What the data for this category looks like
Data related to baijiu yield rates mainly comes from public securities market APIs and industry data service providers. Updates are performed in batches after market close on each trading day. Real-time approximate change data can be provided during trading hours. Each data entry includes fields such as trading date, asset identifier, asset name, opening price, closing price, relative change value, total trading volume, total trading amount, etc. The unit for price fields is Renminbi Yuan, the unit for total trading volume is shares, the unit for total trading amount is Renminbi Yuan, and relative change values are recorded as decimals to represent daily yield change ranges.
Constraints for deployment and upgrade
The multi-source access and scheduled update rhythm of baijiu yield rate data require that a dedicated scheduled pull task for trading days be configured during deployment. Automatic pause of pull operations during non-trading hours saves resources. Since the asset codes and names for baijiu subcategories are relatively fixed but may have new additions, asset whitelist verification rules must be configured during deployment to prevent non-target category data from being included. Real-time market pull operations must adapt to API current limiting thresholds. During upgrades, the asset pool mapping table must be updated synchronously to ensure the accuracy of data pulls. At the same time, field verification rules must adapt to decimal-form yield change values to avoid subsequent broadcast errors caused by format mistakes.
Configuration settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SCHEDULE_CRON_EXP | 0 30 17 * * 1-5 | Matches the closing data update time around 17:30 on domestic A-share trading days, triggers scheduled pull tasks only on weekdays |
DEPLOY_HARDWARE_TYPE | ascend-910B | Adapts to the hardware environment of Huawei Ascend 910B chips, meets the computing power requirements for large model inference |
XINFERENCE_API_BASE | http://localhost:9997/v1 | Adapts to the default deployment port and API path of Xinference, completes model channel configuration |
WORKFLOW_GLOBAL_VAR_ENABLE | true | Enables workflow global variable passing functionality, supports passing parameters such as tokens to MCP tools |
AIPROXY_UPDATE_MODE | patch | Uses incremental update mode, only replaces core proxy components, adapts to deployment upgrade scenarios switching from OneAPI |
PARSE_DATA_FIELD_RULES | {"change": {"type": "float", "range": [-1, 1]}} | Verifies the format and range of yield change values, prevents abnormal data from entering subsequent broadcast 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. It is recommended to test on your own samples before finalizing settings.
Three common configuration mistakes
- Symptom: When calling an MCP tool in a workflow, the token global variable received by the tool is empty, and a
401 Unauthorizedstatus code is returned. Cause: TheWORKFLOW_GLOBAL_VAR_ENABLEconfiguration item is not enabled, and the system does not open global variable passing permissions, causing variables to not be obtained by MCP tools. - Symptom: After configuring the Xinference model channel, an
endpoint not founderror is prompted when calling the model. Cause: TheXINFERENCE_API_BASEparameter is configured incorrectly, the default/v1API path is not added, or the port does not match the actual deployment. - Symptom: After upgrading the aiproxy component, the original external model call link is interrupted. Cause: Full update mode is used to replace all configurations, and original proxy rules are not retained, causing configurations switched from OneAPI to be cleared.
How to verify a successful configuration
- View scheduled task running logs to confirm that data pulls are successfully executed during the preset trading day trigger window, with no abnormal errors.
- Call the model channel test interface, submit a standard test request, confirm that the configured Xinference service can be connected normally and valid results are returned.
- Manually trigger the MCP tool call in the workflow, pass a preset global variable, confirm that the tool can correctly obtain and use the variable.
- Check the aiproxy version information and running logs, confirm that incremental updates have been completed and original proxy configurations are not affected.
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.