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name: leann-search description: Semantic search across codebase using LEANN vector index allowed-tools: [Bash, Read]

LEANN Semantic Search

Use LEANN for meaning-based code search instead of grep.

When to Use

• Conceptual queries: "how does authentication work", "where are errors handled"
• Understanding patterns: "streaming implementation", "provider architecture"
• Finding related code: code that's semantically similar but uses different terms

When NOT to Use

• Exact matches: Use Grep for class Foo, def bar, specific identifiers
• Regex patterns: Use Grep for error.*handling, import.*from
• File paths: Use Glob for *.test.ts, src/**/*.py

Commands

Copy# Search the current project's index
leann search <index-name> "<query>" --top-k 5

# List available indexes
leann list

# Example
leann search rigg "how do providers handle streaming" --top-k 5

MCP Tool (in Claude Code)

Copyleann_search(index_name="rigg", query="your semantic query", top_k=5)

Rebuilding the Index

When codebase changes significantly:

Copycd /path/to/project
leann build <project-name> --docs src tests scripts \
  --file-types '.ts,.py,.md,.json' \
  --no-recompute --no-compact \
  --embedding-mode sentence-transformers \
  --embedding-model all-MiniLM-L6-v2

How It Works

1. LEANN uses sentence embeddings to understand meaning
2. Searches find conceptually similar code, not just text matches
3. Results ranked by semantic similarity score (0-1)

Grep vs LEANN Decision