1 is being used, and global variables must be configured on the team management page, not the application editing page.
- Symptom: Only fixed tokens can be used when calling external interfaces in workflows, and dynamic values cannot be passed. Cause: The
dynamic_request_paramsparameter is not bound to the external interface node, and dynamic parameters from conversation context are not mapped to the interface request body.
How to Verify Successful Configuration
- Manually trigger the workflow, check if the parsed research report content includes complete core fields such as prices and inventory, and verify that field units match the data source.
- Initiate multiple concurrent requests, monitor system resource usage, and adjust
workflow_concurrent_limitto a value suitable for the current deployment environment. - Configure dynamic parameter tests, initiate invocation requests with different tokens, and confirm that the external interface can correctly receive and use the passed parameters.
- View workflow runtime logs to confirm that all nodes execute normally, with no timeout or parsing failure error messages.
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.