Weaviate

Weaviate is a database designed specifically for AI applications that need to understand the meaning behind data. Unlike regular databases that only match exact words, Weaviate can find information based on what you mean, not just what you type.

It combines traditional database features with vector search technology. This means you can store documents, images, or any type of data, and then search through them using natural language or by similarity. The database handles millions or even billions of items quickly, usually returning results in less than 100 milliseconds.

You can use it for many purposes: building chatbots that answer questions accurately, creating search engines that understand context, making recommendation systems, or organizing large amounts of unstructured data like documents and images.

Getting started with Weaviate involves a few simple steps:

• Choose your deployment option. You can start with a free cloud trial at Weaviate Cloud, which sets up everything automatically. Or install it yourself using Docker on your computer or server.

• Connect your AI models. Weaviate works with many providers like OpenAI, Cohere, and Google. You can also upload your own pre-made vectors if you prefer.

• Set up your data structure by creating collections. Think of collections like tables in a regular database, where you define what type of information you want to store.

• Load your data into Weaviate. The system automatically converts your text, images, or other content into vectors that enable smart searching.

• Start searching using simple code. You can search by meaning, by keywords, or combine both approaches for better results. Weaviate also lets you filter results and generate answers using AI.

• Fast vector search across billions of items

• Hybrid search with keyword and semantic capabilities

• Direct integration with major AI providers

• Open source and self-hosted options available

• Built-in data backup and recovery

• Multi-language support and filtering

• Real-time data updates

• Horizontal scaling for large datasets

• RESTful and GraphQL APIs

• Python, JavaScript, Go, and Java client libraries