AI Database Examples: Vector Search, Recommendations & LLM Workflows with SingleStore

Choosing an AI database like SingleStore involves more than just benchmarks; true value comes from building and experimenting with it in real-world scenarios. SingleStore supports innovative features such as vector search, hybrid analytics, and managing the lifecycle of large language models (LLMs) in production-like workflows. It enables semantic and vector search by allowing embeddings to be stored in vector columns and retrieved with vector similarity in SQL, aiding in tasks like real-time recommendations and personalization by blending vector similarity with transactional and behavioral features. SingleStore also supports multimodal applications that handle text, audio, and images by layering retrieval over both structured and unstructured data, which can be particularly useful for applications such as virtual assistants or language translation. The platform facilitates the evaluation and fine-tuning of LLMs by offering a notebook-driven workflow for running repeatable tests, helping refine model accuracy and response style. SingleStore's ability to ingest and transform data quickly, persist vectors in a native type, and maintain a consistent data model when moving from a notebook to a service makes it an appealing choice for AI workloads, enabling various use cases like fraud detection, autonomous systems, and real-time machine learning integration.