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>
67 lines
1.9 KiB
Python
67 lines
1.9 KiB
Python
import os
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import array
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from dotenv import load_dotenv
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from db_oracle 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 images (
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id NUMBER GENERATED ALWAYS AS IDENTITY PRIMARY KEY,
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filename VARCHAR2(255) NOT NULL UNIQUE,
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filepath VARCHAR2(1000) NOT NULL,
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embedding VECTOR(512, FLOAT32)
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)
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"""
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CREATE_INDEX = """
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CREATE VECTOR INDEX images_embedding_idx
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ON images(embedding)
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ORGANIZATION INMEMORY NEIGHBOR GRAPH
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WITH DISTANCE COSINE
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WITH TARGET ACCURACY 95
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PARAMETERS (type HNSW, neighbors 32, efconstruction 200)
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"""
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INSERT = "INSERT INTO images (filename, filepath, embedding) VALUES (:1, :2, :3)"
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def table_exists(cur):
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cur.execute("SELECT COUNT(*) FROM user_tables WHERE table_name = 'IMAGES'")
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return cur.fetchone()[0] > 0
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def main():
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conn = get_connection()
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cur = conn.cursor()
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if not table_exists(cur):
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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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print("Table and index created.")
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else:
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print("Table already exists, skipping creation.")
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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 = :1", (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 = array.array("f", 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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