What this dataset looks like
Data sources include business systems of all first-level business segments, external market APIs, and the group financial middle platform. Full data refresh is completed every day at 1 AM on the day following the trading day. Each daily report document includes three structural layers: group-level summary, subsidiary segment breakdowns, and core holding target details. Fields include group code, subsidiary name, asset category, daily book profit/loss, end-of-period holding scale, and benchmark profit/loss. Book profit/loss is measured in yuan, holding scale is measured in ten thousand yuan. There is no unified fixed statistical sample size.
Constraints during deployment and upgrade
Since data sources involve multiple heterogeneous business systems and external interfaces, the deployment phase must adapt to multi-source authentication and format conversion logic. The upgrade phase must maintain compatibility with legacy data source connection configurations to prevent update interruptions. The layered structure of daily reports requires the parsing process to retain business hierarchy associations; direct flattening is not allowed. Fixed daily batch updates require precise scheduled scheduling to avoid consuming server resources during peak trading hours. The sensitivity of group-level data requires strict access permission and data desensitization rules during deployment. Permission policies must be updated synchronously during upgrades to maintain data security.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
SCHEDULER_CRON_EXPR | 0 2 1 * * ? | Aligns with the T+1 daily refresh schedule for market trend reports, avoids occupying server resources during peak trading hours |
PARSE_FILE_SPLIT_MODE | Layered Chunking | The dataset includes group summary, subsidiary breakdown, and holding detail layers. Layered chunking preserves business context associations |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Single daily report documents have large data volumes, so sufficient parsing time must be reserved |
DATA_SOURCE_WHITELIST | Configure IP ranges of each business segment's systems | Restricts access scope for connected data sources to protect group-level data security |
maxContext | 8000–12000 characters | Adapts to the length of document chunks after layered parsing, ensures the model can access complete business context |
PYTHON_RUNNER_TIMEOUT | 300 seconds | Reserves sufficient time to complete multi-source data cleaning and format conversion |
The parameter values provided on this page are common recommended starting points for configuration. Actual values are affected by material format, data volume, and business rules. Specific issues require targeted analysis. It is recommended to test on your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: The model backend receives response logs, but the workflow dialogue shows a failure. Cause: The
DATA_SOURCE_WHITELISTconfiguration is not set to allow access permissions for workflow nodes, or the interface authentication parameters used by the model are not synchronized to the workflow configuration. - Phenomenon: Waiting time for 16B model responses exceeds 5 minutes. Cause:
PYTHON_RUNNER_MAX_WORKERSis not adjusted to control concurrency, or model quantization is not enabled to adapt to single-card hardware resources. - Phenomenon: After upgrading from V4.9.3 to V4.12.2, historical data fails to load normally. Cause: The database mount volume configuration in
docker-compose.ymlwas not retained during the upgrade, or the database backup file was not exported in advance.
How to Verify Successful Configuration
- Trigger a manual scheduled task, verify that full data pulling and parsing from all data sources completes within a reasonable time frame, with no error logs.
- Upload a single standard daily report document, verify that the parsed vector chunks include complete hierarchical information for the group, subsidiaries, and holding details.
- Call the model test interface, verify that response times meet the expected thresholds for the business scenario, and workflow nodes return normal results.
- Check the
docker-compose.ymlconfiguration, confirm that the database mount volume path matches the pre-upgrade setting to avoid data loss risks.
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.