Main Purpose
Weaviate is an open-source vector database that combines vector search with structured filtering. It provides a cloud-native database accessible through GraphQL, REST, and various language clients.
Key Features
- Vector Database: Weaviate is designed to store both objects and vectors, enabling efficient vector search and retrieval.
- Vector Search: Weaviate allows users to perform vector-based searches to find similar objects based on their semantic meaning.
- Structured Filtering: Users can apply structured filters to narrow down search results based on specific criteria.
- Cloud-Native: Weaviate is built to be fault-tolerant and scalable, making it suitable for cloud-based deployments.
- Multiple Access Methods: Weaviate supports GraphQL, REST, and various language clients, providing flexibility for developers to interact with the database.
Use Case
- Semantic Search: Weaviate can be used in applications that require semantic search capabilities, such as recommendation systems, content discovery, or similarity matching.
- Data Enrichment: Weaviate can help improve the quality of data by using content generated by Large Language Models (LLMs) to enrich datasets, reducing the need for manual data cleaning.
- Cloud-Native Applications: Weaviate's fault-tolerance and scalability make it suitable for building cloud-native applications that require efficient vector search and structured filtering.
