Introduction

Weaviate is an open-source, AI-native vector database built for developers. It is specifically designed to store and manage data objects and vector embeddings, enabling efficient semantic search, classification, and recommendation through integrated machine learning models. By understanding the context and meaning of your data, Weaviate moves beyond simple keyword matching to deliver powerful, intelligent search and data retrieval capabilities.

Key Features

Weaviate is packed with features that make it a robust solution for modern applications.

• Vector Search: Perform high-speed similarity searches on unstructured data like text, images, and more.
• Hybrid Search: Combine the power of vector (semantic) search with traditional keyword-based (BM25) search for the best results.
• Generative Feedback (Generative Search): Retrieve data and use it as context for integrated generative models to get answers, summaries, and insights.
• Module Ecosystem: Seamlessly integrate with your favorite ML models from OpenAI, Cohere, Hugging Face, and others for vectorization and generation.
• Cloud-Native: Built to be scalable, fault-tolerant, and easy to deploy with Kubernetes (k8s).

Unique Advantages

What sets Weaviate apart from other databases?

• Developer-First: Its GraphQL API is intuitive and designed for a great developer experience, making it easy to integrate and query.
• Real-Time Performance: It is built for speed, offering low-latency data retrieval even at massive scales.
• Data Flexibility: It is not just a vector database; it's a full-fledged data store that can handle your objects and their relationships.
• Zero-Code Deployment: With Weaviate Cloud Services (WCS), you can get a production-ready instance running in minutes without managing infrastructure.

Who is it For?

Weaviate is an ideal solution for a wide range of professionals and use cases.

• Developers building next-generation applications with intelligent search, recommendation systems, or RAG (Retrieval-Augmented Generation).
• Data Scientists who need a powerful and scalable database to productionize their ML models and embeddings.
• Enterprises looking to add semantic understanding to their large volumes of unstructured data for improved user experiences and insights.

Frequently Asked Questions

Is Weaviate just a search engine?
No, it is a full-fledged database that stores both your data objects and their vector embeddings, enabling intelligent search and data management.

How does it integrate with machine learning models?
Weaviate uses a module system. You can use built-in modules or connect to external services to automatically vectorize your data and perform generative tasks.

Can I run Weaviate myself?
Yes, Weaviate is open-source and can be self-hosted. For a managed, hassle-free experience, you can use Weaviate Cloud Services (WCS).