What the data for this category looks like
Data sources for hotel catering financing daily reports include store POS systems, reservation management systems, supply chain settlement systems, and local catering industry financing service platforms. The update cadence is daily T+1, generating aggregated data for the previous calendar day. The structure of individual data entries includes fields such as unique store code, store name, business type, daily total revenue, daily supply chain procurement expenditure, cumulative number of financing applications, current available credit limit, and approval progress. Field units include yuan, room nights, count, ten thousand yuan, and others. Some fields are enumeration values; for example, approval progress includes pending submission, under review, approved, rejected, and similar types.
What constraints do these characteristics impose on deployment and upgrade
Since data updates daily T+1 and requires aggregation from multiple systems, scheduled tasks must be configured to trigger data pulling and parsing during deployment. As the number of stores increases, storage and computing resource scaling must be supported. Enumeration fields for multiple business types require additional classification indexes to avoid field matching deviations during retrieval. Data fields include numeric types with different units, so unit conversion rules must be additionally configured during parsing. Upgrades must be compatible with historical data formats to avoid interrupting the daily report generation process. Additionally, the image includes multi-data-source adaptation components, has a large pull size, so image acceleration configuration must be considered during deployment.
How to set the configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CRON_SCHEDULE | 0 2 * * * | Matches the T+1 daily report generation time, triggers data pulling and index construction at 2 AM daily |
REPLICAS_COUNT | 2–5 | Adjust concurrent processing capacity based on the number of stores, avoid single-node overload, and adapt to parallel parsing of multi-store data |
RECALL_TOP_K | 10–20 | The hotel catering financing daily report knowledge base has a large number of entries, so a sufficient number of candidate documents must be recalled for precise matching |
SIMILARITY_THRESHOLD | 0.75–0.85 | Differentiate matching accuracy between financing requirements and operational data, avoid low-relevance entries being included in retrieval results |
PARSE_FILE_TIMEOUT_SECONDS | 600 seconds | Timeout threshold for processing a single store daily report data, adapt to the complex data parsing process after aggregation from multiple systems |
DOCKER_IMAGE_PULL_POLICY | IfNotPresent | Prioritize using locally cached images during deployment, resolve the issue of excessively long image pull 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 targeted analysis, and it is recommended to test on your own samples before finalizing settings.
Three common errors
- Phenomenon: docker-compose times out when pulling images, with logs returning the
ETIMEDOUTerror code. Cause: Image acceleration is not configured. The official image includes multi-data-source adaptation components, so pull time exceeds the default timeout limit. - Phenomenon: Knowledge base indexing speed is slow, and the number of recalled results falls below the preset range. Cause: The
RECALL_TOP_Kparameter is not adjusted, and the default number of recalled entries is used, which cannot cover the full data volume of multiple hotel and catering stores. - Phenomenon: Some fields of the financing daily report are empty after deployment, such as the
available credit limitfield having no data. Cause: The API key for the corresponding data source is not configured, and the synchronization switch for multi-system data aggregation is not enabled.
How to confirm the configuration is valid
- Manually trigger a data pull by executing the scheduled task, check that there are no
PARSE_FILE_TIMEOUTerrors in the system logs. - Test the knowledge base recall function, enter financing-related queries for the corresponding store, confirm that the recall results include the daily report data of the corresponding store, and the number of entries matches the configured
RECALL_TOP_Krange. - View the docker image pull logs, confirm that the pull is completed using a local cache or accelerated source, with no
ETIMEDOUTerrors. - Upload a test historical daily report file, confirm there are no upload failure prompts, and verify that the configuration takes effect.
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.