The Complete Guide to AI Embeddings (With Python Code)

# "The cat sat on the mat" → [0.23, -0.45, 0.67, ...]  (1536 numbers)
# "A feline rested on a rug" → [0.21, -0.43, 0.65, ...]  (similar!)
# "Stock prices rose sharply" → [-0.89, 0.12, -0.34, ...] (very different!)

from openai import OpenAI

client = OpenAI()

def get_embedding(text: str, model: str = "text-embedding-3-large") -> list[float]:
    response = client.embeddings.create(
        input=text,
        model=model
    )
    return response.data[0].embedding

# Generate embeddings
embedding = get_embedding("How do neural networks learn?")
print(f"Dimensions: {len(embedding)}")  # 3072 for text-embedding-3-large

from sentence_transformers import SentenceTransformer

# Free, runs locally, no API key needed
model = SentenceTransformer('all-MiniLM-L6-v2')

texts = [
    "How do neural networks learn?",
    "What is backpropagation in deep learning?",
    "Best restaurants in New York City"
]

embeddings = model.encode(texts)
print(f"Shape: {embeddings.shape}")  # (3, 384)