Embeddings generation

Generate embeddings to improve retrieval performance.

In this section, we'll explore how to generate embeddings for your data to improve retrieval performance in your RAG pipeline. Embeddings are a crucial part of the retrieval mechanism in RAG, as they represent the data in a high-dimensional space where similar items are closer together. By generating embeddings for your data, you can enhance the retrieval capabilities of your RAG pipeline and provide more accurate and relevant responses to user queries.

Embeddings are vector representations of data that capture the semantic meaning and context of the data in a high-dimensional space. They are generated using machine learning models, such as word embeddings or sentence embeddings, that learn to encode the data in a way that preserves its underlying structure and relationships. Embeddings are commonly used in natural language processing (NLP) tasks, such as text classification, sentiment analysis, and information retrieval, to represent textual data in a format that is suitable for computational processing.

The whole purpose of the embeddings is to allow us to quickly find the small chunks that are most relevant to our input query at inference time. An even simpler way of doing this would be to just to search for some keywords in the query and hope that they're also represented in the chunks. However, this approach is not very robust and may not work well for more complex queries or longer documents. By using embeddings, we can capture the semantic meaning and context of the data and retrieve the most relevant chunks based on their similarity to the query.

We're using the sentence-transformers library to generate embeddings for our data. This library provides pre-trained models for generating sentence embeddings that capture the semantic meaning of the text. It's an open-source library that is easy to use and provides high-quality embeddings for a wide range of NLP tasks.