File size: 2,630 Bytes
fb12ddc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | #!/usr/bin/env python3
"""
query_vision_image.py
======================
Query the HF-native image_index using a text prompt.
Embeds the text with CLIP / Qwen3-VL, then performs cosine
similarity search against stored image embeddings.
Usage:
python query_vision_image.py "sunset over water"
"""
import sys
import json
from pathlib import Path
from config import DEFAULT_PROJECT, EMBED_MODEL, EMBED_DIM
from vector_store import get_store
from embedding import embed_text
TOP_K = 5
MIN_SCORE = 0.15 # Adjusted for HF-native CLIP/Qwen scores
def search_images(query: str):
print(f"\n{'='*60}")
print(f" ARIA Vision — Image Search (HF-Native)")
print(f"{'='*60}")
print(f" Query: \"{query}\"")
print(f" Model: {EMBED_MODEL} ({EMBED_DIM}d)")
print()
print(" [1/3] Embedding query text...", end=" ", flush=True)
query_vector = embed_text(query)
print("✓")
print(" [2/3] Searching image_index...", end=" ", flush=True)
store = get_store(DEFAULT_PROJECT, "image_index")
raw_results = store.search(query_vector, top_k=TOP_K)
if not raw_results:
print("no results.")
print("\n ⚠ No images found. Did you run ingest_sample_vision.py first?")
return
rows = [r for r in raw_results if r.get("score", 0) >= MIN_SCORE]
print(f"✓ ({len(rows)} matches)")
print(f"\n [3/3] Results:")
print(f" {'─'*56}")
print(f" {'Rank':<6} {'File':<25} {'Size':<10} {'Resolution':<12} {'Score':<8}")
print(f" {'─'*56}")
for i, row in enumerate(rows):
file_name = row.get("file_name", Path(row.get("file_path", "?")).name)
print(
f" {i+1:<6} {file_name[:24]:<25} "
f"{row.get('file_size', '?'):<10} "
f"{row.get('resolution', '?'):<12} "
f"{row.get('score', 0):.4f}"
)
print(f" {'─'*56}")
output = {
"mode": "Image",
"query": query,
"results": [
{
"file_path": r.get("file_path", ""),
"file_name": r.get("file_name", ""),
"file_size": r.get("file_size", ""),
"resolution": r.get("resolution", ""),
"score": round(r.get("score", 0), 4),
}
for r in rows
],
}
print(f"\n JSON Response:")
print(f" {json.dumps(output, indent=2)}")
print()
def main():
if len(sys.argv) < 2:
print("Usage: python query_vision_image.py \"your search query\"")
sys.exit(1)
query = " ".join(sys.argv[1:])
search_images(query)
if __name__ == "__main__":
main()
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