How to Generate Text Embeddings Using Python

In this article, we will guide you step-by-step on how to generate text embeddings using Python and the Eden AI API.

Whether you are building a recommendation engine, semantic search, or classification model, embedding text into vectors is a foundational step in modern natural language processing (NLP).

We will also explain why Eden AI is a practical and efficient solution for this task.

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What Are Text Embeddings

Text embeddings are numerical vector representations of text that capture the semantic meaning of words, sentences, or paragraphs. These vectors make it easier for machines to understand and process human language.

Text embeddings are used in:

• Semantic Search: Matching user queries with the most relevant documents.
• Recommendation Systems: Suggesting content based on textual similarities.
• Text Classification & Clustering: Grouping similar content.
• Chatbots & Assistants: Understanding context better.

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How to Generate Text Embeddings

Get Access to Eden AI

1. Sign Up for an Eden AI Account: To begin using the Text Embeddings API, create an account on Eden AI. After registration, you’ll receive an API key that unlocks access to Text Embeddings among many other AI services.

2. Access Text Technologies: After logging in, navigate to the Text section of the platform.

3. Select Text Embeddings: Choose the Text Embeddings feature.

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Test Models Live on Eden AI

Before integrating in code, you can test embedding models directly on the Eden AI platform to compare providers and understand outputs.

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Implementing Text Embeddings in Python

Install the requests Module

We’ll use Python’s requests module to make HTTP calls. Install it using pip if it’s not already installed:

pip install requests

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Prepare the Code

Below is an example Python script to generate embeddings from Eden AI:

import requests

response = requests.post(
    "https://api.edenai.run/v2/text/embeddings/",
    json={
        "providers": ["google"],  # You can add other providers like "openai", "cohere", etc.
        "texts": [
            "Machine learning is amazing",
            "Natural language processing is powerful"
        ]
    },
    headers={
        "accept": "application/json",
        "content-type": "application/json",
        "authorization": "Bearer <your_api_key>"  # Replace with your actual Eden AI API key
    }
)

print(response.json())

‍Explanation of the Code

• Endpoint: https://api.edenai.run/v2/text/embeddings/ – Eden AI’s unified endpoint for text embeddings.
• Providers: A list of providers to use (e.g., google, openai, cohere).
• Texts: A list of strings to embed.
• Headers: Include the API key using Bearer authentication.

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Interpreting the Output

Here’s an example output (simplified):

{
  "google": {
    "items": [
      {
        "embedding": [0.034, -0.213, ..., 0.145],
        "text": "Machine learning is amazing"
      },
      {
        "embedding": [0.012, -0.167, ..., 0.101],
        "text": "Natural language processing is powerful"
      }
    ],
    "cost": 0.0001,
    "provider": "google",
    "status": "success"
  }
}

Explanation:

• embedding: A list of floating-point numbers representing the semantic meaning of the text.
• text: The original input string.
• provider: Which provider generated the embedding.
• cost: The API usage cost (if applicable).
• status: Indicates whether the request succeeded.

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Why Eden AI is the Best Tool for Text Embeddings

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Eden AI offers several advantages for Text Embeddings.

Access to multiple providers‍

With Eden AI, you can choose from a variety of providers, giving you great flexibility.

Ease of use‍

Eden AI’s API is designed to be simple and intuitive, making it easy for developers to integrate many AI services into their applications with minimal effort.

Scalability‍

Whether you’re working on small projects or large-scale applications, Eden AI is built to scale with your needs, making it suitable for a wide range of use cases.

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Conclusion

Recap

• Text embeddings are crucial for modern NLP applications.
• Eden AI simplifies embedding generation with a unified API and access to multiple providers.
• Using Python and Eden AI, you can quickly build applications that understand and process text semantically.

If you're looking to integrate text embeddings into your app or project, Eden AI offers a flexible, efficient, and developer-friendly solution. With easy setup and robust documentation, you can get started in minutes.