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 update the Document
Pydantic model to include an embedding
attribute that stores the embedding generated for each document. This allows us to associate the embeddings with the corresponding documents and use them for retrieval purposes in the RAG pipeline.
In this stage, we have utilized the 'parent directory', which we had previously stored in the vector store as an additional attribute, as a means to color the values. This approach allows us to gain some insight into the semantic space inherent in our data. It demonstrates that you can visualize the embeddings and observe how similar chunks are grouped together based on their semantic meaning and context.
So this step iterates through all the chunks and generates embeddings representing each piece of text. These embeddings are then stored as an artifact in the ZenML artifact store as a NumPy array. We separate this generation from the point where we upload those embeddings to the vector database to keep the pipeline modular and flexible; in the future we might want to use a different vector database so we can just swap out the upload step without having to re-generate the embeddings.
In the next section, we'll explore how to store these embeddings in a vector database to enable fast and efficient retrieval of relevant chunks at inference time.
Code Example
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