HTTP Interfaces and External Systems for Rural Commercial

What the Data Looks Like

Data sources for rural commercial bank intelligent due diligence reports include internal credit core systems, People's Bank of China credit inquiry interfaces, National Enterprise Credit Information Publicity System, and local banking and insurance regulatory reporting data. Internal credit data syncs on a T+1 schedule. External public data uses the latest update timestamp returned by the interface. Document structure includes four modules: main overview, credit and overdraft details, related party transaction records, and regulatory rating. Fields include unified social credit code, credit limit (unit: ten thousand yuan), overdraft days (unit: days), related party name, regulatory rating level, and others. Single report document length varies widely. It is recommended to calculate based on internal samples or conduct testing before finalizing values.

Constraints for HTTP Interfaces and External Systems

Multi-source data covers internal core systems and multiple external public interfaces. External system connections must support multi-source data aggregation. Cross-system identity authentication parameters require configuration. Update rhythms differ across data sources. HTTP interfaces must support incremental data pulling by specified timestamp. Reserved space must be available for real-time callback adaptation. Report fields include numeric and enumerated fields with clear units. Interfaces must support field mapping rule configuration to avoid unit conversion errors. Single report has a large character volume. Interfaces must adapt to large request and return payloads. Timeout thresholds must be adjusted to prevent data truncation.

Configuration Settings

Configuration Item	Recommended Value	Rationale
`external_api_timeout`	`300 seconds`	Rural commercial bank due diligence reports require multi-source data aggregation. Total time for multiple interface calls is long. 300 seconds covers most aggregation scenarios
`field_mapping_strategy`	Match by field name + unit	Report fields include numeric fields with units. This avoids mapping errors caused by inconsistent units
`incremental_sync_interval`	`86400 seconds`	Internal credit data updates on a T+1 basis. Daily synchronization ensures data timeliness
`max_response_payload_size`	`50 MB`	Single due diligence report has large character volume. This adapts to interface return requirements for long documents
`api_auth_type`	`Bearer Token + IP whitelist`	Rural commercial bank data involves financial sensitive information. Dual authentication improves data security
`retry_count_on_failure`	`3 times`	External interfaces may experience temporary fluctuations. 3 retries reduces single call failure rate

The parameter values provided on this page are common starting points for configuration. Actual values are affected by material form, data volume, and business rules. Specific issues require targeted analysis. It is recommended to conduct testing on internal samples before finalizing values.

Three Common Misconfigurations

Symptom: column vector does not exist error is returned when calling the vector query interface. Cause: The system initialization script was not executed after reinstalling the pgvector extension. The database cannot recognize vector type fields.
Symptom: HTTP interface calls return title content but no corresponding due diligence results in conversation logs. Cause: The report_type field in the external_api_request_body parameter was not configured, or the field value does not meet rural commercial bank due diligence requirements. The large language model cannot match the corresponding data.
Symptom: 408 Request Timeout status code is returned for interface calls. Cause: The external_api_timeout configuration was not adjusted. The timeout threshold was set too short, and cannot cover the time required for multi-source data aggregation.

How to Confirm Successful Configuration

Call the test interface with the unified social credit code of a rural commercial bank entity. Check if the returned result includes preset fields and units.
Review interface logs to confirm that multi-source data aggregation time meets the configured timeout threshold requirements.
Verify that vector type fields are created normally in the database. Confirm that the pgvector extension initialization script has completed execution.
Trigger an incremental sync task. Check if data update times match the configured sync interval rules.

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.