EDGE OSDEFINITION
Vector databases are a type of database specifically built for searching vector embeddings. These are numerical representations of data such as text, images, audio and documents. By using similarity search methods, they can find related information.
They are a key part of many AI tools today as they allow systems to find relevant information using meaning, rather than precise keyword matching. They are often used in Retrieval Augmented Generation (RAG) systems, recommendation systems, semantic search and AI knowledge management systems.
Importance of Vector Databases
- Enable semantic search based on meaning and context.
- Support efficient similarity searches across large datasets.
- Improve the relevance of AI-generated responses.
- Help connect enterprise knowledge with LLM applications.
Use Cases of Vector Databases
- Building retrieval-augmented generation (RAG) systems.
- Creating AI-powered enterprise search platforms.
- Developing product and content recommendation engines.
- Retrieving relevant information from documents and knowledge bases.
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