Understanding Vector Embeddings: How to Choose the Right Model for Your RAG Pipeline

Retrieval Augmented Generation (RAG) pipelines are the kind of elements where AI applications can be better than ever. If we peel back one of the layers a bit, you’ll see something that is known as vector embeddings. Don’t know what they are? No need to worry, it’s why guides like this were created – to educate you on what they are and how they work.

We’ve got plenty to talk about vector embeddings and how they do their job. Rather than ramble onward with this intro, let’s move on and discuss this cool little thing that makes RAG pipelines work.

The Significance of Vector Embeddings in RAG Pipelines

Do you know how AI gets its ability to convert text and unstructured data into a numerical vector? The solution: vector embeddings. This is even more exciting because it is going to be awesome for RAG pipelines, especially when they need to pull information from large data lakes rapidly and well.

What are Vector Embeddings?

Vector embeddings are data points in a sense. But they are more responsible for mapping out the data that travels to and from all kinds of vectors. The purpose of the mapping is by building relationships between words – which in turn can lead to AI models having a much better understanding and easier time to process natural language.

Why Vector Embeddings are Crucial for RAG Pipelines

Vector embeddings are much needed for RAG pipelines for retrieval purposes. Imagine (if you will) a dog being told to get the stick when it’s thrown. Likewise, you tell an AI what to do with a prompt and vector embeddings will go to work on retrieving the necessary data to help create an accurate response. The pipelines are all connected through various datasets and will ensure that such responses are given accurately and reliably.

Choosing the Right Vector Embedding Model

Now here comes the fun part (or not so fun, if you so choose). What vector embedding model is best suited for your needs and preferences. Take a moment to go over the following list and decide which one will be fitting for you. These include but are not limited to the following:

OpenAI v3 Models

The OpenAI v3 models consistently perform very well in real world use cases. On the MTEB leaderboard, they lag behind others on benchmarks. However, for AI engineers who are already using OpenAI for their large language models they can be a convenient option.

OpenAI v3 comes in a large and small version with variable dimension sizes.

Voyage AI Models

Voyage AI has consistently been a pioneer in new embedding models. Their new models very often take the top spot on overall benchmark performance. The remarkable thing about VoyageAI models is that they are able to achieve top performance with a relatively small 1,204 dimension vector. This smaller vector size creates a favorable total cost of ownership when you factor in vector database costs.

GBE Models

Developed by the Beijing Academy of Artificial Intelligence, models such as bge-large-en-v1.5 have shown impressive performance on benchmark scores. The bge-en-icl model has achieved state-of-the-art (SOTA) performance on both BEIR and AIR-Bench.

Implementing Your Choice in a RAG Pipeline

Did you choose the best vector embedding tool that is fit for you? Great! Now it’s time to implement it into your RAG pipeline accordingly. Let’s walk you through the entire process.

Data Preparation

First, it’s vital to have all the unstructured data necessary for processing. Make sure you have it cleaned, segmented into smaller groups for efficiency sake, and be ready for action.

Model Training

Next, train the vector embedding model for it to function properly. This will depend on the tool that you chose. From there, be sure to test the embeddings and see if they are accurate when it comes to reflecting the semantic relationship between it and the data you are using.

Integration into the RAG Pipeline

With your vector embeddings ready, the final step is integrating them into your RAG pipeline. This involves setting up the retrieval mechanism to leverage the embeddings for efficient data retrieval.

Successfully implementing vector embeddings in your RAG pipeline can significantly enhance the performance of your AI applications, enabling them to retrieve and process information with unprecedented accuracy and efficiency.

Conclusion

Vector embeddings are at the heart of RAG pipelines, transforming unstructured data into a format that AI models can effectively process. The choice of vector embedding model is critical, with each model offering distinct advantages and challenges. By carefully selecting and implementing the right model, you can unlock the full potential of your RAG pipeline, driving significant improvements in your AI applications.