Overview
Weaviate is an open-source, AI-native vector database designed to store and index both data objects and their vector embeddings. This architecture enables advanced semantic search capabilities by comparing the meaning encoded in vectors rather than relying solely on keyword matching. Key capabilities include semantic and hybrid search, Retrieval Augmented Generation (RAG), and agent-driven workflows. It offers language agnostic SDKs (Python, Go, TypeScript, JavaScript) and connects to GraphQL or REST APIs. Weaviate supports seamless integration of ML models, or the use of a built-in embedding service. Its architecture adapts to any workload, scaling seamlessly while optimizing costs. It can be deployed securely in the cloud or on-premise, meeting enterprise requirements like RBAC, SOC 2, and HIPAA. Weaviate simplifies AI application development by providing AI-first features under one roof, avoiding complex data pipelines and custom code.
Common tasks