What are Vector Databases?
Databases designed to store and query high-dimensional vector embeddings for tasks like semantic search and dense retrieval.
More about Vector Databases:
Vector Databases are specialized databases optimized for storing and querying vector embeddings. These embeddings represent data such as text, images, or audio in a high-dimensional space, allowing for similarity-based retrieval using metrics like cosine similarity.
Vector databases play a crucial role in systems such as semantic search, dense retrieval, and knowledge retrieval, enabling efficient and accurate retrieval of information in large datasets.
Frequently Asked Questions
What are the benefits of using vector databases?
They enable efficient querying of high-dimensional embeddings, essential for semantic search and recommendation systems.
What are some popular vector database tools?
Popular tools include Pinecone, Weaviate, and Milvus, commonly used in retrieval augmentation pipelines.
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