What the data for this category looks like
Data sources for rural commercial bank financing daily reports include three types of systems: internal core business systems, the People's Bank of China Financial Statistics Monitoring and Management Information System, and regional financing submission data from local financial supervision bureaus. Reports are generated on a daily T+1 cycle. Statistical data for the previous day is compiled, and updates finish during the early morning of the next day. Documents use structured formats, with Excel or CSV as common delivery methods. Each row contains financing data for one local branch or enterprise. Core fields include: statistical date, total credit limit (ten thousand yuan), number of new disbursement transactions that day, total amount of due repayments that day (ten thousand yuan), balance of agricultural-related loans (ten thousand yuan), and balance of small and micro enterprise loans (ten thousand yuan). Data volume varies based on the number of local enterprises. Daily reports typically contain fewer than several thousand rows.
What constraints these characteristics impose during deployment and upgrade
Multiple data sources require separate authentication parameter configuration for internal systems and external regulatory interfaces during deployment. Deployments must follow financial data permission isolation specifications. The fixed T+1 update cycle requires scheduled tasks to trigger precisely when regulatory data becomes available. Early task triggers will result in missing data. Structured data does not need complex parsing, but subtle differences exist in field names across sources. Teams must configure flexible field mapping rules to unify data formats. The sensitivity of financial data requires enabling data desensitization configuration during deployment. This hides sensitive enterprise information. Upgrade processes must avoid interrupting daily scheduled tasks. Rolling upgrade or gray release strategies must be used to ensure daily report generation workflows are not impacted.
How to Set Configurations
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
datasource_api_key | Create separate keys for internal systems and external regulatory interfaces | Follow financial data permission isolation requirements, prevent a single leaked key from compromising all data sources |
schedule_cron | 0 30 1 * * ? (1:30 AM daily) | Matches the T+1 midnight release rhythm of most regulatory data, ensures complete previous day’s statistical data is retrieved |
field_mapping_rule | Configure mappings by "source system - standard field", for example map internal system's "当日投放笔数" to new_loan_count | Adapt to field name differences across multiple data sources, unify data format to support subsequent analysis |
max_context | 8000 characters | Financing daily reports for rural commercial banks have short individual data entries, no need for large context windows, avoids wasting computing resources |
retrieve_top_k | Top 20 entries | Matches the daily data volume of rural commercial bank financing reports, covers most business analysis scenarios |
data_desensitization_enabled | Enabled | Complies with financial data regulatory compliance requirements, hides sensitive enterprise information such as the last four digits of the unified social credit code |
The parameter values provided on this page are common starting points for configuration. Actual values are affected by material format, 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 Errors
- Phenomenon: In version V4.8.22, the AI configuration page only displays simple parameter adjustment options, with no access to the custom prompt template entry. Cause: This version does not include the advanced configuration module by default. The
enable_advanced_ai_configcompilation parameter must be manually enabled to use this feature. - Phenomenon: Users accessing via login-free links who delete a conversation will not have a corresponding entry generated in the backend operation log. Cause: The
log_anonymous_user_operationparameter was not set totrueduring deployment, so anonymous user operations are not written to logs. - Phenomenon: All rows in imported rural commercial bank financing daily report data have null values for the
new_loan_countfield. Cause: Thefield_mapping_rulewas not configured correctly, with a spelling error in the field name when mapping the internal system's "当日投放笔数" to the standard field.
How to Confirm Proper Configuration
- Manually trigger a scheduled sync task, verify that the returned financing daily report data fields match the preset standard mapping rules.
- Generate a login-free test link, use it to delete a test conversation, check if a corresponding operation record is generated in the backend log.
- Access the AI configuration page, confirm that the custom prompt template editing interface is available.
- Initiate a data retrieval test, check that the number of returned results matches the preset retrieval upper limit.
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.