What data for this category looks like
Sources for industrial metal data include spot quotes on the Shanghai Futures Exchange and London Metal Exchange, monthly inventory and production data from domestic non-ferrous metal industry associations, and regular financial reports of listed industrial metal enterprises. Update frequencies follow this pattern: spot quotes are updated daily, industry inventory and production data is updated monthly, and listed company financial reports are updated quarterly and annually. Most documents are structured reports, with fields such as trading variety, delivery grade, spot transaction price, total warehouse inventory, and monthly output. Units are tons and USD per ton. Some data exists as PDF-format industry monthly reports, and JSON files returned via API interfaces.
What constraints do these characteristics impose on workflow orchestration
Industrial metal data has multiple sources and varying update frequencies. Workflows must be configured with staged scheduled trigger nodes, to separate daily spot data pulls, monthly industry data pulls, and quarterly financial report data pulls. Field units are inconsistent. Configure field standardization nodes to unify data units. Data formats include both structured reports and unstructured PDF documents. Configure document parsing nodes to adapt to different formats. Data volume is large. Configure context truncation nodes to limit the number of characters processed per run, to avoid workflow timeouts.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
scheduleCron | Set the spot data node to 0 0 * * *, set the financial report data node to 0 0 1 * * | Matches daily spot data updates and quarterly financial report data updates |
fieldStandardize | Unify all numeric fields to tons, USD per ton | Industrial metal data has inconsistent units across sources, requiring standardization |
maxContext | 8000–12000 characters | Industrial metal financial report data has a large volume, requiring context limits to avoid overflow |
retrieveCount | Top 10 entries | Industrial metal industry data has a large volume, requiring sufficient recall volume |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Processing large financial report PDF documents requires a longer timeout period |
outputHide | Configure to hide the output of the node with ID node_chat_2 | Meets requirements where some node outputs do not need to be displayed |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, 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 Mistakes
- Phenomenon: All outputs from multiple AI chat nodes in the workflow are displayed in the workflow conversation, and specified node outputs cannot be hidden. Cause: The
outputHideparameter is not configured, and the node ID to be hidden is not specified. - Phenomenon: When testing a workflow based on question classification, only the first question classification node triggers knowledge base recall, and subsequent nodes do not reference the knowledge base. Cause: Context variable transfer parameters are not configured, and knowledge base recall results from preceding nodes are not passed to subsequent nodes.
- Phenomenon: A
408 Request Timeouterror occurs during workflow runtime. Cause: ThePARSE_FILE_TIMEOUT_SECONDSvalue is not adjusted, and the timeout setting is too short to adapt to the parsing time of large industrial metal financial report documents.
How to Confirm Proper Configuration
- Verify the
scheduleCronconfiguration, confirm that the trigger cycle of the corresponding data node matches the actual data update frequency. - Check the
fieldStandardizeconfiguration, confirm that all numeric fields have been unified to the target units. - Test multiple AI chat nodes, confirm that the output of the specified node is not displayed in the conversation.
- Run the workflow, confirm that all node return results have no unit confusion or missing fields.
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.