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The vector database to build knowledgeable AI | Pinecone Before Pinecone, Vanguard’s customer support teams relied on keyword-based search solutions to search for documents where answers to a customer’s question may live With Pinecone and hybrid retrieval, they boosted customer support with faster calls and 12% more accurate responses
Introduction to Pinecone Vector Database - GeeksforGeeks Pinecone is a fully managed vector database designed for machine learning and AI applications It helps you store, index and search high dimensional vector embeddings like those from text, images or user data quickly and accurately
Pinecone Database | Pinecone Reliable With a 99 95% uptime SLA, Pinecone is trusted by leading startups and enterprises for their production AI applications
Pinecone Vector Database: A Complete Guide - Airbyte Pinecone is a fully managed vector database built for fast similarity search at scale It uses vector embeddings to deliver sub-second queries across millions of data points, making it ideal for semantic search, recommendation systems, and RAG applications
Pinecone - GitHub The vector database for machine learning applications Build vector-based personalization, ranking, and search systems that are accurate, fast, and scalable - Pinecone
What Is Pinecone? A Beginner’s Guide to Vector Databases Pinecone is a powerful vector database that simplifies search for AI applications It offers unmatched speed and scalability for projects related to natural language processing, semantic search, or recommendation systems
Pricing | Pinecone The Dedicated plan offers Bring-your-own-cloud (BYOC), allowing you to deploy a private Pinecone region, giving you the security and control of a self-hosted solution while retaining the seamless experience of our fully managed SaaS product
Pinecone documentation - Pinecone Docs Pinecone is the leading vector database for building accurate and performant AI applications at scale in production
Quickstart - Pinecone Docs Use a Pinecone SDK to create an index, upsert data, and perform semantic search To get started in your browser, use the Quickstart colab notebook