Data Profile for This Category
Daily documents for game design and numerical design include gameplay design documents, numerical tables, version requirement sheets, and balance test records. Data sources primarily include player behavior data, public competitor materials, battle logs, and version feedback. Document update frequency follows weekly or per-version cycles. Common fields include rule trigger conditions, damage coefficients, and retention target values. Involved units include multipliers, time intervals, and percentage intervals. Significant structural differences exist across document types: numerical tables use structured table formats, design documents use unstructured text formats, and version requirement sheets mix text descriptions and quantitative indicators.
Constraints on Reranking and Hybrid Retrieval for This Category
Documents in this category have large structural differences and high update frequencies. Reranking and hybrid retrieval must support mixed matching across multiple data types. Structured numerical tables have strict field precision requirements. Hybrid retrieval must balance semantic matching and precise field matching to prevent irrelevant recalls of version feedback or design fragments. Weekly or per-version update cycles require retrieval logic to quickly adapt to newly uploaded document chunks. It is also necessary to control the context length of retrieval results to avoid exceeding model processing limits. Pre-sales Q&A scenarios require rapid recall of relevant rules and historical version data. This creates clear requirements for retrieval recall range and sorting weight. Results related to the current query, such as numerical rules or gameplay descriptions, must be prioritized.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
| Reranking Toggle | Enabled | This category needs to filter low-match numerical table and design document fragments. Reranking improves result relevance |
| Similarity Threshold | 0.72–0.85 (calibrated via actual testing) | Numerical field matching has high precision requirements. The threshold must be higher than general scenarios to avoid irrelevant recalls |
| Single Recall Limit | Top 10 entries | Balances context length and coverage of key information, adapts to document scales updated weekly or per version |
| Retrieval Mode | Hybrid Retrieval | Balances semantic matching and structured field matching, adapts to design document text and numerical table data |
| Reranking Model | Lightweight locally deployed model | Data for this category requires internal network processing. Local deployment ensures data security and stable response |
| Context Length | 8000–12000 token | Accommodates multiple gameplay design and numerical table fragments, meets rule integration needs for pre-sales Q&A |
Three Common Misconfigurations
- Symptom: Reranking model call records do not appear in conversation details, and retrieval result sorting matches the initial recall results. Cause: The reranking toggle was not correctly enabled in the FastGPT v4.17.0 configuration file, or the hybrid retrieval mode was not bound.
- Symptom: After reranking is configured, retrieval results only return a single highest-similarity entry. Cause: The number of returned entries after reranking was incorrectly set to 1, or the reranking model's recall count parameter was not correctly configured.
- Symptom: The reranking model is reachable via ping on the internal network, but reranking logic is not triggered in conversations. Cause: The reranking model path in the configuration file was filled incorrectly, or the knowledge base was not linked to the reranking configuration item.
How to Verify Successful Configuration
- Access the knowledge base configuration page for FastGPT v4.17.0, and verify whether the reranking toggle is enabled.
- Submit a test query involving numerical rules or gameplay descriptions, and check whether the sorting of retrieval results differs from the initial recall results.
- Export conversation detail logs, and check whether the logs include reranking model call records and returned results.
- Adjust the similarity threshold, and verify that the number of retrieval results changes in line with threshold adjustments.
The values listed here are common starting points. They should be evaluated against their own specific 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.