Beginner’s Guide to OpenAI Text Embedding Models

Vector embeddings are essential in AI for converting complex, unstructured data into numerical vectors that machines can process. These embeddings capture the semantic meaning and relationships within the data, enabling more effective analysis and content generation.

OpenAI, the creator of ChatGPT, offers a variety of embedding models that offer high-quality vector representations that can be used across various applications, including semantic search, clustering and anomaly detection. This guide will explore how to leverage OpenAI’s text embedding models to build intelligent and responsive AI systems.

What Are Vector Embeddings and Embedding Models?

Before we get too into the weeds, let’s set the table on a few terms. First of all, what are vector embeddings? These are the cornerstones of many AI concepts. Vector embeddings are numerical representations of data, particularly unstructured data like text, videos, audio, images and other digital media. They capture the semantic meaning and the relationships within that data, and provide an efficient way for storage systems and AI models to understand, process, store and retrieve complex and high-dimensional unstructured data.

So, if an embedding is a numerical representation of data, how do you convert data into a vector embedding? This is where embedding models come in.

An embedding model is a specialized algorithm that transforms unstructured data into vector embeddings. It is designed to learn patterns and relationships within the data and then express them in a high-dimensional space. The key idea is that similar pieces of data will have similar vector representations and will be closer to each other in the high-dimensional space, allowing AI models to process and analyze the data more effectively.

For example, in the context of natural language processing (NLP), an embedding model might learn that the words “king” and “queen” are related and should be positioned near each other in the vector space, while a word like “banana” would be positioned farther away. This proximity in the vector space reflects the semantic relationships between the words.

A common use of embedding models and vector embeddings is in retrieval-augmented generation (RAG) systems. Rather than relying solely on pretrained knowledge in large language models (LLMs), RAG systems provide LLMs with additional contextual information before generating output. This extra data is converted into vector embeddings using an embedding model and then stored in a vector database like Milvus (which is also available as a fully managed service through Zilliz Cloud). RAG is ideal for organizations and developers who need detailed, fact-based query responses, making it valuable across various business sectors.

OpenAI Text Embedding Models

OpenAI, the company behind ChatGPT, offers a variety of embedding models that are well-suited for tasks like semantic search, clustering, recommendation systems, anomaly detection, diversity measurement and classification.

Given OpenAI’s popularity, many developers will likely experiment with RAG concepts using its models. While these concepts apply to embedding models in general, let’s focus on what OpenAI specifically provides.

When talking about NLP, a handful of OpenAI embedding models are especially relevant.

• text-embedding-ada-002
• text-embedding-3-small
• text-embedding-3-large

The following table provides a direct comparison between these models.

Choosing the Right Model

As with everything, choosing a model involves trade-offs. Before you go all-in on one of these models, make sure you clearly understand what you want to do, what resources you have available and what level of accuracy you expect from the generated output. With RAG systems, you’re likely balancing compute resources with the speed and accuracy of query responses.

• text-embedding-3-large: This is likely the preferred model when accuracy and embedding richness are critical. It uses the most CPU and memory resources (i.e., it is more expensive) and takes the longest to generate output, but that output will be high-quality. Typical use cases include research, high-stakes applications or dealing with very complex text.
• text-embedding-3-small: If you’re more concerned with speed and efficiency than achieving the absolute best results, this model is less resource intensive, resulting in lower costs and faster response times. Typical use cases include real-time applications or situations with limited resources.
• text-embedding-ada-002: While the other two models are the newest versions, this was OpenAI’s leading model before their introduction. This versatile model provides a good middle ground between the two extremes, providing solid performance with reasonable efficiency.

How To Generate Vector Embeddings With OpenAI

Let’s walk through how to generate vector embeddings with each of these embedding models. No matter which model you choose, you’ll need a few things to get started, including a vector database.

PyMilvus, the Python software development kit (SDK) for Milvus, is convenient in this context because it seamlessly integrates with all these OpenAI models. The OpenAI Python library is another option, and that’s an SDK offered by OpenAI.

For this tutorial, however, I’ll use PyMilvus to generate vector embeddings and store them in Zilliz Cloud for a simple semantic search.

Getting started with Zilliz Cloud is straightforward:

• Sign up for a free Zilliz Cloud account.
• Set up a serverless cluster and get the public endpoint and API key.
• Create a vector collection and insert your vector embeddings.
• Run a semantic search on the stored embeddings.

OK, now I’ll explain how to generate vector embeddings for each of the three models discussed above.

text-embedding-ada-002text-embedding-ada-002

Generate vector embeddings with text-embedding-ada-002 and store them in Zilliz Cloud for semantic search:

text-embedding-3-small

Generate vector embeddings with text-embedding-3-small and store them in Zilliz Cloud for semantic search:

text-embedding-3-large

Generate vector embeddings with text-embedding-3-large and store them in Zilliz Cloud for semantic search:

Conclusion

While this tutorial just scratches the surface, these scripts should be enough to get you started with vector embeddings. It’s worth noting that these are by no means the only models available. This extensive list of AI models all work with Milvus. Regardless of your AI use case, you’ll probably find a model there that addresses your needs.

To learn more about Milvus, Zilliz Cloud, RAG systems, vector databases and more, visit Zilliz.com.

The post Beginner’s Guide to OpenAI Text Embedding Models appeared first on The New Stack.

A comprehensive guide to using OpenAI text embedding models for embedding creation and semantic search in GenAI applications.