🔍Search Tool
semantic_search
Vector similarity search across all conversations using local embeddings. Find by concept, not keyword.
When to use this
Use semantic_search when you remember a concept but not the exact words. “That thing about chunking strategies for embeddings” will find relevant conversations even if you never used those exact words.
Quick Example
mcporter call brain.semantic_search query="bottleneck as amplifier"Response
## Semantic Search: "bottleneck as amplifier"
Found 10 results (similarity > 0.65):
1. [0.89] "Bottleneck Thesis Deep Dive" (2024-08)
"The bottleneck isn't the constraint — it's the
amplifier. Everything that passes through it gets
compressed into its most essential form..."
ID: abc-123...
2. [0.82] "SEED Principle: Compression" (2024-09)
"Compression through constraint. The narrower the
channel, the more signal gets concentrated..."
ID: def-456...
3. [0.78] "Monotropism as Cognitive Architecture" (2024-10)
"Single-channel processing isn't a bug — it's a
feature that amplifies depth at the cost of breadth..."
ID: ghi-789...Parameters
| Name | Type | Required | Default | Description |
|---|---|---|---|---|
query | string | Yes | — | Natural language search query — conceptual, not just keywords |
limit | integer | No | 10 | Maximum number of results to return |
How It Works
- Your query is embedded locally using
nomic-embed-text-v1.5(768 dimensions) - The embedding is compared against 82K+ pre-computed message vectors in LanceDB
- Results are ranked by cosine similarity and returned with context
All processing happens on your machine — no API calls, no data leaving your device.
Examples
Conceptual search
mcporter call brain.semantic_search query="tradeoffs between consistency and availability"Technical search
mcporter call brain.semantic_search query="embedding chunking strategies for long documents"More results
mcporter call brain.semantic_search query="auth patterns" limit=20semantic_search vs search_conversations
| semantic_search | search_conversations | |
|---|---|---|
| Method | Vector similarity | Keyword matching (ILIKE) |
| Best for | Concepts, ideas, fuzzy recall | Exact terms, names, code |
| Searches | Embedded user messages only | All messages (user + assistant) |