tools-only 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"