Text Embeddings using OpenAI

Text embeddings convert text into numerical representations. These representations help computers understand and process language efficiently. OpenAI provides an easy-to-use API for generating embeddings, which can be used for search, classification, recommendation systems, and clustering tasks.

In this article, we will explore the fundamentals of text embeddings and demonstrate how to generate embeddings using OpenAI’s API with Python.

Generating Text Embeddings with OpenAI

OpenAI provides a robust API for generating embeddings. Below, we will walk through the steps to create text embeddings using Python.

Step 1: Install OpenAI SDK

Before we begin, install the OpenAI Python library if you haven’t already:

pip install openai

Step 2: Import Necessary Libraries

import openai
import numpy as np

Step 3: Set Up OpenAI API Key

To use OpenAI’s API, set up an API key from OpenAI’s platform and use it in your code:

OPENAI_API_KEY = "your_openai_api_key"

Step 4: Generate Embeddings

OpenAI API provides various models for text embeddings. Below, we use text-embedding-ada-002, which is one of the most efficient models.

from openai import OpenAI
client = OpenAI()

response = client.embeddings.create(
    input="Text Embeddings",
    model="text-embedding-3-small"
)

# Get the embedding vector
embedding_vector = response.data[0].embedding

# Print basic information
print(f"Model: text-embedding-3-small")
print(f"Input text: 'Text Embeddings'")
print(f"Embedding vector size: {len(embedding_vector)}")

# Print first few dimensions with formatting
print("\nFirst 5 dimensions of the embedding vector:")
for i, value in enumerate(embedding_vector[:5]):
    print(f"Dimension {i+1}: {value:.6f}")

Output:

This function takes a text input and returns a numerical vector representation.

Use Cases of OpenAI Text Embeddings

1. Semantic Search: Instead of keyword-based searches, use embeddings to find semantically similar documents.
2. Chatbots: Enhance chatbot responses by matching user queries to the closest embeddings.
3. Content Recommendation: Recommend articles, videos, or products based on text similarity.
4. Sentiment Analysis: Use embeddings to classify sentiments by clustering similar texts.

Text embeddings are a crucial part of modern NLP applications, allowing machines to interpret language meaningfully. With OpenAI’s easy-to-use API, generating embeddings is straightforward and powerful.