Lake Of Vectors
БесплатноНе проверенEnables semantic search over local knowledge bases like Obsidian notes, SQLite, and plaintext files, exposing results to Claude via MCP server.
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-v2via sentence-transformers. No API key needed. - OpenAI: Uses
text-embedding-3-smallor another OpenAI model. Requiresapi_keyin 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
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-vectorsFAQ
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.
Похожие MCP
wenb1n-dev/SmartDB_MCP
A universal database MCP server supporting simultaneous connections to multiple databases. It provides tools for database operations, health analysis, SQL optim
автор: wenb1n-devPostgres Server
This server enables interaction with PostgreSQL databases through the Model Context Protocol, optimized for the AWS Bedrock AgentCore Runtime. It provides tools
автор: madhurprashPostgres
Query your database in natural language
автор: AnthropicPostgreSQL
Read-only database access with schema inspection.
автор: modelcontextprotocolCompare Lake Of Vectors with
Не уверен что выбрать?
Найди свой стек за 60 секунд
Автор?
Embed-бейдж для README
Похожее
Все в категории data
