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vector-memory

HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory ma…

不碰外部(只输出文字)无严重或高危命中a5c-ai/babysitter

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命中总数0 处
命中统计严重 0 · 高 0 · 中 0 · 低 0

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技能内容

  • Building and querying knowledge graphs for project context
  • Managing cross-session memory across project/local/user scopes
  • Fast similarity search for routing decisions

HNSW Performance

  • Search latency: ~61 microseconds
  • Query throughput: ~16,400 QPS
  • Configurable embedding dimensions (default: 128)

Knowledge Graph

  • PageRank: Importance scoring for knowledge nodes
  • Community Detection: Cluster related patterns
  • LRU Cache: Fast access to frequently used patterns
  • SQLite Backing: Persistent cross-session storage

3-Tier Memory

| Scope | Persistence | Content |

|-------|------------|---------|

| Project | Codebase-level | Patterns, architecture decisions, dependencies |

| Local | Session-level | Context, adaptations, temporary patterns |

| User | Cross-project | Preferences, learned behaviors, global patterns |

Agents Used

  • agents/optimizer/ - Memory and cache optimization

Tool Use

Invoke via babysitter process: methodologies/ruflo/ruflo-intelligence

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