Embedding Models | Advanced Concepts

Introduction

Embedding models in the OpenAI API allow you to convert text or other inputs into numerical representations (embeddings) that capture semantic meaning. This tutorial covers the concept of embedding models, how to use them with the OpenAI API, and provides examples in JavaScript and Python.

What are Embedding Models?

Embedding models transform input data (such as text) into vectors of numerical values that represent the semantic context of the input. These embeddings can be used for tasks like similarity comparison, clustering, and more.

Using the OpenAI API for Embedding Models

Generating Embeddings

To generate embeddings using the OpenAI API, you can send a request specifying the model and input data.

POST /v1/embeddings HTTP/1.1
Host: api.openai.com
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

{
    "model": "gpt-3.5-turbo",
    "inputs": "Generate embeddings for this text"
}

Replace YOUR_API_KEY with your actual API key. In this example, the model specifies the embedding model and inputs contains the text for which embeddings are requested.

Using Embeddings

Once you have generated embeddings, you can use them for various applications such as similarity search or clustering.

GET /v1/embeddings/generate-12345 HTTP/1.1
Host: api.openai.com
Authorization: Bearer YOUR_API_KEY

This API request retrieves the embeddings generated for a specific input with ID generate-12345.

Parameters

Here are some common parameters used when working with embedding models:

model: Name or ID of the embedding model.
inputs: Text or data for which embeddings are generated.

Examples in JavaScript

Here's how you can use embedding models with the OpenAI API in JavaScript:

const axios = require('axios');

const apiKey = 'YOUR_API_KEY';
const endpoint = 'https://api.openai.com/v1/embeddings';

async function generateEmbeddings(model, inputs) {
    try {
        const response = await axios.post(endpoint, {
            model: model,
            inputs: inputs
        }, {
            headers: {
                'Content-Type': 'application/json',
                'Authorization': `Bearer ${apiKey}`
            }
        });

        console.log('Embeddings generated:', response.data);
        return response.data;
    } catch (error) {
        console.error('Error:', error);
    }
}

const modelName = 'gpt-3.5-turbo';
const textInputs = 'Generate embeddings for this text';

generateEmbeddings(modelName, textInputs);

Examples in Python

Here's how you can use embedding models with the OpenAI API in Python:

import openai

api_key = 'YOUR_API_KEY'
openai.api_key = api_key

def generate_embeddings(model, inputs):
    response = openai.Embedding.create(
        model=model,
        inputs=inputs
    )
    
    print('Embeddings generated:', response)

model_name = 'gpt-3.5-turbo'
text_inputs = 'Generate embeddings for this text'

generate_embeddings(model_name, text_inputs)

Conclusion

Embedding models in the OpenAI API provide a powerful way to convert text or other data into numerical representations (embeddings) that capture semantic meaning. By following the steps outlined in this tutorial and using examples in JavaScript and Python, you can effectively utilize embedding models for various applications requiring semantic analysis and data representation.

Using Embedding Models with OpenAI API