What data for this category looks like
Daily data for this category comes from game server logs, online user monitoring systems, business databases, and player-submitted tickets. Update frequency is mostly daily sync. Additional updates are scheduled during event cycles to align with event timelines. Typical documents include server architecture descriptions, server launch capacity plans, server merging data verification rules, historical fault troubleshooting reports, and more. Fields include server IP ranges, online user thresholds, data sync delay metrics, fault code corresponding processing workflows, and others. Units include units, user-times, milliseconds, GB, and additional standard units.
What constraints these characteristics bring to the knowledge base retrieval and recall link
Documents in this category mostly involve professional operations and maintenance terminology and structured business metrics. Individual documents may have large file sizes, update frequency is high, and most retrieval needs fall into core scenarios such as fault troubleshooting, server launch, and server merging operations. Uploading large documents increases indexing time, which can delay subsequent retrieval tasks. Frequently updated data requires the retrieval link to quickly sync the latest content. Semantic matching for professional terminology requires optimization to prevent misjudgments from general retrieval models. Business data must also be strictly isolated to stop unrelated content from appearing in retrieval results, which reduces the efficiency of operations and maintenance decision-making.
How to set configurations
| Configuration Item | Recommended Value Range | Rationale |
|---|---|---|
similarity | 0.75–0.85 | Matches core content such as fault descriptions and operation steps in game operations and maintenance scenarios. Prevents missing valid recall due to overly high thresholds, or introducing irrelevant results due to overly low thresholds |
limit | Top 6–10 results | Valid information for operations and maintenance tickets is usually concentrated in a small number of relevant documents. Controlling the number of returned results reduces context redundancy |
searchMode | vector+keyword hybrid mode | Balances keyword matching for log text and semantic matching for operations and maintenance terminology, improving retrieval accuracy |
usingReRank | Enabled | Performs secondary sorting on initial recall results to filter low-relevance content, adapting to the precise retrieval needs of professional operations and maintenance scenarios |
datasetSearchUsingExtensionQuery | Enabled | Automatically optimizes retrieval expressions for operations and maintenance professional terminology, improving the success rate of cross-term matching |
collectionIds | Specify the knowledge base collection ID corresponding to game server operations and maintenance | Isolates business data, only retrieves operations and maintenance documents within the specified range, avoiding interference from unrelated content |
Three common mistakes
- When entering questions unrelated to the knowledge base, such as casual daily chat, knowledge base retrieval is still triggered and results are returned. The cause is a
similaritythreshold set too low, or not enabling the irrelevant question filtering mechanism, leading to general semantic matching incorrectly recalling non-operations and maintenance content. - Retrieval results prioritize auxiliary data such as log timestamps over core fault descriptions. The cause is not placing core operations and maintenance content in a priority retrieval position during knowledge base chunking, or not configuring content weights during retrieval, leading to non-core information being recalled first.
- When uploading large documents such as server merging plans, small document indexing experiences delays. The cause is not configuring priority rules for chunked uploads. Large document indexing tasks occupy too many system resources, causing small documents to queue for processing.
How to confirm configurations are set correctly
- Check server environment variable configurations. Confirm that
AIPROXY_API_ENDPOINT(root address of the AI Proxy service) is the AI Proxy root address with no additional path suffix, and thatAIPROXY_API_TOKEN(AI Proxy administrator token) matches the AI Proxy server configuration and is not empty. - Initiate a test retrieval. Enter an operations and maintenance-related question such as "server launch capacity estimation method", and verify that returned results only come from the specified knowledge base collection.
- Upload test documents with significant size differences, and confirm that small documents complete indexing first, and that the chunked upload process for large documents operates normally.
- Enter a test question unrelated to operations and maintenance, and verify that retrieval results do not return knowledge base content, matching the expected filtering logic.
The configuration values listed above are common starting points. Adjust them based on your own specific samples and testing.
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.