By Phil Miesle Cosine similarity has several real-world applications, and by using embedding vectors, we can compare real-world meanings in a programmatic manner. Python is one of the most popular languages for data science, and it offers various libraries to calculate cosine similarity with ease. In this article, we’ll discuss…

By James Gallagher, Roboflow, and Aaron Ploetz, DataStax Embeddings have taken the fields of natural language processing and computer vision by storm. Embeddings are a numeric representation of images and text that encode semantics. This property has a range of applications. For example, you can use image embeddings to find…

By David Dieruf When you refer to an application as “AI,” that usually means it includes interactions with a learning model (like a large language model, or LLM). The [not so] interesting fact is that the use of an LLM is not actually what makes the app intelligent. It’s the…

by Bill McLane With the rapid integration of generative AI into the application development process we are seeing an increasing need to be able to integrate our own private data with public data that is being used to train large language models (LLMs). The challenge this presents is that most…

By Aaron Ploetz DataStax recently participated as a launch partner for LangChain Templates, which are easily deployable reference architectures that anyone can use. Why is this exciting? With DataStax Astra DB and LangChain Templates, it’s easier than ever to build and deploy generative AI applications backed by the power of…

By Charna Parkey We’re fond of saying that there’s no artificial intelligence without data. But it can’t be any kind of data. Take large language models, or LLMs — deep learning models, like OpenAI’s GPT-4 that can generate text that’s quite similar to what a human would write. For LLMs…

By Phil Miesle Cosine similarity is an essential concept in the field of data science, text analysis, and machine learning. If you’ve ever wondered what cosine similarity is or how it’s used in real-world applications, you’re in the right place. This guide aims to give you an in-depth understanding of…

by Alejandro Cantarero The use of generative AI and large language models (LLMs) is growing at a very fast pace. Generative AI models are able to create realistic and interactive text, images, video, and audio for a variety of problems. Companies are finding many uses for these types of AI…

By Phil Miesle In the dynamic landscape of artificial intelligence, retrieval augmented generation (RAG) has emerged as a game-changer, revolutionizing the way we generate and interact with text. RAG seamlessly marries the power of information retrieval with natural language generation using tools like large language models (LLMs), offering a transformative…