memory-search

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Memory Search (SOTA)

内存检索（SOTA）

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

针对Ruflo内存的当前最优语义检索，支持多种检索策略。

Strategy Selection

策略选择

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)

根据查询类型选择：

默认（密集型）：快速单跳语义匹配
--hybrid：稀疏+密集结合RRF融合（针对关键词+语义查询提升20-49%效果）
--graph-rag：多跳知识检索（针对推理查询提升30-60%效果）

Steps

步骤

Parse query and flags — extract search text and strategy flags from arguments

Select retrieval strategy:

Dense search (default):

bash

npx @claude-flow/cli@latest memory search --query "QUERY" --namespace NAMESPACE --limit 10

Or via MCP:

mcp__claude-flow__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__claude-flow__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__claude-flow__memory_search_unified({ query: "QUERY", limit: 10 })

Apply MMR reranking — for diverse results, filter near-duplicates (cosine > 0.92) while maximizing relevance
Apply recency weighting — boost recent entries with exponential decay (0.95/day)

Synthesize context (for complex queries):

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

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

解析查询与标志 — 从参数中提取搜索文本和策略标志
选择检索策略:

密集检索（默认）:
bash
```
npx @claude-flow/cli@latest memory search --query "QUERY" --namespace NAMESPACE --limit 10
```
或通过MCP调用：
```
mcp__claude-flow__memory_search({ query: "QUERY", namespace: "NAMESPACE", limit: 10 })
```
混合检索（当使用--hybrid标志或查询包含特定关键词时）：
bash
```
npx ruvector search "QUERY" --hybrid --limit 10
```
Graph RAG检索（当使用--graph-rag标志或需要多跳推理时）：
bash
```
npx ruvector search "QUERY" --graph-rag --limit 10
```
智能检索（当使用--smart标志或需要复杂召回时）：
bash
```
npx @claude-flow/cli@latest memory search --query "QUERY" --smart --limit 10
```
或通过MCP调用：
```
mcp__claude-flow__memory_search({ query: "QUERY", smart: true, limit: 10 })
```
应用五阶段流程：查询扩展、RRF融合、时效性提升、MMR多样性优化、会话轮询。最适用于：多会话召回、时序查询、多样化结果集。
统一跨命名空间检索:
```
mcp__claude-flow__memory_search_unified({ query: "QUERY", limit: 10 })
```
应用MMR重排序 — 为获取多样化结果，在最大化相关性的同时过滤近似重复项（余弦相似度>0.92）
应用时效性加权 — 通过指数衰减（每日0.95）提升近期条目的权重

合成上下文（针对复杂查询）：

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

展示结果 — 按综合得分（相关性多样性时效性）排序，并标注来源命名空间

Namespace Guide

命名空间指南

Namespace	Best 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

命名空间	适用场景
`patterns`	“我们是如何处理X的？”
`tasks`	“Y的上下文是什么？”
`solutions`	“我们是如何修复Z的？”
`feedback`	“用户偏好什么？”
`security`	“已知的……漏洞”
（省略）	搜索所有命名空间