Weaviate
An open-source vector database that combines vector search with traditional filtering, designed for AI applications.
In-Depth Explanation
Weaviate is an open-source vector database designed for AI applications, particularly semantic search and RAG systems. It offers both self-hosted and cloud deployment options.
Key features:
- Vector + keyword search: Hybrid search combining both approaches
- Modules system: Built-in vectorization with OpenAI, Cohere, etc.
- GraphQL API: Flexible querying
- Multi-tenancy: Isolated data per customer
- Generative search: Built-in RAG capabilities
- Schema-free or schema-based: Flexibility in data modeling
Technical capabilities:
- HNSW indexing for fast approximate search
- BM25 for keyword search
- Automatic vectorization with integrated models
- Metadata filtering during search
- Cross-references between objects
Deployment options:
- Self-hosted (Docker, Kubernetes)
- Weaviate Cloud (managed service)
- Hybrid deployments
Business Context
Weaviate offers flexibility with open-source self-hosting or managed cloud, suitable for teams wanting control over their vector infrastructure.
How Clever Ops Uses This
We deploy Weaviate for Australian businesses needing vector search with more control or cost efficiency than fully managed alternatives.
Example Use Case
"Self-hosting Weaviate for a document search system, using its built-in OpenAI integration for automatic embedding generation."