What are Gemini API embeddings and what models support them?

from google import genai
from google.genai import types
import numpy as np

client = genai.Client()

# Generate embeddings with Gemini Embedding:
result = client.models.embed_content(
    model="gemini-embedding-exp-03-07",  # current experimental embedding model
    contents=[
        "Python is a versatile programming language.",
        "Gemini is Google's AI model family.",
        "The stock market fell today.",
    ]
)

vectors = [e.values for e in result.embeddings]
print(f"Embedding dimensions: {len(vectors[0])}")  # typically 768 or 3072

# Compute cosine similarity:
def cosine_sim(a, b):
    a, b = np.array(a), np.array(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

print(f"Python vs Gemini: {cosine_sim(vectors[0], vectors[1]):.3f}")  # medium
print(f"Python vs stocks: {cosine_sim(vectors[0], vectors[2]):.3f}")  # low

# Task type specification (improves accuracy):
result = client.models.embed_content(
    model="gemini-embedding-exp-03-07",
    contents="How does async/await work in Python?",
    config=types.EmbedContentConfig(
        task_type="RETRIEVAL_QUERY"  # vs RETRIEVAL_DOCUMENT, SEMANTIC_SIMILARITY
    )
)