Tana-Embeddings-Public

A high-performance semantic search engine for Tana that syncs nodes to a local vector store for instant, conceptual retrieval. Built to bypass the limitations of keyword-only search by providing deep, semantic understanding of your knowledge graph.

• Local Semantic Sync: Automatically pulls nodes from your Tana workspace and indexes them into a local vector store.
• FastEmbed BGE Model: Utilises the BAAI/bge-small-en-v1.5 model for high-quality embeddings without requiring an external API (runs entirely on your machine).
• LanceDB Vector Store: Uses the high-performance LanceDB for serverless, disk-based vector storage.
• Hybrid Search Logic: Designed to work alongside Tana's native keyword search for the ultimate "Second Brain" retrieval experience.
• Background Automation: Includes scripts for scheduled syncs via launchd or cron.

• Python 3.10+
• LanceDB: Disk-persistent vector database.
• FastEmbed: Lightweight CPU-optimised embedding generation.
• Tana MCP: Interfaces with Tana via the Model Context Protocol.

• A Tana account and an active Tana Token.
• Python installed on your system.
• Tana Desktop app (recommended for local MCP bridge).

1. Clone the repository:

   git clone https://github.com/krshirkoohi/Tana-Embeddings-Public.git
   cd Tana-Embeddings-Public

1. Install dependencies:

   pip install -r requirements.txt

1. Configure Environment:

Create a .env file in the root directory (this is ignored by Git):

   TANA_TOKEN=your_tana_token_here

To pull your latest Tana nodes and update the local vector store:

python3 sync_tana.py

To search your Tana workspace conceptually:

python3 search_tana.py "How do I handle project manage