What Data Looks Like for This Category
Data for this category primarily comes from engine Profiler tools, crash reporting platforms, real device test equipment, and frame rate collection tools. Daily documents include performance test reports, device compatibility lists, optimization records, and crash analysis reports. Common fields in these documents cover frame rate values, resource occupancy ratios, device models, reproduction scenarios, and pre- and post-optimization comparison data. Update frequency follows a daily or per-version iteration cycle. Core data centers on game client frame rate, package size, and resource loading duration. Unlike general mobile development, this category focuses more on game-specific performance metrics.
What Constraints Do These Characteristics Impose on the Context and Token Link
Document data for game client performance optimization mostly consists of structured performance indicators and scenario-based reproduction records. The length of individual data entries varies widely, ranging from a few lines of frame rate statistics to several pages of crash analysis reports. The daily or per-version update frequency requires context recall to cover recent optimization records and latest device compatibility data. This avoids referencing outdated performance conclusions. Documents in this category must also be associated with specific device models and frame rate thresholds. Context recall must accurately match fields such as device model and resource type. An overly large context range introduces irrelevant historical optimization data, which interferes with reference documents for current requirements. Issues such as lag and app crashes are directly tied to user feedback. Context recall must prioritize high-priority crash analysis and reproduction step documents.
How to Configure Settings
| Configuration Item | Recommended Values | Rationale |
|---|---|---|
maxContext | 12000–18000 token | Game client performance optimization documents include multiple performance reports and device data. A reasonable context range can cover core optimization records and test data from 1 to 2 versions, while avoiding exceeding the model processing limit of FastGPT v4.17.0. |
quoteMaxToken | 2000–3000 characters | The length of a single core document such as a crash analysis report or frame rate test data is mostly in the thousands of characters range. Limiting the quoted token length ensures that extracted content accurately matches current requirements, and avoids interference from redundant information. |
| Context recall similarity threshold | 0.75–0.85 | Documents in this category mostly take performance indicators as their core. They require accurate matching of fields such as device model and optimization scenario. A threshold that is too low will introduce irrelevant historical optimization data, while a threshold that is too high may miss relevant device compatibility records. |
| Single recall upper limit | Top 8 entries | Documents associated with daily performance optimization requirements mostly number no more than 5. Limiting the number of recalled entries reduces redundant data processed by the model, and speeds up the generation efficiency of document references. |
| Context chunk overlap length | 200–300 characters | Performance test reports mostly consist of segmented indicator statistics. Overlapping chunks ensures that key data such as frame rate and resource occupancy is fully extracted, avoiding information loss caused by chunk truncation. |
| Recalled document sorting rule | Sorted by update time in descending order | Performance optimization data has strong timeliness. The latest device compatibility lists and optimization records have higher reference value for current requirements. Recalling recently updated documents first is preferred. |
Three Common Errors
- Phenomenon: Optimization suggestions generated by the model fail to match the frame rate requirements of the current device. Cause: The context recall similarity threshold is set too high, which misses performance test report data for the corresponding device model, resulting in insufficient reference documents.
- Phenomenon: Context configuration does not take effect after docker deployment, and performance optimization documents cannot be loaded. Cause: The data source directory for performance test reports and crash analysis reports is not correctly specified in the configuration items, resulting in an empty document pool associated with tokens.
- Phenomenon: Generated requirement documents include outdated package size optimization data. Cause: Recalled context is not sorted by update time, or
maxContextis set too large, introducing optimization records from multiple previous versions, which interferes with the reference logic for current requirements.
How to Verify Correct Configuration
- Manually import a recent device compatibility list and performance test report, and verify whether context recall can match this document.
- Trigger a requirement drafting process, and check whether the generated reference documents include the current version’s optimization records and real device test data.
- View the FastGPT v4.17.0 configuration log, and confirm that the values of
maxContextandquoteMaxTokenhave been loaded as specified. - Simulate a low-end device performance test scenario, and verify whether the recalled documents include adaptation requirements for the corresponding device model.
The values provided here are common starting points, and should be evaluated against respective test 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.