What data for this category looks like
Data sources for baijiu intelligent due diligence include public annual/quarterly financial reports from distilleries, production and sales data released by industry associations, liquor quality inspection reports from third-party testing institutions, sales and review data from e-commerce platforms, and batch and circulation records for aged liquor traceability. Update rhythms vary significantly: e-commerce data updates in real time, industry association data is released monthly, and distillery financial reports are updated quarterly or annually.
Document structures include structured tables (such as production capacity and revenue details), long-text analysis (such as brand strategy explanations), and single-line structured fields (such as alcohol content and production batch). Common fields include alcohol content (unit: %vol), total acid (unit: g/L), total ester (unit: g/L), production batch, production date, and distribution area.
What constraints do these characteristics impose on workflow orchestration
Format differences across multi-source data create parsing constraints: financial reports are long PDF documents, quality inspection reports are structured tables, and traceability data is single-line CSV records. Parsing nodes adapted to different formats must be configured.
Differences in update rhythms across data sources require layered scheduling rule configuration. High-frequency synchronization must be set for e-commerce data, and low-frequency synchronization must be set for financial reports.
Inconsistent field units and naming requires adding standardization mapping steps in the workflow to avoid unit confusion in subsequent analysis.
The mixed structure of long documents and short fields requires adjusting chunking parameters to balance context coherence and data recall accuracy.
How to set the configurations
| Configuration Item | Recommended Value | Basis for This Value |
|---|---|---|
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Baijiu-related financial reports and quality inspection reports are mostly long documents, requiring sufficient time for text parsing and structured conversion |
chunkSize | 1000–1200 characters | Baijiu data includes short fields and long-text financial reports. This range balances context coherence and recall accuracy |
SYNC_DATA_INTERVAL | 3600 seconds (e-commerce data), 2592000 seconds (annual financial reports) | Update rhythms vary significantly across different data sources, requiring matching synchronization frequencies |
fieldMappingRule | Unified conversion to {alcohol content: %vol, total acid: g/L, production batch: string} | Units of industry data fields for baijiu vary, requiring standardization for due diligence analysis |
CODE_RUN_TIMEOUT_SECONDS | 30 seconds | The code logic for baijiu due diligence is mostly data cleaning and field mapping, which does not require long running time |
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 specific analysis, and it is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After the code running node is executed, there is no
console.logoutput log in the debug panel, and the workflow status shows success but no expected data. Cause: TheCODE_RUN_LOG_ENABLEconfiguration item is not enabled, and logs are not redirected to the workflow debug panel. - Phenomenon: The code node returns a
500 Internal Server Errorafter execution in a locally deployed workflow. Cause: The Node.js runtime dependency is not configured in the local environment, or the code references third-party packages that are not installed. - Phenomenon: The AI model dropdown list for the problem classification node is empty. Cause: Available large model API keys are not configured in the platform backend, or model permissions are not enabled.
How to confirm the configuration is complete
- Enter the workflow debug panel, trigger an execution, and check whether the parsing node's time consumption matches the currently configured timeout threshold.
- Run the code node, view the output log in the debug panel, and confirm whether the custom
console.logcontent is printed normally. - Randomly select a piece of raw data, and check whether the field names and units after conversion by the field mapping rule conform to the preset standards.
- Call the official API interface to verify whether the
/v1/workflows/createinterface can normally create a dedicated workflow for baijiu due diligence.
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.