Embeddings
Explore how to use the OpenAI embeddings endpoint to transform text into numerical vectors for machine learning. Learn to calculate semantic similarity using cosine similarity and apply embeddings in practical NLP applications like anomaly detection and text clustering.
The embeddings endpoint
Embedding is a method to represent the data in a vector of continuous numbers. We can provide these vectors to machine learning algorithms and models. Similar texts will have the same embedding vectors, and two different texts will have very different embeddings. OpenAI API takes text as input and returns the embedding vector.
The Embeddings API call
To get an embeddings vector for a chunk of text, we can call the following function:
response = client.embeddings.create(input="The text whose embeddings are required",model="<model_name>")
Understanding the embeddings endpoint
Let’s look at the embeddings endpoint in more detail, reviewing the request parameters and the response parameters.
Request parameters
Let’s look at the parameters that are required to make a request at the embeddings endpoint. ...