Pinecone is a managed, cloud-native vector database offering long-term memory for high-performance AI applications. Credit: HighDispersion / Shutterstock Microsoft has announced the Pinecone .NET SDK, for building AI applications that leverage the Pinecone vector database. With the SDK, Pinecone is becoming the newest member of the AI ecosystem in .NET, Microsoft said. Building AI applications requires efficient vector data processing, Microsoft said in an August 27 blog post. A vector database stores and indexes embedding vectors for fast retrieval and similarity search. Embeddings are numerical representations of data such as images, text, and audio, capturing semantic meaning and relationships, thus making them essential in AI applications. With the complexity of vector embeddings, a database is needed that is designed specifically for this data type. To start working with Pinecone in .NET, developers either must have or must set up a Pinecone account and database and create an API key. They can download the Pinecone SDK from NuGet. Afterward, they can connect the .NET client to their Pinecone database Pinecone offers long-term memory for high-performance AI applications, according to the Pinecone documentation. It is a managed, cloud-native vector database with a streamlined API and no infrastructure hassles, said proponents. Relevant query results are served. The database is billed by Microsoft as a robust vector database designed to efficiently handle and query large-vector data. Data scientists and engineers leveraging Pinecone can build vector-based AI applications that require efficient similarity search and ranking capabilities. Related content feature Dataframes explained: The modern in-memory data science format Dataframes are a staple element of data science libraries and frameworks. Here's why many developers prefer them for working with in-memory data. By Serdar Yegulalp Nov 06, 2024 6 mins Data Science Data Management analysis Cloud providers make bank with genAI while projects fail Generative AI is causing excitement but not success for most enterprises. This needs to change quickly, but it will take some work that enterprises may not be willing to do. By David Linthicum Nov 05, 2024 5 mins Generative AI Cloud Computing Data Management feature Overcoming data inconsistency with a universal semantic layer Disparate BI, analytics, and data science tools result in discrepancies in data interpretation, business logic, and definitions among user groups. A universal semantic layer resolves those discrepancies. By Artyom Keydunov Nov 01, 2024 7 mins Business Intelligence Data Management feature Bridging the performance gap in data infrastructure for AI A significant chasm exists between most organizations’ current data infrastructure capabilities and those necessary to effectively support AI workloads. By Colleen Tartow Oct 28, 2024 12 mins Generative AI Data Architecture Artificial Intelligence Resources Videos