Assess
Weaviate is an open-source vector database designed for scalable AI-powered applications. It excels at storing and searching through vector embeddings, enabling semantic search, recommendation systems, and other AI features through its advanced vector similarity capabilities.
Key Capabilities:
- Fast and accurate vector similarity search using HNSW indexing
- Hybrid search combining vector and keyword-based (BM25) approaches
- Multi-modal support for text, images, audio, and video data
- Built-in integrations with popular AI models and embedding providers
- GraphQL and REST APIs for easy development
- Support for multi-tenancy and role-based access control
- Vector quantization for efficient storage
- Horizontal scaling through database sharding
MOHARA should assess Weaviate for its potential use in AI applications requiring semantic search, particularly for projects involving unstructured data retrieval and RAG (Retrieval Augmented Generation) implementations. While powerful, careful evaluation is needed regarding scalability requirements and operational complexity.