What the Data for This Category Looks Like
Rural commercial bank financing daily reports pull data from internal credit management systems, core business systems, and financial statistics data required for submission by local banking and insurance regulatory bureaus. Reports for the previous calendar day are generated by 07:00 each day. Documents use a modular structured tabular format, with fields including jurisdictional institutions, financing subject type, financing term range, investment amount, number of application households, number of approved transactions, number of completed due repayment transactions, and more. Field units are ten thousand yuan, number of households, and number of transactions, respectively.
Constraints for Multi-turn Dialogue and Prompt Engineering
The structured multi-module format of financing daily reports requires multi-turn dialogue to support layered follow-up queries by institution and subject type. Layered context recall rules must be configured. The daily T+1 update schedule means dialogue cannot request unscheduled same-day data. Prompts must clearly limit data time ranges to pre-generated calendar daily reports. Fields have clear specified units, so prompts must mandate matching these units in outputs to avoid confusion. Multiple data sources require dialogue to only use compliant internal data sources, preventing calls to irrelevant external data.
Configuration Recommendations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
maxContext | 8000–12000 tokens | A single rural commercial bank financing daily report document is approximately 3000-5000 tokens. Multi-turn dialogue needs to retain 3 rounds of context, so setting 8000-12000 tokens covers context requirements |
retrievalTopK | Top 6 entries | Financing daily reports contain multi-module statistical fields. Recalling 6 entries covers most query needs while avoiding interference from redundant information |
promptTemplate | "Based on the uploaded rural commercial bank financing daily report data, answer after filtering by specified institution and subject type. Output must match field units, and only use the provided data to generate content" | Adapts to the structured characteristics of rural commercial bank financing daily reports. Clearly defines data scope and output rules to avoid generating irrelevant content |
logAutoCleanThreshold | Triggered when conversation rounds ≥10 | Conversations for rural commercial bank financing daily reports usually need to retain context for multiple follow-up queries. Setting a higher cleanup threshold prevents accidental deletion of associated logs |
dataSourceWhitelist | "Credit management system, core business system, banking and insurance regulatory statistics data" | Limits use to only compliant internal data sources for rural commercial banks, to avoid calling irrelevant external data that reduces result accuracy |
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, and it is recommended to test against your own samples before finalizing settings.
Three Common Misconfigurations
- Phenomenon: Manually deleting a single round of dialogue causes the entire associated multi-turn dialogue log to disappear. Reason: The
logAutoCleanThresholdis not configured to retain by conversation rounds, and the default content-based cleanup rule is enabled, which accidentally deletes the associated context along with the targeted log. - Phenomenon: Prompts cannot correctly reference the institution parameter specified in a multi-turn dialogue. Reason: The prompt template does not include explicit instructions to retain and reuse parameters from the previous round of dialogue, resulting in context not being correctly recalled.
- Phenomenon: After enabling image recognition configuration, uploaded screenshots of financing daily reports cannot be parsed correctly. Reason: Image recognition trigger conditions are not limited to only structured report screenshots, or the recognition threshold adapted to report text is not configured, causing the model to fail to recognize the table structure.
How to Confirm Proper Configuration
- Initiate a financing daily report query specifying a jurisdictional institution, then initiate a second follow-up query for agricultural subject financing data of that institution. Verify that the context retains the previously specified institution name.
- Manually delete a single round of conversation content. Verify that the log system only deletes that round of records without clearing associated context data.
- Upload a simulated rural commercial bank financing daily report document, configure the prompt template, then initiate a query. Verify that the output matches the specified field units.
- After enabling image recognition configuration, upload a structured screenshot of a financing daily report. Verify that the model correctly extracts fields and values from the table.
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.