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VECTOR_SEARCH.md

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Vector Search Setup

Installation on DigitalOcean VPS

1. Install Ollama

curl -fsSL https://ollama.com/install.sh | sh

2. Pull the embedding model

ollama pull nomic-embed-text

This downloads ~274MB. The model will automatically start when needed.

3. Verify Ollama is running

curl http://localhost:11434/api/embeddings -d '{
  "model": "nomic-embed-text",
  "prompt": "test"
}'

Should return a JSON with an "embedding" array of 768 floats.

4. Restart your mu application

./mu

The app will now automatically:

  • Generate embeddings for all indexed content (news, tickers, videos)
  • Use semantic vector search for queries
  • Fallback to keyword search if Ollama is unavailable

How it works

  • Indexing: When news/tickers are indexed, embeddings are generated automatically
  • Search: Queries are embedded and compared using cosine similarity
  • Performance: ~50-100ms per embedding on 1-2 CPU cores
  • Fallback: If Ollama is down, keyword search is used automatically

Testing

Try asking:

  • "what's the bitcoin price" → should find BTC ticker
  • "ethereum value" → should find ETH ticker
  • "crypto markets" → should find crypto-related news
  • "digital gold" → should find Bitcoin content

Memory usage

  • Ollama idle: ~100MB
  • During embedding: +400MB temporarily
  • Index with embeddings: ~4KB per entry