What this category of data looks like
This category of data comes from official government service platforms, copyright registration systems, game package files, and operating institution materials. Core documents fall into four categories: declaration request files, game application forms, copyright certificates, and review reports. Data updates align with each game’s individual declaration process, with irregular update cycles. Each document has a fixed structure, including fields such as declaration subject information, game qualification documents, and review conclusions. There is no unified fixed unit. Document length varies widely, from hundreds of characters to tens of thousands of characters.
Constraints for Reranking and Hybrid Retrieval
Scattered data sources lead to varying embedding quality across different documents. Use a reranking model to filter low-relevance recall results, ensuring accuracy for on-duty operations and ticket Q&A. The wide range of document lengths requires controlling the total context length of recall results during hybrid retrieval, to avoid exceeding model processing limits. Irregular updates require retrieval logic to dynamically adapt to new declaration materials, while maintaining retrieval accuracy during material consistency checks. Core fields focus on qualification and review content. Prioritize these high-weight fields during reranking, to avoid irrelevant recall interfering with business workflows.
Configuration Settings
| Configuration Item | Recommended Approach | Rationale |
|---|---|---|
usingReRank | Enabled, use FastGPT v4.17.0 built-in reranking model | This category has concentrated document fields. The built-in model quickly filters low-relevance recall results, meeting fast response needs for material checks and ticket Q&A |
similarity | 0.72–0.85 | Core qualification and review fields require high association. This range filters cross-batch irrelevant historical declaration documents, avoiding confusion between different project materials during ticket Q&A |
limit | Top 8–12 entries | Individual declaration documents have wide length variation. Limiting recall entries controls total context length, adapting to real-time response needs of on-duty operations |
searchMode | Hybrid retrieval mode | Data sources for this category are scattered. Hybrid retrieval recalls both structured qualification documents and unstructured review reports, covering all types of business documents |
Three Common Misconfigurations
- Issue: Retrieval API call times out after reranking, with response time exceeding 60 seconds. Cause: No limit on the number of recall entries. The reranking model processes too many candidate results, exceeding the response threshold for on-duty operations scenarios.
- Issue: Reranking model video memory usage rises continuously until overflow, unable to process subsequent retrieval requests. Cause: No automatic video memory release trigger logic configured, and no limit on the number of candidate documents processed per batch. This is a common video memory congestion issue in this business.
- Issue: Retrieval results include large numbers of historical documents unrelated to the current declaration. Cause: Similarity threshold set too low, failing to filter cross-project irrelevant declaration materials, reducing ticket Q&A accuracy.
How to Verify Correct Configuration
- Upload a single game application form and review report, initiate a retrieval test, confirm returned results prioritize core qualification fields and review conclusion content.
- Check the system monitoring dashboard, confirm the reranking model’s peak video memory usage does not exceed 1000 MB, with no continuous upward trend.
- Initiate more than 3 repeated retrieval requests, confirm consistency of returned results, verify no abnormal configuration fluctuations over time.
- Adjust the similarity threshold range, verify the number of returned results changes reasonably with threshold adjustments.
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.