Weaviate in a nutshell: Weaviate is a vector search engine and vector database. Weaviate uses machine learning to vectorize and store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale. Weaviate in detail: Weaviate is a low-latency vector search engine with out-of-the-box support for different media types (text, images, etc.). It offers Semantic Search, Question-Answer-Extraction, Classification, Customizable Models (PyTorch/TensorFlow/Keras), and more. Built from scratch in Go, Weaviate stores both objects and vectors, allowing for combining vector search with structured filtering with the fault-tolerance of a cloud-native database, all accessible through GraphQL, REST, and various language clients.
Features
- Out-of-the-box modules for: NLP/semantic search, automatic classification and image similarity search
- Easy to integrate into your current architecture, with full CRUD support like you're used to from other OSS databases
- Cloud-native, distributed, runs well on Kubernetes and scales with your workloads
- Weaviate has a modular setup that allows you to use your ML models inside Weaviate, but you can also use out-of-the-box ML models (e.g., SBERT, ResNet, fasttext, etc)
- Weaviate takes care of the scalability, so that you don't have toDeploy and maintain ML models in production reliably and efficiently
Project Samples
