A RAG (Retrieval-Augmented Generation) system can utilize a vector database as a key component, but it is not necessarily a vector database itself.
A vector database is a type of database that stores and manages vector embeddings, which are dense vector representations of data, such as text or images.
In a RAG system, a vector database can be used to store and manage the vector embeddings of the knowledge graph, allowing for efficient and scalable retrieval of relevant information.
However, a RAG system also includes additional components, such as the generation model, the retrieval model, and the ranking model, which work together to generate text based on the retrieved information.
Some popular vector databases that can be used in a RAG system include:
- Faiss: A library for efficient similarity search and clustering of dense vectors.
- Pinecone: A managed vector database service that provides scalable and secure vector search.
- Weaviate: A cloud-native, open-source vector search engine that provides scalable and flexible vector search.
By utilizing a vector database, a RAG system can efficiently store and manage large amounts of data, and quickly retrieve relevant information to generate text.
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