How to generate text embeddings with OpenAI's API in Python
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Text embeddings convert concepts into numerical sequences, making it easy for computers to grasp the relationships between different ideas. OpenAI has launched an enhanced and more efficient embedding model that’s not only more capable but also cost-friendly and easier to handle. In this Answer, we will look into the process of generating these text embeddings with OpenAI’s API utilizing Python.
What are embeddings?
An essential part of natural language processing (NLP) and machine learning, embeddings represent words or even whole documents as vectors in a high-dimensional space. This representation holds the underlying meaning of the text, and it’s useful for many tasks such as clustering, classification, and topic identification.
For example, the embedding vector of “felines say” will be more similar to the embedding vector of “meow” than that of “roar.”
Setting up the environment
Before you begin, you’ll need to have Python installed on your system and the OpenAI Python library. You can install the latter using pip:
pip install openai
Don’t forget to secure an API key from OpenAI for the necessary authentication.
Creating text embeddings
The latest model, text-embedding-ada-002, from OpenAI, supersedes five earlier models, delivering enhanced performance at a reduced cost. The following shows how to request the new model at the /embeddings endpoint:
import openaiimport osopenai.api_key = os.environ["SECRET_KEY"]response = openai.Embedding.create(input="Educative answers section is helpful",model="text-embedding-ada-002")print(response)
This code will render the embedding for the input text provided.
Text similarity models
These models help identify how similar different texts are
import openai, numpy as npopenai.api_key = os.environ["SECRET_KEY"]resp = openai.Embedding.create(input=["feline friends say", "meow"],engine="text-similarity-davinci-001")embedding_a = resp['data'][0]['embedding']embedding_b = resp['data'][1]['embedding']similarity_score = np.dot(embedding_a, embedding_b)print(similarity_score)
Text search models
Text search models enable large-scale search tasks, like finding relevant documents among a collection given a text query. They generalize better than word overlap techniques and capture the semantic meaning of the text.
Code search models
These specialized models are crafted for searching code, enabling you to locate relevant code segments using natural language inquiries. They offer substantially improved outcomes compared to earlier techniques.
Applications and use cases
OpenAI’s embeddings have found their way into various practical applications, including:
Kalendar AI: Tailoring the correct sales pitch to clients.
Notion: Enhancing search capabilities beyond mere keyword matching.
JetBrains Research: Employed in astronomical data examination.
FineTune Learning: Assisting in discovering textbook content according to educational goals.
Conclusion
The text embeddings provided by OpenAI offer a versatile way to interact with both text and code. With just a minimal amount of Python coding, you can create embeddings that capture the actual essence of your input, unlocking a wide range of applications. Whether you aim to build a search mechanism, categorize documents, or visually represent the relationships among various concepts, OpenAI’s embeddings are an invaluable resource in your array of tools.
