dierk
66f7db40b0
Three FastAPI backends comparing PostgreSQL/pgvector and Oracle 26ai for semantic image search using CLIP embeddings: Python-side embedding for both databases, plus Oracle in-database embedding via VECTOR_EMBEDDING(CLIP_TXT). Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
57 lines
1.4 KiB
Python
57 lines
1.4 KiB
Python
import os
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from dotenv import load_dotenv
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from db import get_connection
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from embedder import embed_image
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load_dotenv()
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PHOTOS_DIR = os.getenv("PHOTOS_DIR")
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CREATE_TABLE = """
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CREATE TABLE IF NOT EXISTS images (
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id SERIAL PRIMARY KEY,
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filename TEXT NOT NULL UNIQUE,
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filepath TEXT NOT NULL,
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embedding vector(512)
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);
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"""
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CREATE_INDEX = """
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CREATE INDEX IF NOT EXISTS images_embedding_idx
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ON images USING hnsw (embedding vector_cosine_ops);
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"""
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INSERT = """
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INSERT INTO images (filename, filepath, embedding)
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VALUES (%s, %s, %s)
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ON CONFLICT (filename) DO NOTHING;
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"""
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def main():
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conn = get_connection()
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cur = conn.cursor()
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cur.execute(CREATE_TABLE)
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cur.execute(CREATE_INDEX)
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conn.commit()
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files = [f for f in os.listdir(PHOTOS_DIR) if f.lower().endswith((".jpg", ".jpeg"))]
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print(f"Found {len(files)} photos in {PHOTOS_DIR}")
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for i, filename in enumerate(files, 1):
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filepath = os.path.join(PHOTOS_DIR, filename)
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cur.execute("SELECT 1 FROM images WHERE filename = %s", (filename,))
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if cur.fetchone():
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print(f"[{i}/{len(files)}] Skipping {filename} (already indexed)")
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continue
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embedding = embed_image(filepath)
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cur.execute(INSERT, (filename, filepath, embedding))
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conn.commit()
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print(f"[{i}/{len(files)}] Indexed {filename}")
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cur.close()
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conn.close()
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print("Done.")
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if __name__ == "__main__":
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main()
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