Skip to main content

Weaviate

Overview​

Weaviate is an open-source vector database designed for managing and processing embedding vectors generated by machine learning models and neural networks. It's built to handle similarity searches and analytics over massive datasets with high performance and scalability.

Key Features​

  • Scalability: Weaviate is designed to scale horizontally, allowing it to handle billions of vectors.
  • High Performance: It leverages optimized indexing techniques and parallel processing to deliver fast query performance.
  • Real-time Search: Supports real-time search and retrieval of vectors.
  • Dynamic Data Management: Enables dynamic insertion, deletion, and updating of vectors.
  • Multiple Index Types: Offers a variety of indexing methods, including IVF, HNSW, and ANNOY, to optimize search performance for different datasets and query requirements.
  • Cloud Native: Built to be cloud-native, making it easy to deploy and manage on Kubernetes and other cloud platforms.

Use Cases​

Weaviate is suitable for a wide range of applications, including:

  • Recommendation Systems: Finding similar items based on user preferences or item embeddings.
  • Image and Video Retrieval: Searching for similar images or videos based on visual features.
  • Natural Language Processing (NLP): Semantic search, question answering, and text similarity analysis.
  • Drug Discovery: Identifying similar molecules based on chemical properties.
  • Fraud Detection: Detecting fraudulent transactions based on behavioral patterns.

Installation​

You can install Weaviate using Docker or by building from source. Here's how to install it using Docker: