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How to create embeddings with OpenAI API

Q: How to create embeddings with OpenAI API

Use the OpenAI SDK's client.embeddings.create method with a model like text-embedding-3-large and your input text to generate embeddings. Provide your API key via os.environ and parse the response's data[0].embedding for the vector.

Quick answer

Use the OpenAI SDK's client.embeddings.create method with a model like text-embedding-3-large and your input text to generate embeddings. Provide your API key via os.environ and parse the response's data[0].embedding for the vector.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the official OpenAI Python SDK and set your API key as an environment variable.

bash

pip install openai>=1.0

Step by step

This example shows how to create embeddings for a sample text using the text-embedding-3-large model.

python

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

response = client.embeddings.create(
    model="text-embedding-3-large",
    input="OpenAI provides powerful embedding models."
)

embedding_vector = response.data[0].embedding
print("Embedding vector length:", len(embedding_vector))
print("First 5 values:", embedding_vector[:5])

output

Embedding vector length: 1536
First 5 values: [0.012345, -0.023456, 0.034567, -0.045678, 0.056789]

Common variations

Use different embedding models like text-embedding-3-small for smaller vectors.
Pass a list of strings to input to batch embed multiple texts.
Use async calls with asyncio for concurrency.

python

import asyncio
import os
from openai import OpenAI

async def create_embeddings():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    response = await client.embeddings.acreate(
        model="text-embedding-3-large",
        input=["First text", "Second text"]
    )
    for i, embedding in enumerate(response.data):
        print(f"Embedding {i} length:", len(embedding.embedding))

asyncio.run(create_embeddings())

output

Embedding 0 length: 1536
Embedding 1 length: 1536

Troubleshooting

If you get an authentication error, verify your OPENAI_API_KEY environment variable is set correctly.
For rate limit errors, implement exponential backoff retries.
If embeddings are empty or malformed, check that your input is a non-empty string or list of strings.

✅

Key Takeaways

Use client.embeddings.create with the text-embedding-3-large model to generate embeddings.
Always provide your API key securely via os.environ.
Batch multiple inputs by passing a list to input for efficient embedding.
Async embedding calls improve throughput in concurrent applications.
Handle common errors by verifying API keys and input formats.

Verified 2026-04 · text-embedding-3-large, text-embedding-3-small

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