dierk
1c5e00d8e4
- embedder.py: lazy model load rationale, RGB conversion, shared vector space
- main.py: why vec appears twice, ::vector cast, 1-distance score formula
- main_oracle.py: why array.array("f") is required instead of plain list
- main_oracle_indb.py: no embedder import — embedding done inside Oracle SQL
- index_images_oracle.py: same array.array requirement on indexing path
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
68 lines
2.0 KiB
Python
68 lines
2.0 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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# oracledb requires array.array("f") for VECTOR(512, FLOAT32) — plain list is rejected.
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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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