dosubot[bot]

Yes, you can run DeepDoc on GPU starting with RAGFlow v0.22.0. GPU acceleration is supported for OCR and layout analysis using ONNX Runtime with CUDAExecutionProvider, and DeepDoc will automatically detect...

I'm a bot trained on Zod's codebase and issues. Issue #5078 wasn't found, but I did review related threads and the codebase. The behavior you're seeing is intentional in Zod...

Setting `ctx.jsonSchema.id = undefined` doesn't actually remove the property from the output—it just sets its value to `undefined`, so it still appears in the JSON Schema. To remove the `id`...

Hi, @reteps. I'm [Dosu](https://dosu.dev), and I'm helping the Zod team manage their backlog and am marking this issue as stale. **Issue Summary:** - You reported that the override function in...

**Flake triage report for periodic-kubevirt-e2e-k8s-1.34-sig-compute** The failures in this lane are consistent with infrastructure instability in the k8s-1.34 provider. The main symptoms are: (1) control-plane components not ready, with connection...

Hi, @Charlieyurong. I'm [Dosu](https://dosu.dev), and I'm helping the Dify team manage their backlog. I'm marking this issue as stale. **Issue Summary:** - You suggested adding a manual 'break' feature to...

Hi @nurbolnygmanov! I'm [Dosu](https://go.dosu.dev/dosubot) and I’m helping the valibot team. Valibot's `v.check` is most similar to Zod's `superRefine`: both are only triggered if all field-level validations pass, so they won't...

The unexpected "video relay" warnings after the TURN server migration are likely due to how TURN credentials are managed. TURN credentials are generated with a fixed 4-hour validity and are...

你反馈的 1.5.0 版本 Gremlin 查询比 1.0.0 慢 20 倍，结合代码和社区资料，主要可能原因有： 1. **后端配置和集群模式影响**：1.5.0 默认推荐集群（hstore）模式，哪怕单机部署也会引入分布式调度、锁和网络通信等额外开销，实际测试中多层查询性能下降 10~20 倍是已知现象（如[这里](https://github.com/apache/incubator-hugegraph/issues/2709)）。建议优先确认是否为集群模式，单机建议用单节点模式或优化集群配置。 2. **JVM 和缓存参数**：1.5.0 建议 JVM 堆内存至少 16GB，生产建议 32GB（-Xmx32g -Xms32g），并调大 vertex/edge cache 容量和过期时间，避免频繁访问后端存储，提升查询性能（[参考](https://github.com/apache/incubator-hugegraph/issues/2836)）。 3. **索引覆盖与查询优化**：确保所有 has/hasLabel 等查询条件都被二级索引或范围索引覆盖，避免全表扫描。复杂多层...

你的查询在 HugeGraph 1.5.0 下性能大幅下降，主要瓶颈集中在多层 HugeVertexStep、TraversalFilterStep、AndStep 等遍历和过滤环节。代码实现上，这些步骤会先构造后端查询（如 GraphTransaction.constructEdgesQuery），再用 FilterIterator 在内存中做二次过滤，复杂条件和多层 inE/outE 会导致大量数据在 JVM 内部处理，极易拖慢查询，尤其是在 50 亿点/边的大规模数据下，分布式锁、session 管理和后端通信开销也显著增加[代码分析](https://github.com/apache/incubator-hugegraph/blob/a53af864b31f803780a1db31cb20d3d94d5d34fc/hugegraph-server/hugegraph-core/src/main/java/org/apache/hugegraph/traversal/algorithm/HugeTraverser.java)。 优化建议： - JVM 堆建议 32GB 以上，memory.mode 设为 off-heap，vertex/edge cache_capacity、cache_expire 调大，避免频繁后端 IO，提升缓存命中率[配置建议](https://app.dosu.dev/documents/7efdc24e-edec-4811-afdd-d39a113f3dfe)。 - 检查所有 where/has/filter...