vector-memory
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
安装与使用
请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。
查看来源 ↗使用指南技能说明
Vector Memory
Overview
High-performance vector search using HNSW (Hierarchical Navigable Small World) graphs for pattern storage and retrieval, combined with a knowledge graph for relational reasoning.
When to Use
- Retrieving similar patterns from execution history
- 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
使用示例
查看来源仓库中的使用示例与配置说明。
查看来源 ↗来源历史
- 收录时间
- 2026-03-11
- 索引更新时间
- 2026-03-11
- Branch
- main
收录与同步日期属于本站记录。源码修改历史请查看来源仓库。
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