What data for this category looks like
Data originates from internal core business modules including corporate credit management systems, interbank business trading systems, and capital operation middle platforms. The system collects daily business data at a fixed time point each day, then generates a financing daily report for the previous natural day. Documents use structured CSV or fixed-template Excel format, and include fields such as statistical date, unified social credit code of the financing subject, financing amount (ten thousand RMB), financing interest rate (annualized %), financing term (days), counterparty, business type, and risk control rating. All fields are standardized structured content, with no free-text unstructured data.
What constraints these characteristics impose on model access and configuration
Data sources are internal core business systems, so model access must comply with strict internal authentication specifications. Security mechanisms including two-way SSL authentication and static tokens must be configured. The fixed daily update rhythm requires scheduled synchronization tasks to trigger after the daily report generation time point, to avoid pulling incomplete semi-finished data. Structured fields and fixed units require model input and output to strictly match field formats and unit rules, with no arbitrary conversion or truncation. Highly sensitive business data requires desensitization rule configuration to hide sensitive fields such as counterparty and unified social credit code, to meet financial industry compliance requirements.
How to set configurations
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
data_sync_cron | 0 19 * * * (Cron expression, 19:00 daily) | Matches the rhythm of financing daily reports being generated before 17:30 each day for state-owned large banks, pulling data 1.5 hours later to ensure completeness |
rag_chunk_size | 800-1200 characters | A single structured record in the financing daily report is approximately 500 characters long. Splitting within this range preserves field integrity and avoids truncating critical information |
api_auth_type | Two-way SSL authentication + static token | Adapts to strict authentication requirements of internal systems for state-owned large banks, and complies with internal data security specifications |
response_format | JSON format | Matches the output requirements of structured fields in the financing daily report, facilitating subsequent data processing and analysis |
timeout_seconds | 600 seconds | Prevents timeout errors when processing bulk data from large financing daily reports |
sensitive_field_mask | Counterparty, unified social credit code | Hides sensitive fields per internal data desensitization requirements, complying with financial industry compliance standards |
The parameter values provided on this page are all conventional recommendations used as a starting point for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require individual analysis. It is recommended to test on your own samples before finalizing settings.
Three common mistakes
- Phenomenon: After adding a locally deployed large model to the configuration, the interface displays an error prompt "Model only supports streaming output". Cause: The non-streaming call compatibility setting of the model is not enabled. Bulk data processing for financing daily reports requires stable non-streaming returns, leading to compatibility exceptions.
- Phenomenon: When accessing a third-party deployed model via aiproxy, a 500 status code is returned with the prompt "Cannot read p". Cause: The request header and parameter mapping for model access are not configured correctly, causing aiproxy to fail to properly parse the raw data returned by the model.
- Phenomenon: The field order in the financing daily report analysis results generated by the model does not match the original report, with field misalignment. Cause: Structured data field mapping configuration is not enabled, and data is processed in the default order, leading to matching errors.
How to confirm the configuration is complete
- Initiate a manual data pull, and verify that the pulled financing daily report fields match the original report’s field names and quantity. Adjust field mapping configurations until they match.
- View the structured data format returned by the model, confirm it matches the output format specified in the configuration, and check whether sensitive fields have been processed according to compliance requirements.
- Verify the scheduled task trigger logic, confirm that the latest financing daily report data is automatically pulled at the specified daily time point, with no duplicate or missed pulls.
- Simulate single and bulk data processing flows, confirm that there are no unit mix-ups, field misalignment, or other issues in the model-processed results. Adjust relevant configurations until expectations are met.
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.