Weaviate

Weaviate is an open source, cloud-native vector database designed for AI-powered applications that need to store, index, and query vector embeddings at scale. As one of the most mature and feature-rich vector databases available, Weaviate provides semantic search, generative search, hybrid search, multi-tenancy, and a flexible schema system that makes it suitable for a wide range of AI and machine learning workloads.

The vector storage and indexing capabilities of Weaviate are built around the HNSW (Hierarchical Navigable Small World) algorithm, which provides approximate nearest neighbor search with excellent recall and query performance at scale. Weaviate also supports a flat (brute-force) index for smaller datasets where exact search is preferred over approximation, and a dynamic index that automatically switches from flat to HNSW based on the number of vectors in a collection.

Weaviate's schema system allows developers to define collections (formerly classes) with typed properties and vector configurations. Each collection can be associated with a vectorizer module that automatically generates embeddings from text or other data using models from Hugging Face, OpenAI, Cohere, Google, and other providers. This automatic vectorization means that developers can store objects in Weaviate without pre-computing embeddings separately, simplifying the development of RAG pipelines and semantic search applications.

Hybrid search in Weaviate combines vector-based semantic search with keyword-based BM25 search to provide search results that excel in both semantic relevance and keyword precision. The fusion of the two ranking signals produces results that outperform either approach alone for many real-world search scenarios. Reranking with cross-encoder models is available for further improving the quality of search results at query time.

Generative search is a powerful feature that combines vector retrieval with a language model in a single API call. By specifying a generative module (OpenAI, Cohere, Anthropic, or others), developers can retrieve relevant objects from Weaviate and pass them directly to an LLM for summarization, question answering, content transformation, or any other generative task, all within a single request.

Multi-tenancy in Weaviate allows a single Weaviate instance to serve many isolated tenants, each with their own data, indexes, and access controls. This is a critical feature for SaaS applications that need to store and search user-specific data in an isolated, scalable manner. Tenant isolation is enforced at the storage level, preventing any cross-tenant data access.

Weaviate's GraphQL and REST APIs provide flexible query interfaces that support filtering by property values, geolocation, and vector similarity. The Python, JavaScript, TypeScript, Go, and Java client libraries provide idiomatic access to Weaviate for the most popular languages in the ML and backend development communities.

Weaviate can be deployed locally using Docker, on Kubernetes using Helm charts, or through the Weaviate Cloud (WCD) managed service. The embedded Weaviate option allows running Weaviate directly within a Python process for development and testing without any external service setup.