Semantic Search

Semantic search finds content based on meaning, not just matching words. When you search for “productivity techniques”, it also finds notes about “getting things done”, “focus methods”, and “time management” - even if they don’t contain your exact words.

How It Works

Indexing: When you run cru process, Crucible reads each note and creates an “embedding” - a numerical representation of its meaning
Searching: When you search, your query is also converted to an embedding
Matching: Crucible finds notes whose embeddings are closest to your query’s embedding

This happens at the block level - each paragraph, heading, or list is indexed separately. This means searches return specific sections, not just whole files.

Using Semantic Search

# Find content similar to your query
cru semantic "how do I stay focused while working?"

# Limit results
cru semantic "project planning" --limit 5

When to Use It

Semantic search works best for:

Exploratory queries (“notes about creativity”)
Finding connections you forgot existed
Questions in natural language

Text search (cru search) works best for:

Exact phrases (“meeting notes 2024”)
Known keywords (“TODO”, “FIXME”)
Specific names or terms

Block-Level Precision

Because Crucible indexes at block level, searches return the specific paragraph that matches, not just the file. This is especially useful in long notes.

Configuration

Semantic search requires an embedding provider. See embedding for setup options including:

Local (Ollama, FastEmbed)
Cloud (OpenAI)