weaviate and awesome-weaviate
The main project provides a production vector database system, while the curated collection serves as a community resource for discovering integrations, examples, and extensions—making them ecosystem siblings where one is the core technology and the other documents its surrounding tools and use cases.
About weaviate
weaviate/weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of a cloud-native database.
Built in Go for millisecond-scale performance on billions of vectors, Weaviate integrates vectorization from major providers (OpenAI, Cohere, HuggingFace) at import time or accepts pre-computed embeddings. It unifies semantic search, BM25 keyword filtering, image search, and generative RAG/reranking in a single query interface, with production features including horizontal scaling, multi-tenancy, replication, and RBAC for enterprise deployments.
About awesome-weaviate
weaviate/awesome-weaviate
Awesome Weaviate
A curated collection of examples, tutorials, and resources for Weaviate, a vector database that performs semantic search using machine learning embeddings and GraphQL APIs. The repository indexes blog posts, Colab notebooks, conference talks, and demo datasets covering use cases from Wikipedia semantic search to knowledge graph queries across millions of vectors. It serves as a learning hub for integrating Weaviate with transformer models, custom ML pipelines, and building production neural search applications.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work