What Are Embeddings in OpenAI?

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Embeddings, in the context of OpenAI, are numerical representations of textual or code-based information. They convert concepts into number sequences, which enables computers to comprehend the relationships between these concepts more easily.

OpenAI’s embedding models, such as text-embedding-ada-002, have been designed to outperform other top models in various standard benchmarks, including a 20% relative improvement in code search performance.

What Are Embeddings in Python OpenAI?

This concept is used in applications such as document search and code search, where high-quality embeddings enable accurate and efficient retrieval of related material.

Firstly, you should know that the concept of embeddings isn’t proprietary to OpenAI, but rather a broadly recognized principle within the machine learning field.

πŸ₯œ In a nutshell, when someone talks about “computing the embedding of a text”, they’re essentially converting that text into a unique numerical form. However, this isn’t just any number – it’s a special kind of number that allows arithmetic operations to be conducted with words or phrases, a task at which computers excel.

As an example, using embeddings, you could mathematically express something like "child" + "time" equating to "adult" or "queen" minus "female" resulting in "king". (source)

Another handy feature of these numbers is that you can use them to group alike words together. For instance, "dog" and "puppy" would have numbers that are relatively close to each other, but significantly distant from "submarine".

For users of Language Learning Models (LLMs) like ChatGPT, embeddings are typically leveraged for search operations.

Bear in mind that LLMs have finite space for the input and output, often referred to as the context window, meaning you can’t process an entire book, for example. If you wanted to extract information about the book, you’d divide it into smaller sections that can fit within the context window, and then you’d follow these steps:

1. Compute the embedding for each section.
2. Once you've calculated the "number" (embedding) for each section, take the user prompt and compute its embedding.
3. Next, compare the user embedding to the book section embeddings and identify the closest one. Since these are numerical values, you can use a mathematical function to determine their proximity (cosine distance is recommended by OpenAI).
4. Now, you can pass the question to GPT, accompanied by the relevant section that you've just identified.

This procedure is extremely useful and versatile and can be implemented for various data sources, including emails in your inbox, documents on your drive, or even Amazon reviews.

To generate embeddings, you can utilize OpenAI’s embeddings API endpoint. It takes a piece of text or code as input and returns an information-dense vector of floating-point numbers.

The distance between two embeddings in this vector space indicates the semantic similarity between the original inputs, thereby providing a robust method for measuring relatedness.

Key Takeaways

  • OpenAI embeddings provide numerical representations of text or code for better computer understanding.
  • By using the embeddings API, you can effectively measure and assess semantic similarity in vector space.
  • High-quality embeddings play a crucial role in applications like document search and code search, enhancing retrieval accuracy and efficiency.

Understanding Embeddings

Numerical Representations

Embeddings are numerical representations of complex entities, such as words, phrases, or even sentences.

In OpenAI, these embeddings are used to capture the semantic meaning of a piece of text, transforming it into a format that can be more easily processed and analyzed.

By converting text into a dense vector of floating-point numbers, you’re able to efficiently perform various linguistic tasks.

Vector Space

The vector space associated with embeddings is a mathematical environment in which these numerical representations can exist and interact. An important characteristic of this space is that the distance between two embeddings correlates with their semantic similarity.

In other words, the closer the embeddings are in the vector space, the more likely they are to have a similar meaning. This property allows you to effectively compare, organize, and classify text data.


One practical way embeddings can be used is to uncover relationships between different elements in a text. By mapping these vector representations onto a higher-dimensional space, you can visualize connections and draw conclusions regarding the semantic structure of language.

For instance, when studying the relationships of words in a sentence, the position of these words in the vector space can reveal synonyms, antonyms, or other meaningful associations.

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OpenAI Embeddings

GPT and GPT-3

OpenAI Embeddings are a powerful way to represent and process natural language. They are derived from the company’s highly successful GPT-3, GPT-3.5, and GPT-4 models, which have revolutionized the field of natural language processing.

Using these models, you can achieve remarkable results in tasks like semantic search, clustering, topic modeling, and classification.


With Text-Embedding-Ada-002, your applications can benefit from an optimized text embedding model specifically built for this purpose. This model enables you to take advantage of OpenAI’s powerful embeddings feature set while focusing on the core tasks you need to accomplish.

To use text-embedding-ada-002, simply send a request to the API, which will return the corresponding vector representing your text input. By doing so, you can uncover a rich space of relationships and patterns, allowing your applications to better understand and interpret human language.


Davinci is another prominent model in the OpenAI ecosystem, known for its exceptional capabilities in understanding context and generating human-like text. When working with Davinci, you can take advantage of its embeddings to extract valuable insights from your text data.

Embedding Models in Machine Learning

Neural Networks

Neural networks are a key component in machine learning models. These networks mimic the structure and functionality of the human brain to process information and learn from it.

They consist of interconnected nodes or neurons, which work together to process input data and provide accurate predictions or classifications. In the context of text embeddings, neural networks can be trained to understand similarities and relationships within the natural language.

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Deep Learning

Deep learning is a subset of machine learning that deals with artificial neural networks containing multiple layers. These multiple layers of neurons enable deep learning models to capture complex patterns and abstractions in the data.

When working with text embeddings, deep learning models can automatically learn useful representations of words and phrases, allowing them to effectively capture the semantic meaning in your data. This helps improve the performance of natural language processing tasks.

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Natural Language Processing

Natural language processing (NLP) is an area of artificial intelligence that focuses on the interaction between computers and humans via natural language.

By leveraging embedding models from OpenAI, NLP tasks can benefit from a comprehensive understanding of linguistic patterns and structures. Embeddings allow machine learning models to represent words and phrases in a way that makes sense, ultimately leading to more accurate search, clustering, and recommendations in your projects.

Integrating these embeddings with Azure OpenAI can enhance the overall performance and efficiency of your NLP tasks.

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Text Similarity

Embeddings in OpenAI are very useful for measuring text similarity. They provide an effective way to compare the semantic meaning of different pieces of text. By converting texts into embeddings and calculating the distance between them in the vector space, you can assess the similarity of their meanings.

This can be used in various applications such as:

  • Identifying similar articles or documents
  • Grouping similar user-generated content
  • Detecting plagiarism

Text Classification

Text classification is another application of embeddings in OpenAI. By using embeddings, you can train machine learning models to categorize texts based on their semantic content.

This can help with tasks like:

  • Sentiment analysis
  • Spam detection
  • Topic modeling
  • Automatic tagging of content

The embeddings can serve as input features for your classification model, making it easier to understand and process the information in the text.

Text Search

OpenAI embeddings can improve your text search capabilities as well. With traditional keyword-based search, you usually rely on exact matches or simple word frequency, which can miss documents that are semantically related but do not use the same vocabulary.

By incorporating OpenAI embeddings into your search algorithm, you can:

  • Retrieve documents that are semantically similar to the query, even if they don’t have the exact words
  • Rank your search results according to semantic relevance
  • Enhance recommendation systems by finding related content

Code Search

OpenAI embeddings have applications in code search too. Just like text similarity, they can be used to measure the similarity of pieces of code.

This is particularly useful for developers who want to:

  • Find code examples or solutions to specific problems
  • Identify reusable code snippets
  • Detect code plagiarism

By converting code snippets into embeddings, you can compare their semantic meanings and discover related code fragments more effectively than through traditional string-based search methods.

Working with the Embeddings API


To begin using the Embeddings API, you need to authenticate first. Obtain your API key from the OpenAI platform and use it to access the /embeddings endpoint.

List Models

Before retrieving an embedding, you should know which models are available. You can list models by sending a request to the API endpoint. This will return a list of available embedding models, each designed for different functionalities (e.g., text similarity, text search, code search).

Retrieve an Embedding

To obtain an embedding, send a request to the /embeddings endpoint. You must include the model ID (e.g., text-embedding-ada-002) and your input text or code. The response you receive will contain an embedding vector that you can extract, save, and use for your desired application.

Here’s an example request to retrieve an embedding:

import openai

response = openai.Embedding.create(
  texts=["your text here"]

embedding = response['embeddings'][0]['vector']

Performing a Search

Once you have an embedding, you can use it to perform searches within your dataset. For instance, if you have a list of documents and want to find the most relevant ones to a given query:

  1. Preprocess: Tokenize your documents and create a storage for embeddings.
  2. Obtain document embeddings: Send each document to the /embeddings endpoint with the chosen model ID and save the resulting embedding vectors.
  3. Calculate similarities: Compute the similarity between the query embedding and the document embeddings using a suitable distance metric (e.g., cosine similarity) to get similarity scores.
  4. Rank documents: Sort the documents according to their similarity scores, with higher scores indicating stronger relevance.

By following these steps, you can efficiently search your dataset for relevant content using the power of OpenAI’s Embeddings API.

Remember to maintain a confident, knowledgeable, neutral, and clear tone of voice throughout your explanation.

Semantic Similarity and Cosine Similarity

Semantic Meaning

In the context of OpenAI, embeddings capture the semantic meaning of text, allowing you to understand and analyze the relationships between words, sentences, and documents.

With embeddings, you can turn unstructured text data into a structured format which can be easily utilized for various machine learning tasks. The main goal is to represent the semantic meaning in a numerical form that can be easily processed by algorithms.

Cosine Similarity Measures

To compare the semantic similarity between pieces of text, one can use cosine similarity. Cosine similarity is a measure that compares the angle between two vectors, in this case, the embedding vectors representing the texts.

If the vectors have a small angle between them, it indicates that the texts have similar meanings, whereas a large angle suggests that the texts are quite different. The cosine similarity ranges from -1 to 1, with -1 being completely dissimilar, 1 being identical, and 0 signifying no similarity.

When using OpenAI’s embeddings, the cosine similarity between the embeddings can be calculated to determine how closely related two pieces of text are in terms of their semantic meaning.

Practical Examples

Below are some examples to illustrate how cosine similarity and embeddings can be used in real-world applications:

  • Text clustering: Group documents with similar content together based on their embeddings and cosine similarity. This can be useful for organizing large volumes of text data for better navigation and search.
  • Sentiment analysis: Determine the sentiment of a piece of text by comparing its embeddings to those of known positive and negative texts using cosine similarity. This can help gauge the overall sentiment of user reviews, social media posts, or other text data.
  • Text classification: Assign predefined categories to documents based on the cosine similarity between their embeddings and the embeddings of known examples in each category. This method can be employed in tasks such as spam detection, news article categorization, or topic labeling.

Datasets and Use Cases

In this section, you will learn about the different types of datasets used in OpenAI embeddings, the Senteval benchmark, and creating a vector database for various use cases.

Dataset Types

When working with OpenAI embeddings, you may encounter the following types of datasets:

  • Text Datasets: These datasets consist of documents, articles, or sentences used to train and evaluate text embeddings. Text datasets are useful for tasks such as document search, text similarity, and natural language understanding.
  • Code Datasets: Datasets containing code snippets or source code files are employed for training and evaluating code embeddings. These datasets aid in code search, code-autocompletion, and other programming-related tasks.


Senteval is a benchmarking tool used by the OpenAI community to evaluate the quality and performance of text embeddings.

Senteval provides a set of tasks and metrics that allow you to measure the effectiveness of your embeddings in various language understanding tasks, ranging from sentiment analysis to entailment.

By using Senteval, you can ensure that your embeddings are fine-tuned to perform optimally for specific use cases.

Vector Database

Creating a vector database is essential when working with embeddings, as it enables efficient storage and retrieval of embedding vectors. A vector database can be created using tools such as FAISS or Annoy to store, index, and search embedding vectors efficiently.

For instance, when using text or code embeddings in OpenAI API, you can create a vector database to store the embeddings of your dataset and perform tasks like document search, finding similar documents, or retrieving relevant code snippets easily. By utilizing a vector database, you can enhance your search capabilities and optimize the performance of your application.

Performance and Limitations


When using OpenAI embeddings, it’s important to be aware of the limitations and risks. There are second-generation models (denoted by -002 in the model ID) and first-generation models (denoted by -001 in the model ID).

For most use cases, text-embedding-ada-002 is recommended as it offers better performance in text search, code search, and sentence similarity tasks compared to the older models. However, it’s vital to evaluate the model’s accuracy in the context of your specific application and requirements.

Response Time

Embeddings are information-dense representations of semantic meaning, with each embedding being a vector of floating point numbers.

The processing time required for generating embeddings might vary depending on the input text data and model used. To optimize your experience, select the appropriate model and ensure that your input text is processed within the token limits of the API request.


At the time of writing, these are the costs of the embeddings model (OpenAI):

Ada v2$0.0001 / 1K tokens
Ada v1$0.0040 / 1K tokens
Babbage v1$0.0050 / 1K tokens
Curie v1$0.0200 / 1K tokens
Davinci v1$0.2000 / 1K tokens

Overall, when using OpenAI embeddings, it’s essential to consider factors like accuracy, response time, and price to make an informed decision.

Additional Resources

In your quest to learn more about embeddings in OpenAI, it’s helpful to explore some additional resources that provide valuable context and insights. When you’re diving into OpenAI embeddings, you want to be well-informed and confident in your understanding of the topic.

The OpenAI Platform offers an excellent starting point, providing concise information on how to get embeddings using their API. This resource will guide you through the process of sending text strings to the API and obtaining embeddings in response.

Azure also has informative resources related to OpenAI embeddings. Azure OpenAI Service’s explanation of embeddings is an especially valuable read. This detailed article discusses embedding models and cosine similarity, helping you to understand the importance of data representation for machine learning models and algorithms.

Another Azure OpenAI resource offers helpful guidance on generating embeddings within their platform. This will give you a clear understanding of how embeddings, as information-dense representations, can be easily utilized by machine learning models and algorithms.

Finally, OpenAI’s blog post introduces the embeddings API endpoint, the purpose of embeddings, and discusses how it enables tasks such as semantic search and clustering. This post gives you an opportunity to learn about the objectives, benefits, and potential applications of OpenAI embeddings directly from the creators.

These additional resources will provide you a strong foundation to understand OpenAI embeddings thoroughly. Take time to explore these sources and gain a well-rounded understanding of the subject, positioning yourself as knowledgeable and confident in the world of OpenAI embeddings.

Frequently Asked Questions

What is the purpose of embeddings in OpenAI?

Embeddings in OpenAI are used to measure the relatedness of text strings. They help in tasks such as search (ranking results by relevance to a query), clustering (grouping text strings based on similarity), and recommendations (suggesting items with related text strings).

How do OpenAI embeddings work with neural networks?

OpenAI embeddings are generated using neural networks, which convert text strings into numerical representations that capture semantic relationships. The embeddings allow neural networks to understand the relationships between concepts more easily and perform tasks like classification, clustering, or similarity matching.

What are some common OpenAI embedding models?

OpenAI offers a variety of embeddings models. As of January 2022, the main focus is on text and code embeddings, as described in this OpenAI blog post. These embeddings help measure the relatedness of text and code, enabling various applications, such as search and clustering.

How can I use embeddings in OpenAI with Python?

To use embeddings in OpenAI with Python, you can use the OpenAI API. You will need an API key and the appropriate SDK for easier integration. Follow the OpenAI documentation for examples of how to generate embeddings using Python and interact with the API.

Are there any tutorials for working with OpenAI embeddings?

Yes, there are several tutorials and guides available for working with OpenAI embeddings. For example, Microsoft provides a tutorial on generating embeddings with Azure OpenAI, which guides you through the steps to generate embeddings and use them in your applications.

What are some alternatives to OpenAI embeddings?

There are various alternatives to OpenAI embeddings, with some popular options being word2vec, GloVe, and FastText. These methods provide similar capabilities, such as creating word or text embeddings that capture semantic relationships between words or phrases. You can explore and choose the most suitable method depending on your specific requirements and preferences.

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