Update nexus: fix conflicts and sync local changes
This commit is contained in:
Shen Wei
@@ -1,46 +1,46 @@
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---
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title: "Semantic Memory Search"
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type: source
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tags: [memory, semantic-search, vector-db, openclaw]
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date: 2026-04-22
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---
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## Source File
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- [[Agent/usecases/semantic-memory-search]]
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## Summary（用中文描述）
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- 核心主题：为 OpenClaw 的 Markdown 记忆文件添加向量语义搜索能力
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- 问题域：OpenClaw 记忆以纯 Markdown 存储，随时间积累后无法检索，grep 只能关键词匹配，无法语义理解
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- 方法/机制：使用 memsearch 库（Milvus 向量数据库）构建混合搜索（稠密向量 + BM25）配合 RRF 重排；SHA-256 内容哈希实现增量索引；文件监视器自动重建索引
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- 结论/价值：用自然语言提问（如"我们选了哪个缓存方案？"）即可找到相关内容，无需记忆精确措辞；支持本地模式无需 API Key
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## Key Claims（用中文描述）
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- OpenClaw 记忆库积累后，纯 Markdown 无法语义检索，用户需要通过含义而非关键词找到过去决策
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- 混合搜索（稠密向量 + BM25）结合 RRF 重排，同时捕获语义相似性和关键词精确匹配，优于纯向量搜索
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- SHA-256 内容哈希确保仅新内容或变更内容被嵌入，避免重复 API 调用，节省成本
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- Markdown 文件是唯一真相，向量索引只是派生缓存，随时可通过 `memsearch index` 重建
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## Key Quotes
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> "Markdown stays the source of truth. The vector index is just a derived cache — you can rebuild it anytime with `memsearch index`. Your memory files are never modified." — 核心理念：原始文档不可变
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> "Hybrid search beats pure vector search. Combining semantic similarity (dense vectors) with keyword matching (BM25) via Reciprocal Rank Fusion catches both meaning-based and exact-match queries." — 混合搜索的优越性
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> "Smart dedup saves money. Each chunk is identified by a SHA-256 content hash. Re-running `index` only embeds new or changed content, so you can run it as often as you like without wasting embedding API calls." — 增量索引节省成本
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## Key Concepts
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- [[Semantic Memory Search]]：通过向量嵌入实现对记忆文件的语义搜索，而非仅关键词匹配
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- [[Hybrid Search]]：结合稠密向量（语义相似性）和 BM25（关键词精确匹配）的混合检索策略
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- [[Reciprocal Rank Fusion (RRF)]]：通过排名融合重排合并多个检索结果，提升搜索质量
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- [[Content Hashing]]：使用 SHA-256 哈希识别内容块，仅对新增或变更内容重新嵌入
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- [[File Watcher]]：监视记忆文件变化，自动触发增量重建索引，保持索引实时更新
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## Key Entities
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- [[memsearch]]：ZillizTech 开源的向量语义搜索 CLI/库，为 OpenClaw 记忆提供语义搜索能力，基于 Milvus 向量数据库
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- [[Milvus]]：开源向量数据库后端，memsearch 的向量存储和检索引擎
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- [[OpenClaw]]：多 Agent 框架，自带 Markdown 记忆系统，是本用例的上层应用框架
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## Connections
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- [[OpenClaw]] ← extends ← [[Semantic Memory Search]]：本用例在 OpenClaw 纯 Markdown 记忆之上叠加向量语义搜索层
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- [[Knowledge-Base-RAG]] ← related_to ← [[Semantic Memory Search]]：两者都涉及向量 Embedding 检索，属于 RAG 技术栈的不同场景
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- [[Second Brain]] ← related_to ← [[Semantic Memory Search]]：第二大脑的记忆持久化与语义检索能力相辅相成
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## Contradictions
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- 与 [[Knowledge-Base-RAG]] 无冲突，两者属同一技术栈的不同实现：Knowledge Base RAG 侧重 Telegram/Slack 投递 URL 并入库，本用例侧重现有 Markdown 文件的语义索引
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---
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title: "Semantic Memory Search"
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type: source
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tags: [memory, semantic-search, vector-db, openclaw]
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date: 2026-04-22
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---
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## Source File
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- [[Agent/usecases/semantic-memory-search]]
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## Summary（用中文描述）
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- 核心主题：为 OpenClaw 的 Markdown 记忆文件添加向量语义搜索能力
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- 问题域：OpenClaw 记忆以纯 Markdown 存储，随时间积累后无法检索，grep 只能关键词匹配，无法语义理解
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- 方法/机制：使用 memsearch 库（Milvus 向量数据库）构建混合搜索（稠密向量 + BM25）配合 RRF 重排；SHA-256 内容哈希实现增量索引；文件监视器自动重建索引
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- 结论/价值：用自然语言提问（如"我们选了哪个缓存方案？"）即可找到相关内容，无需记忆精确措辞；支持本地模式无需 API Key
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## Key Claims（用中文描述）
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- OpenClaw 记忆库积累后，纯 Markdown 无法语义检索，用户需要通过含义而非关键词找到过去决策
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- 混合搜索（稠密向量 + BM25）结合 RRF 重排，同时捕获语义相似性和关键词精确匹配，优于纯向量搜索
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- SHA-256 内容哈希确保仅新内容或变更内容被嵌入，避免重复 API 调用，节省成本
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- Markdown 文件是唯一真相，向量索引只是派生缓存，随时可通过 `memsearch index` 重建
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## Key Quotes
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> "Markdown stays the source of truth. The vector index is just a derived cache — you can rebuild it anytime with `memsearch index`. Your memory files are never modified." — 核心理念：原始文档不可变
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> "Hybrid search beats pure vector search. Combining semantic similarity (dense vectors) with keyword matching (BM25) via Reciprocal Rank Fusion catches both meaning-based and exact-match queries." — 混合搜索的优越性
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> "Smart dedup saves money. Each chunk is identified by a SHA-256 content hash. Re-running `index` only embeds new or changed content, so you can run it as often as you like without wasting embedding API calls." — 增量索引节省成本
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## Key Concepts
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- [[Semantic Memory Search]]：通过向量嵌入实现对记忆文件的语义搜索，而非仅关键词匹配
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- [[Hybrid Search]]：结合稠密向量（语义相似性）和 BM25（关键词精确匹配）的混合检索策略
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- [[Reciprocal Rank Fusion (RRF)]]：通过排名融合重排合并多个检索结果，提升搜索质量
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- [[Content Hashing]]：使用 SHA-256 哈希识别内容块，仅对新增或变更内容重新嵌入
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- [[File Watcher]]：监视记忆文件变化，自动触发增量重建索引，保持索引实时更新
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## Key Entities
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- [[memsearch]]：ZillizTech 开源的向量语义搜索 CLI/库，为 OpenClaw 记忆提供语义搜索能力，基于 Milvus 向量数据库
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- [[Milvus]]：开源向量数据库后端，memsearch 的向量存储和检索引擎
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- [[OpenClaw]]：多 Agent 框架，自带 Markdown 记忆系统，是本用例的上层应用框架
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## Connections
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- [[OpenClaw]] ← extends ← [[Semantic Memory Search]]：本用例在 OpenClaw 纯 Markdown 记忆之上叠加向量语义搜索层
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- [[Knowledge-Base-RAG]] ← related_to ← [[Semantic Memory Search]]：两者都涉及向量 Embedding 检索，属于 RAG 技术栈的不同场景
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- [[Second Brain]] ← related_to ← [[Semantic Memory Search]]：第二大脑的记忆持久化与语义检索能力相辅相成
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## Contradictions
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- 与 [[Knowledge-Base-RAG]] 无冲突，两者属同一技术栈的不同实现：Knowledge Base RAG 侧重 Telegram/Slack 投递 URL 并入库，本用例侧重现有 Markdown 文件的语义索引
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