This new series of articles will help you enhance your SEO skills by leveraging AI, specifically OpenAI’s text embeddings. In the previous article, we discussed vectors, vector distance, and text embeddings. Now, let’s dive into using text embeddings to identify and prevent keyword cannibalization.
Let’s start by comparing OpenAI’s text embeddings models:
1. **text-embedding-ada-002**
– Dimensionality: 1536
– Pricing: $0.10 per 1M tokens
– Great for most use cases.
2. **text-embedding-3-small**
– Dimensionality: 1536
– Pricing: $0.002 per 1M tokens
– Faster and cheaper, but less accurate.
3. **text-embedding-3-large**
– Dimensionality: 3072
– Pricing: $0.13 per 1M tokens
– More accurate for complex long text-related tasks, but slower.
Before we begin, make sure you have Python and Jupyter installed on your computer. Jupyter is a web-based tool that allows for complex data analysis and machine learning model development in any programming language.
Here’s a quick guide to get started:
– Download and install Python.
– Open your command line (Windows) or terminal (Mac).
– Install Jupyter by entering `pip install jupyterlab` and `pip install notebook`.
– Run Jupyter by typing `jupyter lab`.
Now, let’s set up your OpenAI API:
– Sign up for OpenAI’s API and set up billing.
– Enable email notifications for usage limits.
– Obtain API keys under Dashboard > API keys and keep them private.
To experiment with text embeddings, you can follow these steps:
– Install necessary Python libraries like pandas, openai, scikit-learn, numpy, and unidecode.
– Download a sample CSV file with URLs and titles and upload it to your Jupyter notebook.
– Set your OpenAI API key and run the code provided in a Jupyter notebook.
The code will read the CSV file, clean the titles, generate embeddings, calculate similarity between titles, group similar titles, and write the results to a new CSV file.
When identifying keyword cannibalization, it’s crucial to choose the right similarity threshold. Experiment with different thresholds to find the one that best fits your data and task.
Different text embedding models like ‘text-embedding-ada-002’, ‘text-embedding-3-small’, and ‘text-embedding-3-large’ yield varying results in identifying similar articles. It’s essential to test these models with your data to see which one performs best for your specific use case.
In conclusion, working with OpenAI’s embedding models opens up a wide range of possibilities for improving your SEO tasks. Experiment with different models, thresholds, and techniques to find the best approach for your needs. Stay tuned for more insights on leveraging AI tools for SEO tasks.