Weaviate

Weaviate is an open-source AI-native vector database platform that enables developers to build and deploy applications with advanced search, retrieval-augmented generation (RAG), and persistent memory capabilities. At its core, Weaviate provides a scalable vector database that stores high-dimensional embeddings alongside traditional data, allowing efficient similarity search across billions of objects. It supports multiple vector indexing algorithms (such as HNSW) and hybrid search that combines vector similarity with keyword-based BM25 filtering, giving precise control over relevance ranking. The platform integrates natively with popular machine learning frameworks and embedding models, including OpenAI, Cohere, Hugging Face, and custom ONNX models. Developers can configure automated vectorization pipelines: when data is added or updated, Weaviate automatically computes and stores embeddings using user-defined modules. This removes the need for external embedding management and ensures real-time index updates. Weaviate is designed for RAG workflows. It can store chunks of documents, metadata, and their vector representations. When a query arrives, the system retrieves the most relevant chunks via vector similarity, then passes them along with the query to a large language model (LLM) for answer generation. This reduces hallucination because the LLM grounds its outputs on retrieved facts. The platform also includes a built-in memory layer (called Engram) that enables applications to recall user-specific context across sessions—useful for chatbots, personal assistants, and adaptive interfaces. For data modeling, Weaviate uses a graph-like schema where each object belongs to a class and can have cross-references. This allows representation of relationships (e.g., "Author