What this category of data looks like
This data primarily comes from engine Profiler, crash platforms, real-device testing tools, and frame rate collection tools. It updates on a daily or per-version basis. Associated daily documents include performance test reports, device compatibility lists, optimization records, and crash analysis reports. Core fields include device model, average frame rate, peak memory usage, crash stacks, and resource loading duration. Common units are frames per second (fps), milliseconds (ms), and megabytes (MB). Individual log volume increases as test coverage expands.
What constraints these characteristics impose on conversation logs and auditing workflows
The data comes from scattered sources and links to multiple specialized document types. Configure conversation logs to bind metadata for corresponding device models, test versions, and optimization records. This enables cross-document traceability later. Differences in update frequencies require auditing workflows to support filtering logs by day or version. This aligns with different teams’ retrospective cycles. Retain non-general fields such as frame rate and memory usage in logs to meet professional troubleshooting content needs. Limit the scale of single-batch log pulls in auditing workflows to avoid excessive computing resource consumption, given the large size of individual logs. Add support for full-text search and precise matching for long-text fields such as crash stacks.
How to configure settings
| Configuration Item | Recommended Practice | Rationale |
|---|---|---|
chatId | Bind test version and device model identifiers | Associate metadata for performance test reports and device compatibility lists, enabling retrieval by test dimension |
responseChatItemId | Bind frame rate and memory usage field IDs | Precisely associate conversation response content related to performance optimization, preventing unrelated logs from being included |
detail | Retain complete crash stacks and resource loading logs | Meet the needs of professional troubleshooting for long-text fields |
| Log retention period | 7–30 days | Cover version iteration cycles and retrospective requirements. Adjust based on actual measurements |
| Single-batch pull limit | Top 100 entries | Limit log volume to avoid excessive consumption of computing resources |
| Metadata association toggle | Enable device model and test version tags | Match the classification logic of daily documents, enabling cross-document traceability |
Three common configuration errors
- Symptom: An error indicating
chatIdis empty appears in logs. Cause: Metadata for test versions and device model identifiers is not bound. This prevents logs from being linked to corresponding troubleshooting documents. - Symptom: Abnormal log calls occur overnight, with log counts far exceeding preset thresholds. Cause: The single-batch pull limit is not configured. This leads to resource consumption from bulk pulls or triggers unauthorized calls.
- Symptom: Only partial content is returned when pulling logs, and full conversation records cannot be obtained. Cause: The single-batch pull limit is not correctly configured, or the full-retrieval toggle is not enabled.
How to confirm configuration is complete
- Review the log list to confirm that each log is bound with metadata tags for device model and test version.
- Initiate a log pull request, and verify that the number of returned entries matches the preset single-batch pull limit.
- Search for crash stack keywords, and confirm that corresponding conversation logs and associated documents can be matched.
- In the FastGPT v4.17.0 configuration panel, check that the storage toggle for the
detailfield is enabled.
The values provided here are common starting points and should be evaluated 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.