What Data for This Category Looks Like
The core data for this category comes from engine Profiler, crash platforms, real-device testing tools, and frame rate collection tools. Updates occur daily or per version iteration. Daily output documents include performance test reports, device compatibility lists, optimization records, and crash analysis reports. Typical document fields include test device model, frame rate (fps), memory usage (MB), pre- and post-optimization comparison data, and other relevant items. Document formats adjust dynamically based on test scenarios, with no unified fixed structure.
Constraints Imposed by These Characteristics on Knowledge Base Retrieval and Recall
Decentralized data sources and inconsistent formats require the retrieval link to adapt to multiple document structures, preventing missed recalls due to format differences. The high-frequency update feature requires the retrieval link to limit the context loaded per retrieval, avoiding increased latency from accumulated historical conversations. The coverage of multi-scenario document types requires retrieval rules to prioritize core performance optimization entries, while also addressing scenarios such as device compatibility and crash analysis. High-latency steps for low-end device reproduction and resource analysis also require retrieval results to accurately point to reusable optimization solutions, reducing interference from irrelevant information.
How to Configure Settings
This page is written based on the configuration logic of FastGPT v4.17.0. Below are recommended configurations for the game client performance optimization scenario:
| Configuration Item | Recommended Value | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches the semantic precision requirements for game performance optimization-related documents, filtering low-relevance general technical content |
limit | Top 10 entries | The total volume of game client optimization-related documents is moderate; too many entries will increase context processing burden |
searchMode | Semantic Retrieval Mode | Delivers more stable semantic matching effects for professional technical documents, aligning with the actual needs of this scenario |
usingReRank | Enabled | Filters low-relevance performance test report entries, improving the accuracy of final recall results |
datasetSearchUsingExtensionQuery | Enabled | Expands question semantics, covering common performance optimization terminology variants used by engineers, and improving recall coverage |
collectionIds | Bind dedicated performance optimization knowledge base collection | Isolates document resources across different categories, avoiding interference from irrelevant content in retrieval results |
Three Common Misconfigurations
- Phenomenon: After multiple rounds of questions in the same conversation window, knowledge base retrieval latency exceeds 60 seconds. Switching to a new window restores normal latency. Cause: The conversation context length is not limited, and each retrieval loads excessive historical conversation content, increasing the load on the retrieval link.
- Phenomenon: Semantic retrieval returns results with high scores, but they do not match the current optimization scenario. For example, recalling package size optimization documents for a low-end device stutter issue. Cause: The
similaritythreshold is not configured, or the threshold is set too low, resulting in the recall of a large number of entries that meet the score requirement but are irrelevant. - Phenomenon: Directly referencing the knowledge base ID returns results normally, but an error occurs when passing the knowledge base ID via application variables. Cause: The
collectionIdsconfiguration is not correctly bound, or the permission rules of the knowledge base collection are not matched during variable passing.
How to Verify Proper Configuration
- Initiate more than 5 consecutive performance optimization-related questions, check whether the latency of each retrieval remains within a reasonable range with no obvious increasing trend.
- Switch between different search modes, compare the relevance of returned results, and verify whether the actual effect of the
searchModeconfiguration meets expectations. - Attempt to pass the knowledge base collection ID via application variables, check whether documents from the corresponding collection can be correctly recalled with no error messages returned.
- Adjust the value range of the
similarityparameter, verify whether the number and relevance of returned results meet the requirements of the current scenario.
The values provided 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.