What the data for this category looks like
Commodity trading data is sourced from spot trading platforms, futures exchange APIs, enterprise warehouse management systems, and transaction contract documents. Spot quotes update hourly. Futures market data is pushed in real time. Warehouse inventory data updates daily. Documents include fields such as product code, specification model, transaction unit price, delivery area, settlement cycle, and more. Units include yuan/ton, cubic meter, ten thousand tons, and others. Some contract documents use long text format, containing complete transaction terms and attachment information.
What constraints these characteristics impose on the "workflow orchestration" link
Multi-source heterogeneous data sources require workflow configuration to align data nodes, and complete field mapping and format conversion across different platforms. Frequently updated market data requires workflow node call frequencies to match data update rhythms, to avoid using expired information. Long-text transaction contracts require split-node processing to prevent context overflow. Parameter units from different sources need conversion logic configured in the workflow, to ensure calculation consistency across data sources.
How to set the configuration
Reference only; verify against the target FastGPT release and deployment documentation.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
variables | Bind product code, delivery area, and query cycle as custom variables, select 2-3 core business fields | Inquiry scenarios for commodity trading heavily rely on specific product and regional parameters. Using variables enables workflow reuse and adapts to different business needs |
| Number of parallel nodes | 2-3 parallel AI dialogue nodes | Queries for commodity market and contract data need to cover multi-dimensional information simultaneously. Parallel nodes can shorten overall response time |
| Dialogue log association | Bind user ID and session identifier, associate with nodeId | Inquiries related to commodity trading involve transaction-sensitive information. Log association is required to confirm the request subject and achieve traceability |
| Query merging module | Merge multi-source query results for the same product, group by delivery area | Commodity data comes from multiple platforms. The merging module can eliminate duplicate entries and organize results according to business logic |
| Timeout setting | 600 seconds | Queries for commodity data across multiple sources require a long waiting period. This avoids interrupting the workflow process due to timeout |
Three common mistakes
- Phenomenon: The workflow returns empty results with missing fields after running. Cause: Core parameters such as product code and delivery area are not bound via
variables, causing the query logic to fail to match specific business scenarios. - Phenomenon: The workflow execution times out and returns a timeout status. Cause: A reasonable number of parallel nodes is not configured. Multi-dimensional commodity data queries are executed serially, exceeding the preset timeout threshold.
- Phenomenon: Dialogue logs cannot be associated with specific users, making it impossible to trace the inquiry subject. Cause: User identifiers and
nodeIdare not bound in the configuration, causing logs to fail to correspond to the request subject.
How to confirm the configuration is complete
- Trigger a test query, check whether the parameters bound to
variablesin the workflow are correctly brought into the query logic. - View the workflow running logs, confirm that multiple AI dialogue nodes start execution simultaneously, and no serial blocking occurs.
- Export the dialogue logs, confirm that each log is associated with a unique session identifier and user subject.
- Trigger a multi-source data query, check whether the query merging module organizes the returned results according to preset rules.
The values provided here are common starting points and should be measured against the reader's own samples.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-09-28.