OpenAI Embeddings: Complete Guide · Lesson 1
Embeddings: Basic Usage
Obtain vector representations of text via the OpenAI API. We cover the text-embedding-3-small model, exponential backoff with tenacity, and best practices for batch processing.
Practical exercise
What to do after this lesson
Write a get_embedding function with @retry, obtain embeddings for 5 different phrases, and compute cosine similarity between them. Compare semantically close and distant pairs.
Task grader
Write a get_embedding function with @retry, obtain embeddings for 5 different phrases, and compute cosine similarity between them. Compare semantically close and distant pairs.
Ready-to-use prompt
Template for this lesson
Copy and adapt to your context. Text in angle brackets should be replaced.
from tenacity import retry, wait_random_exponential, stop_after_attempt
from openai import OpenAI
import numpy as np
client = OpenAI()
@retry(wait=wait_random_exponential(min=1, max=20), stop=stop_after_attempt(6))
def get_embedding(text: str, model="text-embedding-3-small") -> list[float]:
return client.embeddings.create(
input=[text], model=model
).data[0].embedding
def cosine_similarity(a, b):
return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))
texts = [
"The cat sat on the mat",
"A feline rested on the rug",
"Python is a programming language",
]
embeddings = [get_embedding(t) for t in texts]
print(cosine_similarity(embeddings[0], embeddings[1])) # ~0.92
print(cosine_similarity(embeddings[0], embeddings[2])) # ~0.6