Weaviate

Overview

Weaviate is an open-source, AI-native vector database designed to store and index both data objects and their vector embeddings. This architecture enables advanced semantic search capabilities by comparing the meaning encoded in vectors rather than relying solely on keyword matching. Key capabilities include semantic and hybrid search, Retrieval Augmented Generation (RAG), and agent-driven workflows. It offers language agnostic SDKs (Python, Go, TypeScript, JavaScript) and connects to GraphQL or REST APIs. Weaviate supports seamless integration of ML models, or the use of a built-in embedding service. Its architecture adapts to any workload, scaling seamlessly while optimizing costs. It can be deployed securely in the cloud or on-premise, meeting enterprise requirements like RBAC, SOC 2, and HIPAA. Weaviate simplifies AI application development by providing AI-first features under one roof, avoiding complex data pipelines and custom code.

Common tasks

Vector Search Semantic Search RAG AI Agents

FAQ

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What is Weaviate?

Weaviate is an open-source vector database designed to store and index data objects and their vector embeddings, enabling advanced semantic search capabilities.

What are the key features of Weaviate?

Key features include semantic and hybrid search, Retrieval Augmented Generation (RAG), agent-driven workflows, billion-scale architecture, and enterprise-ready deployment options.

How does Weaviate handle scalability?

Weaviate adapts to any workload and scales seamlessly as you grow up or out, all while optimizing costs, using a billion-scale architecture.

What deployment options are available for Weaviate?

Weaviate can be deployed in the cloud (Weaviate Cloud), on-premise (using Docker or Kubernetes), or embedded directly within Python or JavaScript applications.

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