playbook/antigravity-awesome-skills/skills/weaviate/references/semantic_search.md

Semantic Search

Pure vector similarity search using embeddings on a single collection.

Usage

uv run scripts/semantic_search.py --query "USER_QUERY" --collection "CollectionName" [--limit 10] [--distance 0.5] [--target-vector "vector_name"] [--json]

Parameters

Parameter	Flag	Required	Default	Description
--query	-q	Yes	—	Search query text
--collection	-c	Yes	—	Collection name
--limit	-l	No	10	Maximum number of results
--distance	-d	No	—	Maximum distance threshold (filters out less similar results)
--target-vector	-t	No	—	Target vector name for named vector collections
--json	—	No	false	Output in JSON format

Output

• Default: Markdown table with object properties and distance scores
• JSON: Array of objects with properties and distance metadata

Examples

Basic semantic search:

uv run scripts/semantic_search.py --query "environmental impact of urbanization" --collection "Research"

With distance threshold:

uv run scripts/semantic_search.py --query "machine learning" --collection "Papers" --distance 0.3 --limit 5

With named vector:

uv run scripts/semantic_search.py --query "abstract art" --collection "Artworks" --target-vector "description_vector"