What the data for this category looks like
Data sources for comprehensive service financing daily reports include public APIs from major domestic trading venues, regulatory authorities, and official disclosures from licensed financial institutions. Updates are completed at a fixed daily time for full daily data. Documents use structured table formats, with fields including statistical date, market sector, total margin purchase amount, total margin balance, total short sale amount, short sale remaining quantity, number of underlying assets, and others. Units are uniformly billion yuan and count units. Data is stored as a single daily summary file or multi-underlying row-based files, with no overly nested unstructured content.
Constraints imposed on deployment and upgrade
Multi-source data access requirements mandate configuring multiple sets of data source verification rules during deployment, to avoid data format conflicts across different channels. The fixed daily update rhythm requires matching scheduled task trigger timing, to ensure synchronized data is the latest for the current day. The large number of structured fields requires configuring accurate field mapping rules during deployment, to ensure correct data import and retrieval. During upgrades, old version field mapping logic must be compatible, to prevent retrieval failures caused by adjustments to data source fields. Changes in data scale require configuring a vector database sharding strategy, to balance retrieval performance and resource usage.
How to configure parameters
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
CRON_EXPRESSION | 0 18 * * * | Matches the rhythm of most data sources updating after 17:00 daily, ensuring access to the latest same-day data |
STRUCTURED_DATA_FIELD_MAPPING | {"Statistical Date": "date", "Total Financing Purchase": "buy_total", "Financing Balance": "balance_total"} | Matches the standard field names of financing daily reports, ensuring correct parsing and import of structured data |
VECTOR_DB_SHARD_COUNT | 2-4 | Adapts to the field count and data scale of a single daily report, balancing retrieval speed and resource usage |
EMBEDDING_BATCH_SIZE | 64 | Adapts to the field length of structured data, avoiding timeouts for single embedding requests |
SCHEDULER_MAX_RETRIES | 3 | Addresses temporary fluctuations in data source APIs, ensuring data synchronization success rate |
CHAT_HISTORY_EXPIRE_DAYS | Set based on business requirements | Controls the storage duration of chat records, avoiding redundant data occupying resources |
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 individual analysis, and it is recommended to test on your own samples before finalizing.
Three common configuration mistakes
- Phenomenon: After upgrading to version V4.14.7.1 or V4.14.8, knowledge base retrieval latency increases significantly. Retrieval results return more slowly with the same knowledge base and embedding model. Cause: The
VECTOR_DB_SHARD_COUNTparameter was not adjusted to adapt to the new vector database sharding strategy, causing more shard nodes to be traversed during retrieval. - Phenomenon: Plugin pull failure error logs appear during Docker deployment, and containers restart continuously and cannot start normally. Cause: No domestic mirror source was configured to accelerate plugin pulling, and official plugin packages cannot be obtained normally in overseas network environments.
- Phenomenon: The storage duration of chat records cannot be limited after deployment, and historical chat data continues to occupy storage resources. Cause: The default value of the
CHAT_HISTORY_EXPIRE_DAYSparameter was not modified, or the chat record automatic cleanup switch was not enabled in the deployment configuration.
How to confirm configuration is correct
- Run a manual data synchronization task, check synchronization logs for field parsing failure prompts, and confirm that the
STRUCTURED_DATA_FIELD_MAPPINGconfiguration matches data source fields. - Submit a retrieval request for the financing daily report, check the retrieval latency of returned results, and adjust the values of
MAX_RECALL_NUMandEMBEDDING_BATCH_SIZEbased on business requirements. - Check scheduled task execution records, confirm whether data synchronization is successfully triggered at the fixed daily time, and verify the correctness of the
CRON_EXPRESSIONconfiguration. - Check container storage usage, confirm whether the chat record automatic cleanup function is executed according to the configuration, and verify the effective status of
CHAT_HISTORY_EXPIRE_DAYS.
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.