What Data Looks Like for This Use Case
Core data is sourced from engine Profiler, crash platforms, real-device testing tools, and frame rate collection systems. Updates follow a daily or per-release cycle. Daily deliverables include performance test reports, device compatibility lists, optimization records, and crash analysis reports. Data fields include frame rate, package size, resource load duration, crash stacks, and similar metrics. Units include seconds, MB, and others. Typical users are client engineers, engine engineers, and performance testing teams. Data must be paired with specific device and version scenarios to locate online stutters, crashes, and similar faults.
Constraints for HTTP Interfaces and External Systems
This use case’s data relies on real-device environments and real-time collection, which raises higher requirements for HTTP interface real-time performance and stability. Sub-scenario documents and data sets must be filtered by device and version, so external system calls must support passing targeted business variables. Fault troubleshooting requires correlating multi-source data, so interfaces must support precise field filtering to avoid invalid data transmission consuming client bandwidth. Online faults have strict timeliness requirements, so interface calls must include fault tolerance mechanisms to handle network fluctuations in real-device testing scenarios, ensuring troubleshooting workflows do not interrupt. Data version updates require interfaces to support multiple versions of performance report formats, preventing call failures caused by format changes.
Configuration Settings
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
Authorization | Bearer {ACCESS_TOKEN} | Complies with HTTP interface authentication standards, prevents permission leaks caused by using AI Proxy admin Token |
variables | {"testScenes":["低端机性能测试","崩溃复盘"],"dataTypes":["帧率数据","加载日志"]} | Adapts to targeted business scenarios for game client performance optimization, accurately filters required data |
responseData | Only includes abnormal frame rate, timed-out interface, and crash-related fields | Reduces data transmission volume, adapts to real-device testing environments with limited client bandwidth |
| Timeout Threshold | 30-60 seconds | Adapts to network fluctuations in real-device testing, prevents troubleshooting interruptions caused by timeouts |
| Retry Count | 2 attempts | Addresses temporary network jitter causing call failures, ensures stable data retrieval |
Three Common Misconfigurations
- Troubleshooting step: When an interface returns a 401 Unauthorized error, check if the AI Proxy admin Token was mistakenly entered into the
Authorizationfield. Use the application API Key as the authentication credential instead. - Troubleshooting step: If variable parameter parsing fails when passed, but works when a fixed value is used, check if the
variablesparameter uses valid JSON format. - Troubleshooting step: If cross-origin blocking errors occur when calling the interface from the frontend, check if the server has configured allowed request origins and properly handled cross-origin preflight requests.
How to Verify Proper Configuration
- Verify environment variables on the App/Admin server. Confirm that
AIPROXY_API_ENDPOINT(AI Proxy service root address) andAIPROXY_API_TOKEN(AI Proxy admin Token) are correctly configured per FastGPT v4.17.0 requirements, and are not exposed in the frontend or application layer. - Initiate a test call, pass the preset
variablesparameter, and confirm that theresponseDatareturned by the interface matches the expected filtering rules. - Simulate network jitter scenarios, confirm that the interface completes retries within the timeout threshold and returns valid data.
- Check server logs, confirm that the authentication field
Authorizationuses a valid format, and no permission-related errors are present.
The values provided are common starting points and should be measured against the reader's own samples.
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.