Several vector databases have gained popularity due to their ability to efficiently handle high-dimensional vectors and support vector search. One such database is Pinecone, which offers a managed service for building vector search applications. Pinecone provides scalable and low-latency search capabilities, making it ideal for applications that require real-time data retrieval.
Another popular choice is Milvus, an open-source vector database designed for similarity search. Milvus supports a wide range of machine learning models and can handle both structured and unstructured data. It excels in managing large-scale data sets, offering high recall and precision in search results.
Weaviate is also a well-regarded vector database, known for its ability to handle multimodal data. It integrates seamlessly with existing systems, allowing for easy data partitioning and indexing. Weaviate's flexibility makes it suitable for various use cases, from semantic search to question answering systems.
These vector databases are instrumental in advancing the capabilities of AI-driven applications, providing the necessary infrastructure to support efficient and accurate vector search.