Initial implementation of pgvector and Oracle 26ai vector search demo

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>

pgvector-demo/backend/index_images.py

@@ -0,0 +1,56 @@
+import os
+from dotenv import load_dotenv
+from db import get_connection
+from embedder import embed_image
+
+load_dotenv()
+
+PHOTOS_DIR = os.getenv("PHOTOS_DIR")
+
+CREATE_TABLE = """
+CREATE TABLE IF NOT EXISTS images (
+    id        SERIAL PRIMARY KEY,
+    filename  TEXT NOT NULL UNIQUE,
+    filepath  TEXT NOT NULL,
+    embedding vector(512)
+);
+"""
+
+CREATE_INDEX = """
+CREATE INDEX IF NOT EXISTS images_embedding_idx
+    ON images USING hnsw (embedding vector_cosine_ops);
+"""
+
+INSERT = """
+INSERT INTO images (filename, filepath, embedding)
+VALUES (%s, %s, %s)
+ON CONFLICT (filename) DO NOTHING;
+"""
+
+def main():
+    conn = get_connection()
+    cur = conn.cursor()
+    cur.execute(CREATE_TABLE)
+    cur.execute(CREATE_INDEX)
+    conn.commit()
+
+    files = [f for f in os.listdir(PHOTOS_DIR) if f.lower().endswith((".jpg", ".jpeg"))]
+    print(f"Found {len(files)} photos in {PHOTOS_DIR}")
+
+    for i, filename in enumerate(files, 1):
+        filepath = os.path.join(PHOTOS_DIR, filename)
+        cur.execute("SELECT 1 FROM images WHERE filename = %s", (filename,))
+        if cur.fetchone():
+            print(f"[{i}/{len(files)}] Skipping {filename} (already indexed)")
+            continue
+        embedding = embed_image(filepath)
+        cur.execute(INSERT, (filename, filepath, embedding))
+        conn.commit()
+        print(f"[{i}/{len(files)}] Indexed {filename}")
+
+    cur.close()
+    conn.close()
+    print("Done.")
+
+if __name__ == "__main__":
+    main()