Weaviate is an open-source vector database that stores both objects and vectors. This allows for combining vector search with structured filtering, making it a powerful tool for semantic search and other AI-powered applications.
Weaviate is built on top of the Go programming language and uses a GraphQL API to access data. This makes it easy to integrate with other AI tools and applications.
Some of the key features of Weaviate include
Vector search Weaviate can perform lightning-fast pure vector similarity search over raw vectors or data objects, even with filters. This makes it ideal for applications where it is important to find the most relevant results, such as product search, customer support, and fraud detection.
Hybrid search Weaviate can also combine keyword-based search with vector search techniques for state-of-the-art results. This is a powerful way to find results that match both the user's query terms and the semantic meaning of the data.
Generative search Weaviate can be used in conjunction with generative models to create new content that is relevant to the user's search query. This can be used for applications such as question answering, summarization, and creative writing.
Weaviate is a powerful tool that can be used for a variety of AI-powered applications. It is open source, scalable, and easy to use, making it a great choice for developers and businesses of all sizes
