Semantic Search
How it works
Every thought in your graph is converted to a 768-dimensional embedding vector using nomic-embed-text-v1.5. This model understands meaning — not just keywords.
When you search, your query is also embedded, and the server finds thoughts with the highest cosine similarity to your query vector.
Example
You search for: “that idea about making AI tools work together”
This finds thoughts about:
- Cross-vendor AI coordination protocols
- MCP server architecture
- Plugin distribution strategies
Even though none of those thoughts contain the words “making AI tools work together.”
Scores
Results include a distance score from 0 to 1:
| Score | Meaning |
|---|---|
| 0.7+ | Strong match — very relevant |
| 0.5-0.7 | Related — worth reviewing |
| < 0.5 | Weak — probably not what you want |
When to use semantic vs keyword
| Scenario | Use |
|---|---|
| You know the exact term | arx_search (keyword) |
| You remember the concept, not the words | arx_similar (semantic) |
| Looking for a specific tag like [DECISION] | arx_search (keyword) |
| Exploring related ideas broadly | arx_similar (semantic) |
Auto-association
When a new thought is added, the server automatically runs a semantic search against existing thoughts. If any match with cosine similarity > 0.5, relates_to edges are created automatically. This is Hebbian auto-association — thoughts that fire together, wire together.