What This Category of Data Looks Like
Typical daily documents for game design and numerical tuning include gameplay design docs, numerical tables, version requirement sheets, and balance test records. Data sources include player behavior data, public competitor materials, battle logs, and version feedback. Data updates follow a weekly or per-version cadence. Document structures vary significantly: numerical tables are structured tables with fields such as damage coefficients and growth curve parameters; gameplay design docs are unstructured long texts containing rule descriptions and process explanations.
What Constraints Do These Characteristics Impose on Reranking and Hybrid Retrieval
The mixed structure of documents requires retrieval to cover both structured numerical fields and unstructured text content. This creates constraints for hybrid retrieval recall logic. Data updates occur at a relatively high frequency, so retrieval logic must support incremental updates to avoid recalling outdated version content. Ticket questions related to numerical tuning often require precise field matching. This sets high requirements for the similarity threshold in reranking, to filter out low-relevance competitor materials and historical version documents. The real-time response needs of on-duty operations set clear limits on the overall latency of retrieval and reranking, to avoid impacting ticket processing efficiency.
How to Configure Settings
This configuration guide uses FastGPT v4.17.0.
| Configuration Item | Recommended Setting | Rationale |
|---|---|---|
usingReRank | Enabled | Operations tickets for numerical tuning often require matching precise numerical table entries and rule descriptions. Enabling reranking filters out low-relevance historical version documents and non-core design content |
similarity | 0.70–0.82 | Differentiates between precise numerical field matching and broad rule description matching. It avoids recalling irrelevant competitor analysis content and adapts to the precise retrieval needs of numerical tuning scenarios |
limit | Top 6–10 results | Controls the number of returned retrieval results. It adapts to the rapid response needs of on-duty operations and covers core numerical adjustments and gameplay rule scenarios for the current version |
searchMode | Hybrid Retrieval | Recalls both structured numerical tables and version requirement sheets, as well as unstructured battle logs and balance records. This matches mixed ticket requirements involving both numerical calculations and rule explanations |
Three Common Misconfiguration Issues
- Phenomenon: GPU video memory rapidly increases to 6–7 GB after calling the reranking model, and multiple cards are occupied simultaneously. Cause: No video memory limit is configured for the reranking model to meet the lightweight needs of the game design scenario, or a large-sized reranker model adapted for general scenarios is used.
- Phenomenon: Hybrid retrieval response time exceeds 10 seconds, exceeding the conventional response threshold for on-duty operations. Cause: No limit is set on the number of recalled entries or the reranking processing scope, and multi-source full recall and reranking are enabled simultaneously, leading to excessive overall latency.
- Phenomenon: A rerank error prompt appears when connecting the reranking model offline. Cause: The chunking rules for game design documents are not adapted, and chunking of long-text numerical tables or design docs is improper, leading to format errors during model invocation.
How to Verify Successful Configuration
- Retrieval result lists can be reviewed to confirm inclusion of both structured numerical table entries, version requirement sheets, and unstructured battle logs and balance records, to verify whether the hybrid retrieval recall range meets expectations.
- Test tickets related to numerical adjustments can be submitted, and retrieval result sorting can be checked to confirm prioritization of core numerical fields for the current version, to verify that the reranking function is active.
- The value range of the
similarityparameter can be adjusted, changes in retrieval result relevance can be observed, and normal activation of the parameter configuration can be confirmed. - Reranking model running logs can be reviewed to confirm absence of abnormal video memory usage growth, to verify the rationality of the model configuration.
The values given here 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.