Weaviate
Enables fast, scalable, and robust vector search for objects and vectors, combining vector search with structured filtering in a cloud-native environment.
About
Weaviate is an open-source, cloud-native vector database designed for high performance and scalability. It leverages state-of-the-art machine learning models to transform diverse data types like text and images into searchable vectors, allowing for lightning-fast approximate nearest neighbor searches on millions of objects. Weaviate provides flexibility by either vectorizing data at import time or allowing users to upload pre-vectorized data, and it is highly extensible through modules that integrate with popular services and model hubs like OpenAI and HuggingFace. Built with production in mind, Weaviate offers native scaling, replication, and security, empowering users to go from rapid prototyping to large-scale deployment with ease, and extends beyond basic search to support recommendations, summarization, and integration with neural search frameworks.
Key Features
- Combines vector search with structured filtering for nuanced data querying.
- 13,594 GitHub stars
- Production-ready with built-in features for scaling, replication, and security.
- Extensible through modules for integration with popular ML services and model hubs (e.g., OpenAI, HuggingFace).
- High-speed Approximate Nearest Neighbor (ANN) search on millions of objects in milliseconds.
- Flexible data vectorization supporting auto-vectorization at import or uploading custom vectors.
Use Cases
- Building AI-powered search engines for text, images, or multimodal data.
- Developing recommendation systems and automatic classification applications.
- Serving as a long-term memory backend for Large Language Models (LLMs) and AI agents (e.g., Auto-GPT, LangChain).
