Weaviate is an open-source vector database that enables storing, indexing, and searching data using semantic similarity for AI and machine learning applications.
Weaviate is an open-source, AI-native vector database designed to power semantic search, recommendation systems, and intelligent applications. It enables organizations to store, index, and query both unstructured and structured data using vector embeddings, making it easier to build applications that understand context, meaning, and similarity rather than relying solely on keyword matching. Its primary purpose is to provide a scalable, low-latency foundation for AI workloads that reduces hallucinations, data leakage risks, and dependence on a single model or vendor.
Weaviate supports hybrid search (combining vector and keyword search), modular vectorization, and integration with multiple embedding providers and large language models, including OpenAI, Cohere, and open-source models. It offers a flexible schema, GraphQL and REST APIs, and a robust filtering system that allows complex queries across high-dimensional vectors and metadata. The database is optimized for horizontal scalability and high availability, with support for sharding, replication, and cloud-native deployment options. Built-in security features, multi-tenancy, and configurable data isolation help organizations manage sensitive information while maintaining performance.
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