In this article, we will explore the power of embeddings in natural language processing. Embeddings are one of the most interesting things in the field of NLP. They allow us to perform operations on documents within a specific context. For example, imagine that your organization or application needs to find relevant documents related to a specific topic. Instead of searching the entire internet, you can use embeddings to find the most relevant documents within your organization’s context.
In this video, we will learn how to use Python and the OpenAI API to work with embeddings. The process involves three main steps:
- Reading local files and training them with the embedding functionality.
- Receiving a user question and transforming it into an embedding.
- Comparing the user’s embedding with the embeddings of the documents to find the most similar ones.
Once we have identified the most similar documents, we can ask the question to the chatGPT model, using the most similar document as the basis for the answer. This allows us to provide context-specific answers instead of relying on generic internet content.
The code for this example is available on GitHub. It is a relatively short code, consisting of around 100 lines. However, please note that the API key used in the video will not be available for execution. You will need to generate your own API key to run the code.
To summarize, embeddings are a powerful tool in NLP that allow us to find relevant documents within a specific context. By using Python and the OpenAI API, we can easily work with embeddings and provide context-specific answers to user questions. Check out the code on GitHub and start exploring the power of embeddings in NLP!
