Vector embeddings capture the essence of unstructured information. These embeddings, when combined with a vector database or search algorithm, offer a valuable means of retrieving contextually relevant data for a LLM.

By dynamically linking vector embeddings to specific information in the database, LLMs gain access to an up-to-date and ever-expanding knowledge base. This continuous updating process ensures that the LLMs remain capable of generating accurate and contextually appropriate outputs, even in the face of constantly changing information. As the generated output is being augmented by retrieved context, this approach is sometimes called Retrieval Augmented Generation or (RAG).

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Generative models such as ChatGPT have changed many product roadmaps. Interfaces and user experience can now be re-imagined and often drastically simplified to what resembles a google search bar where the input is natural language. However, some models remain behind APIs without the ability to re-train on contextually appropriate data. Even in the case where the model weights are publicly available, re-training or fine-tuning is often expensive, requires expertise and is ill-suited to problem domains with constant updates. How then can such APIs be used when the data needed to generate an accurate output was not present in the training set because it is consistently changing?

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Let’s face it, the challenge of search today is indexing billions of entries while delivering relevant results quickly. Traditional keyword-based methods have limitations, leaving us searching for a better way to improve search. But what if we could use deep learning to revolutionize search? Imagine representing data as vectors, where the distance between vectors reflects similarity, and using Vector Similarity Search algorithms to search billions of vectors in milliseconds. It’s the future of search, and it can transform text, multimedia, images, recommendations, and more. Check out this panel discussion, where you will learn how you can incorporate vector search into your own applications to harness deep learning insights at scale.

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