Skills Artificial Intelligence SOTA Semantic Knowledge Retrieval System

SOTA Semantic Knowledge Retrieval System

v20260716
memory-search
A state-of-the-art memory search engine providing multi-faceted retrieval strategies. It supports default dense semantic matching, advanced hybrid search (sparse + dense with RRF), deep multi-hop Graph RAG for complex reasoning, and 'Smart' search utilizing query expansion, recency boosting, and MMR diversity reranking. Ideal for retrieving nuanced, diverse, and time-sensitive information from large, complex knowledge bases across multiple namespaces.
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Overview

Memory Search (SOTA)

State-of-the-art semantic search across Ruflo memory with multiple retrieval strategies.

Strategy Selection

Choose based on query type:

  • Default (dense): fast single-hop semantic match
  • --hybrid: sparse + dense with RRF fusion (20-49% better for keyword+semantic queries)
  • --graph-rag: multi-hop knowledge retrieval (30-60% better for reasoning queries)

Steps

  1. Parse query and flags — extract search text and strategy flags from arguments
  2. Select retrieval strategy:

Dense search (default):
```bash
npx @claude-flow/cli@latest memory search --query "QUERY" --namespace NAMESPACE --limit 10
```
Or via MCP: mcp__plugin_ruflo-core_ruflo__memory_search({ query: "QUERY", namespace: "NAMESPACE", limit: 10 })

Hybrid search (when --hybrid or query has specific keywords):
```bash
npx ruvector search "QUERY" --hybrid --limit 10
```

Graph RAG (when --graph-rag or multi-hop reasoning needed):
```bash
npx ruvector search "QUERY" --graph-rag --limit 10
```

Smart retrieval (when --smart or complex recall needed):
```bash
npx @claude-flow/cli@latest memory search --query "QUERY" --smart --limit 10
```
Or via MCP: mcp__plugin_ruflo-core_ruflo__memory_search({ query: "QUERY", smart: true, limit: 10 })

Applies 5-phase pipeline: query expansion, RRF fusion, recency boost, MMR diversity, session round-robin.
Best for: multi-session recall, temporal queries, diverse result sets.

Unified cross-namespace:
mcp__plugin_ruflo-core_ruflo__memory_search_unified({ query: "QUERY", limit: 10 })

  1. Apply MMR reranking — for diverse results, filter near-duplicates (cosine > 0.92) while maximizing relevance
  2. Apply recency weighting — boost recent entries with exponential decay (0.95/day)
  3. Synthesize context (for complex queries):

mcp__plugin_ruflo-core_ruflo__agentdb_context-synthesize({ query: "QUERY", sources: ["patterns", "tasks", "solutions"] })

  1. Present results — ranked by composite score (relevance diversity recency), with source namespace attribution

Namespace Guide

NamespaceBest For
patterns"How did we handle X?"
tasks"What was the context for Y?"
solutions"How did we fix Z?"
feedback"What did the user prefer?"
security"Known vulnerabilities in..."
(omit)Search all namespaces
Info
Name memory-search
Version v20260716
Size 2.98KB
Updated At 2026-07-18
Language