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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 management.

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Repository
a5c-ai/babysitter
Last source activity
June 1, 2026 at 07:46
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English
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name
vector-memory
description
HNSW vector search for pattern similarity retrieval and knowledge graph maintenance with PageRank scoring, community detection, and 3-tier memory management.
allowed-tools
Read, Write, Edit, Bash, Grep, Glob, WebFetch, WebSearch, Agent, AskUserQuestion
graph
{"domains":["domain:software-engineering"],"skillAreas":["skill-area:agentic-loops","skill-area:orchestration-loop"],"workflows":["workflow:feature-development"],"topics":["topic:developer-experience"],"roles":["role:tech-lead","role:backend-engineer"]}
- 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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