Command Palette

Search for a command to run...

UnylyUnyly
Browse all

Lake Of Vectors

FreeNot checked

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

GitHubEmbed

About

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

Install Lake Of Vectors in Claude Desktop, Claude Code & Cursor

Recommended · one command, every IDE
unyly install lake-of-vectors

Installs into Claude Desktop, Claude Code, Cursor & VS Code — handles npx, uvx and build-from-source repos for you.

First time? Get the CLI: curl -fsSL https://unyly.org/install | sh

Or configure manually

Run in your terminal:

claude mcp add lake-of-vectors -- uvx --from git+https://github.com/tungpun/lake-of-vectors lake-of-vectors

FAQ

Is Lake Of Vectors MCP free?

Yes, Lake Of Vectors MCP is free — one-click install via Unyly at no cost.

Does Lake Of Vectors need an API key?

No, Lake Of Vectors runs without API keys or environment variables.

Is Lake Of Vectors hosted or self-hosted?

Self-hosted: the server runs locally on your machine via the install command above.

How do I install Lake Of Vectors in Claude Desktop, Claude Code or Cursor?

Open Lake Of Vectors on unyly.org, pick your client tab (Claude Desktop, Claude Code, Cursor) and press Install — the config is generated automatically, no JSON editing.

Related MCPs

Compare Lake Of Vectors with

Not sure what to pick?

Find your stack in 60 seconds

Author?

Embed badge for your README

Browse similar

All data MCPs