What the data for this category looks like
Data sources for white goods industry research reports include securities firm industry research reports, monthly monitoring data from home appliance industry associations, public financial reports from leading brands, and online and offline retail channel monitoring reports. Update frequency fluctuates with business cycles. Research report update frequency is higher during earnings season and new product launch weeks, while regular monitoring data updates monthly or quarterly. Document structures typically include core data tables, market size analysis, competitive landscape, and supply chain upstream and downstream dynamics. Fields cover shipment volume, average price, online and offline penetration rate, GMV share, and more. Units are mostly ten thousand units and yuan per unit.
What constraints do these characteristics impose on workflow orchestration
The above data characteristics impose multiple constraints on workflow orchestration. Multi-source data requires configuring multiple data source access nodes to connect to securities firm platforms, industry association interfaces, and public financial report pages separately, to avoid information loss from a single data source. Data sources with different update frequencies need differentiated scheduled trigger rules configured, to adapt to the pull rhythm of high-frequency and low-frequency data. Structured tables in research reports require enabling dedicated parsing components, and extracted fields need unified units to avoid unit confusion during subsequent retrieval. For research report content with many detailed dimensions, recall parameter values need adjusted to balance retrieval coverage and accuracy.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
PARSE_TABLE_ENABLE | Enabled | White goods industry research reports contain a large number of structured tables for shipment volume, average price, and other metrics, which need to be extracted for accurate retrieval |
VAR_STANDARDIZE_RULE | Unify units to ten thousand units, yuan per unit | Research reports from different sources have unit differences, and standardization avoids unit confusion during retrieval |
CRON_EXPRESSION | 0 9 * * 1,3,5 | Securities firm research reports are mostly released shortly after weekday morning trading sessions, this configuration pulls the latest research reports at fixed times |
RECALL_TOP_K | Top 6 entries | White goods industry research reports cover detailed dimensions including market, supply chain, and competition, so a sufficient number of fragments need recalled to support complete responses |
SIMILARITY_THRESHOLD | 0.72–0.78 | Research report text has strong professionalism, this range balances retrieval precision and content coverage |
WORKFLOW_TIMEOUT | 900 seconds | Multi-data source splicing and table parsing require long processing times, this duration covers conventional business processes |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material form, data volume and business rules. Specific issues require case-by-case analysis, and it is recommended to test on your own samples before finalizing.
Three common mistakes
- Phenomenon: Global variables updated in the workflow cannot be read in subsequent AI chat components. Cause: The variables are not configured with a globally accessible scope and only take effect in the current node.
- Phenomenon: The AI chat component cannot obtain the output content of the code running component. Cause: The output of the code component is not correctly mapped to workflow context variables, or the output format does not follow the standard JSON structure.
- Phenomenon: The workflow import entry cannot be found. Cause: The user did not enter the dedicated workflow management page, and mistakenly used the knowledge base upload entry as the import entry.
How to confirm the configuration is complete
- Manually trigger the workflow once, check the execution logs of each node to confirm that the data source has been pulled normally, and there are no errors in the table parsing and variable standardization steps.
- Enter a white goods-related query in the workflow test panel, verify the matching degree between the retrieved research report fragments and the query topic, and adjust the
SIMILARITY_THRESHOLDto a range that meets business requirements. - Check the binding relationship of global variables, call the variables in the test node to confirm that their values are consistent with the output of the upstream component.
- Set a test scheduled task to verify whether the trigger rule executes as expected, and confirm that the
CRON_EXPRESSIONconfiguration conforms to the business rhythm.
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.