140 lines
4.7 KiB
Python
140 lines
4.7 KiB
Python
#!/usr/bin/env python3
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# /// script
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# dependencies = [
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# "weaviate-client==4.19.2",
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# "typer==0.21.0",
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# ]
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# ///
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"""
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Semantic (vector) search on a Weaviate collection.
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Usage:
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uv run semantic_search.py --query "your query" --collection "CollectionName" [--limit 10] [--json]
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Environment Variables:
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WEAVIATE_URL: Weaviate Cloud cluster URL
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WEAVIATE_API_KEY: API key for authentication
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+ Any provider API keys (OPENAI_API_KEY, COHERE_API_KEY, etc.) - auto-detected
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"""
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import json
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import sys
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import typer
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import weaviate
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from weaviate.classes.query import MetadataQuery
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# Import shared connection utilities (local to this skill)
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from weaviate_conn import get_client
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app = typer.Typer()
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@app.command()
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def main(
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query: str = typer.Option(..., "--query", "-q", help="Search query text"),
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collection: str = typer.Option(..., "--collection", "-c", help="Collection name"),
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limit: int = typer.Option(10, "--limit", "-l", help="Maximum results to return"),
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distance: float = typer.Option(
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None, "--distance", "-d", help="Maximum distance threshold"
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),
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target_vector: str = typer.Option(
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None,
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"--target-vector",
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"-t",
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help="Target vector name for named vector collections",
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),
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json_output: bool = typer.Option(False, "--json", help="Output in JSON format"),
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):
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"""Perform semantic (vector similarity) search on a Weaviate collection."""
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try:
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with get_client() as client:
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if not client.collections.exists(collection):
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print(f"Error: Collection '{collection}' not found.", file=sys.stderr)
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raise typer.Exit(1)
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coll = client.collections.use(collection)
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print("Searching...", file=sys.stderr)
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response = coll.query.near_text(
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query=query,
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limit=limit,
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distance=distance,
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target_vector=target_vector,
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return_metadata=MetadataQuery(distance=True),
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)
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print("Done.", file=sys.stderr)
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objects = []
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for obj in response.objects:
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obj_data = {
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"uuid": str(obj.uuid),
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"properties": dict(obj.properties),
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"distance": obj.metadata.distance if obj.metadata else None,
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}
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objects.append(obj_data)
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result = {
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"query": query,
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"collection": collection,
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"limit": limit,
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"distance_threshold": distance,
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"target_vector": target_vector,
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"objects": objects,
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"object_count": len(objects),
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}
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if json_output:
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print(json.dumps(result, indent=2, default=str))
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else:
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print(f"## Semantic Search Results\n")
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print(f"**Query:** {query}")
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print(f"**Collection:** {collection}")
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if distance:
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print(f"**Max Distance:** {distance}")
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print(f"**Found:** {len(objects)} objects\n")
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if objects:
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all_props = set()
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for obj in objects:
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all_props.update(obj.get("properties", {}).keys())
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sorted_props = sorted(list(all_props))
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headers = ["#", "UUID", "Distance"] + sorted_props
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header_row = "| " + " | ".join(headers) + " |"
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separator_row = "| " + " | ".join(["---"] * len(headers)) + " |"
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print(header_row)
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print(separator_row)
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for idx, obj in enumerate(objects, 1):
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dist = obj.get("distance")
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dist_str = f"{dist:.4f}" if dist is not None else "N/A"
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row_data = [
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str(idx),
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str(obj.get("uuid", "N/A")),
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dist_str,
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]
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props = obj.get("properties", {})
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for prop in sorted_props:
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val = props.get(prop, "-")
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val_str = str(val).replace("\n", " ").replace("|", "\\|")
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row_data.append(val_str)
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print("| " + " | ".join(row_data) + " |")
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print()
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else:
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print("No objects found matching the query.\n")
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except weaviate.exceptions.WeaviateConnectionError as e:
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print(f"Error: Connection failed - {e}", file=sys.stderr)
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raise typer.Exit(1)
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except Exception as e:
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print(f"Error: {e}", file=sys.stderr)
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raise typer.Exit(1)
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if __name__ == "__main__":
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app()
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