The Hitchhiker's Guide to Vector Embeddings

Generative AI (GenAI) has opened up new use cases for developers, including intelligent agents, content creation experiences, synthetic data, language translation, and more. Underpinning these applications are vector embeddings, which allow developers to operate on unstructured data like natural language queries. Unstructured data represents everything from documents to videos and audio files, and GenAI apps rely heavily on it. Vector embeddings represent data in a multidimensional space where semantically similar pieces of content reside close together. Machine learning models are used to convert unstructured data into vector embeddings, with popular providers like OpenAI offering various text embedding models. Selecting the right model is crucial for building successful GenAI apps, considering factors such as relevance, language support, domain specificity, latency, and cost. Once vector embeddings are generated, they should be stored and managed in a vector database designed to handle high-dimensional vectors efficiently. Astra Vectorize simplifies the process by enabling developers to perform CRUD operations directly with unstructured data without having to manually build and maintain intermediate data structures like vectors.