What are embeddings in the OpenAI API and what are they used for?

Embeddings convert text (or other content) into dense numerical vectors that capture semantic meaning. Texts with similar meaning produce similar vectors, enabling mathematical operations on language: search, clustering, classification, and anomaly detection without requiring labelled training data for each task.

from openai import OpenAI
import numpy as np

client = OpenAI()

# Generate embeddings
response = client.embeddings.create(
    model="text-embedding-3-large",    # 3072 dimensions
    # model="text-embedding-3-small",  # 1536 dimensions, cheaper
    input=[
        "OpenAI Codex is an agentic coding assistant.",
        "GitHub Copilot helps developers write code.",
        "The stock market rose 2% today.",
    ]
)

# Extract vectors
vectors = [item.embedding for item in response.data]

# Semantic similarity via cosine similarity
def cosine_similarity(a, b):
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

sim_coding = cosine_similarity(vectors[0], vectors[1])  # high - both about coding AI
sim_unrelated = cosine_similarity(vectors[0], vectors[2])  # low - different topic
print(f"Coding similarity: {sim_coding:.3f}")    # e.g. 0.89
print(f"Unrelated similarity: {sim_unrelated:.3f}")  # e.g. 0.21

# Dimension reduction (for storage/speed tradeoff):
response = client.embeddings.create(
    model="text-embedding-3-large",
    input="example text",
    dimensions=512,  # reduce from 3072 to 512
)

Common use cases: RAG (Retrieval-Augmented Generation) where documents are embedded and stored in a vector database, then the most relevant chunks are retrieved for a query; semantic search; clustering documents; recommendation systems; and classifying content without labelled training data.

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