What the data for this category looks like
Daily documents for this segment include account ban records, appeal materials, review opinions, and user notifications. Data originates from risk control systems, audit records, user behavior logs, and report evidence. Update cycles fall into two categories: real-time and daily. Real-time data covers appeals and violation trigger submissions from users in real time. Daily-level data consists of batch-summarized ban disposal logs. Document fields include account ID, violation type tag, appeal submission time, supporting material link, review result, and adjusted disposal level. Time fields use standard timestamps or YYYY-MM-DD HH:MM:SS format. Account identifiers use string data types.
What constraints these characteristics impose on HTTP Interfaces and External Systems
Multi-source data input requires interfaces to support multi-dimensional parameter filtering. This enables accurate pulling of corresponding risk control, audit, and log data. Mixed real-time and daily update cycles require interfaces to support both real-time single-account queries and batch incremental synchronization. This avoids platform load pressure from full-volume pulls. Standardized document field structures require interface parameters and return formats to strictly match field definitions. This ensures external systems can correctly parse associated data. The most time-consuming violation evidence verification and appeal reason judgment steps require interfaces to support asynchronous processing or set reasonable timeout thresholds. This prevents request interruptions that cause task failures. Severe error consequences require interfaces to configure retry mechanisms and complete log records. This facilitates post-hoc troubleshooting and correction.
How to set configurations
This page's configurations apply to FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
Authorization | Bearer ${fastgpt_api_token} | Follow HTTP interface standard authentication rules to ensure valid calls to FastGPT interfaces |
variables | {"targetAccount": "", "violationEvidence": []} | Match core query and input parameters for account ban review, accurately associate target accounts and supporting materials |
responseData | {"recheckStatus": "", "adjustedPenaltyLevel": "", "notifyTime": ""} | Standardize encapsulation of review results, disposal adjustments, and notification information to facilitate parsing and processing by external systems |
| Timeout setting | 300–600 seconds | Cover the typical time required for violation evidence verification and appeal reason judgment, avoid task failures from early request interruptions |
| Batch pull limit | Top 100 entries | Balance interface load and data synchronization efficiency, adapt to the volume of ban records and appeal materials for daily-level updates |
Three common configuration mistakes
- Phenomenon: Calling the API generated by FastGPT returns authentication failure or parameter parsing errors, and the review process cannot be triggered. Cause: The
Authorizationparameter is not configured correctly, or thevariablesfield lacks core identifiers such as targetAccount. This prevents the interface from locating target review data. - Phenomenon: The interface call returns no results, or returns an empty
responseDatafield. Cause: No reasonable timeout period is set. The time required for violation evidence verification exceeds the interface timeout threshold, causing the request to be forcibly interrupted. - Phenomenon: External system workflows appear blank when pulling ban records in batches, but the interface call status shows normal. Cause: The batch pull limit is set too high, exceeding the load threshold of FastGPT nodes. This causes synchronization link blocking and delays in updating workflow caches.
How to confirm configurations are set correctly
- Call the configured interface, check if the returned
responseDataincludes preset fields such as review results and adjusted disposal level. Ensure the field structure matches the configured return format. - Simulate submitting a test appeal data, trigger the review process via the interface. Confirm external systems receive expected response content.
- Adjust the timeout period and batch pull limit. Verify interface stability across different loads through multiple calls. Ensure compliance with time requirements for business scenarios.
- View FastGPT interface logs. Confirm that incoming call requests are properly received and processed. No authentication failure or parameter error messages are present.
The values provided are common starting points and should be measured against samples from the actual deployment environment.
Question material comes from public community discussions. Configuration values are common starting points and should be measured against your own samples. Verified on 2026-10-19.