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Lake Of Vectors

БесплатноНе проверен

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

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Описание

Enables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.

README

Local semantic search over your personal knowledge bases. Index Obsidian notes, SQLite databases, plaintext, and Notion/Confluence (not supported yet) files into ChromaDB, exposed to Claude via an MCP server.

Features

  • Multiple Publishers: Crawl Markdown directories, SQLite tables, and plaintext files
  • Semantic Search: Find relevant content by meaning, not just keywords
  • Local Vectors: Runs entirely on your machine with sentence-transformers
  • OpenAI Support: Optional OpenAI embeddings if preferred
  • MCP Server: Integrates directly with Claude Code for seamless querying

Installation

uv pip install -e .

Configuration

Copy the example config and adjust paths for your sources:

mkdir -p ~/.config/lake-of-vectors
cp config.example.yaml ~/.config/lake-of-vectors/config.yaml

Edit ~/.config/lake-of-vectors/config.yaml:

sources:
  - type: markdown
    name: my-notes
    path: ~/obsidian-vault/Notes/

  - type: sqlite
    name: knowledge-db
    path: ~/knowledge.db
    table: notes
    content_column: body
    metadata_columns: [title, tags]

  - type: plaintext
    name: misc-notes
    path: ~/notes/

embedding:
  backend: local
  model: all-MiniLM-L6-v2

Source Types

Type Description
markdown Recursive crawl of .md files in a directory
sqlite Query a SQLite table with content and metadata columns
plaintext Recursive crawl of .txt files in a directory

Embedding Backends

  • Local (default): Uses all-MiniLM-L6-v2 via sentence-transformers. No API key needed.
  • OpenAI: Uses text-embedding-3-small or another OpenAI model. Requires api_key in config.
embedding:
  backend: openai
  model: text-embedding-3-small
  api_key: sk-...

Usage

Sync your sources

lake sync                 # Sync all sources
lake sync --source my-notes    # Sync only one source
lake sync --rebuild      # Delete and re-embed everything

Check status

lake status

Start the MCP server

lake serve

Claude Code Integration

Register as a global MCP server using the Claude Code CLI:

claude mcp add -s user lake-of-vectors $(pwd)/.venv/bin/lake serve

The -s user scope makes it available in all sessions. Restart Claude Code after running.

Qwen Code Integration

Register as a global MCP server using the Qwen Code CLI:

qwen mcp add -s user lake-of-vectors $(pwd)/.venv/bin/lake serve

The -s user scope makes it available in all sessions. Restart Qwen Code after running.

To scope it to a single project instead, add a .mcp.json file in the project root:

{
  "mcpServers": {
    "lake-of-vectors": {
      "command": "lake",
      "args": ["serve"]
    }
  }
}

Add to your CLAUDE.md:

When answering security questions or searching your personal knowledge, always use lake-of-vectors semantic_search first.

CLI Commands

Command Description
lake sync Sync all configured sources into ChromaDB
lake sync --source <name> Sync a specific source
lake sync --rebuild Delete all vectors and re-sync everything
lake prune Remove stale collections not in current config
lake prune --dry-run Preview what would be pruned without deleting
lake serve Start the MCP server (stdio mode)
lake status Show sync status for all sources

Data

ChromaDB vectors are stored at:

~/.local/share/lake-of-vectors/chromadb

Architecture

  ┌──────────────────────────────────────────────────────────────────┐
  │                      lake-of-vectors                             │                                                                   
  │                                                                  │
  │  ┌─────────────┐   ┌──────────────────┐   ┌──────────────────┐   │                                                                    
  │  │  Publishers │──▶│   Sync Engine    │──▶│    ChromaDB      │   │                                                                    
  │  │             │   │                  │   │    (on-disk)     │   │                                                                    
  │  │ • Markdown  │   │ • Chunking       │   │                  │   │                                                                    
  │  │ • SQLite    │   │ • Content hash   │   │ one collection   │   │                                                                    
  │  │ • Plaintext │   │   diffing        │   │ per source       │   │                                                                    
  │  └─────────────┘   │ • Embedding      │   └────────┬─────────┘   │                                                                    
  │                    │ • ChromaDB upsert│            │             │                                                                    
  │                    └──────────────────┘            │             │                                                                    
  │                                                    │             │                                                                  
  │  ┌─────────────────────┐   ┌──────────────────┐    │             │                                                                     
  │  │  Embedding Backends │   │   MCP Server     │◀───┘             │                                                                     
  │  │                     │   │   (stdio)        │                  │                                                                     
  │  │ • Local             │   │                  │                  │                                                                     
  │  │   (sentence-        │   │ • semantic_search│                  │                                                                     
  │  │    transformers)    │   │ • list_sources   │                  │                                                                   
  │  │ • OpenAI API        │   └──────────────────┘                  │                                                                     
  │  └─────────────────────┘                                         │                                                                  
  │                                                                  │                                                                   
  │  CLI: lake sync | lake serve | lake prune | lake status          │                                                                 
  └──────────────────────────────────────────────────────────────────┘  

License

MIT

from github.com/tungpun/lake-of-vectors

Установка Lake Of Vectors

У этого сервера нет опубликованного пакета — он собирается из исходников. Открой репозиторий и следуй инструкции в README.

▸ github.com/tungpun/lake-of-vectors

FAQ

Lake Of Vectors MCP бесплатный?

Да, Lake Of Vectors MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Lake Of Vectors?

Нет, Lake Of Vectors работает без API-ключей и переменных окружения.

Lake Of Vectors — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Lake Of Vectors в Claude Desktop, Claude Code или Cursor?

Открой Lake Of Vectors на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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