Combines vector and keyword search natively, which matters more than it sounds - pure vector search alone often misses exact-match queries a hybrid approach catches easily.
Where weaviate fits, based on its real tags and topics.
git clone https://github.com/weaviate/weaviate.gitSee the repository's README for language-specific setup steps.
[](https://github.com/weaviate/weaviate) [](https://pkg.go.dev/github.com/weaviate/weaviate) [](https://github.com/weaviate/weaviate/actions/workflows/.github/workflows/pull_requests.yaml) [](https://goreportcard.com/report/github.com/weaviate/weaviate) [](https://codecov.io/gh/weaviate/weaviate)
**Weaviate** is an open-source, cloud-native vector database that stores both objects and vectors, enabling semantic search at scale. It combines vector similarity search with keyword filtering, retrieval-augmented generation (RAG), and reranking in a single query interface. Common use cases include RAG systems, semantic and image search, recommendation engines, chatbots, and content classification.
Weaviate supports two approaches to store vectors: automatic vectorization at import using integrated models (OpenAI, Cohere, HuggingFace, and others) or direct import of pre-computed vector embeddings. Production deployments benefit from built-in multi-tenancy, replication, RBAC authorization, and many other features.
Category: 🛢️ Databases
Difficulty: Intermediate
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