All 28 Dependencies
Every package Weaviate depends on, ranked by repo health score.
Weaviate is an open-source, cloud-native vector database that stores both objects and their vector embeddings in a unified store, enabling semantic search, hybrid queries, and retrieval-augmented generation at scale. It is written entirely in Go and uses HNSW (Hierarchical Navigable Small World) graphs for approximate nearest neighbor search, delivering millisecond query times even over billions of vectors.
The system integrates directly with leading embedding providers including OpenAI, Cohere, HuggingFace, Google, and dozens more, so data can be vectorized automatically at import without external preprocessing pipelines. Alternatively, developers can bring their own pre-computed vectors for full control over the embedding strategy. Hybrid search combining BM25 keyword matching with semantic similarity is built in and available in a single API call.
Weaviate is built for production from the ground up. It ships with native multi-tenancy that physically isolates tenant data at the shard level, configurable replication via Raft consensus, and fine-grained role-based access control. Vector compression using product quantization, scalar quantization, and rotary quantization reduces memory footprint by up to 80% with minimal accuracy loss, making billion-vector deployments economically viable.
Deployment is flexible: run locally via Docker Compose, self-host on Kubernetes, deploy on AWS or GCP Marketplace, or use the fully managed Weaviate Cloud service. Client libraries are available for Python, JavaScript/TypeScript, Java, Go, and C#, alongside REST, gRPC, and GraphQL APIs.
