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articles/cosmos-db/vector-database.md

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@@ -33,7 +33,7 @@ There are two common types of vector database implementations - pure vector data
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A pure vector database is designed to efficiently store and manage vector embeddings, along with a small amount of metadata; it is separate from the data source from which the embeddings are derived.
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A vector database that is integrated in a highly performant NoSQL or relational database provides additional capabilities. The integrated vector database converts the existing data in a NoSQL or relational database into embeddings and stores them alongside the original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, this architecture keeps the vector embeddings and original data together, which better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance.
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A vector database that is integrated in a highly performant NoSQL or relational database provides additional capabilities. The integrated vector database in a NoSQL or relational database can store, index, and query embeddings alongside the corresponding original data. This approach eliminates the extra cost of replicating data in a separate pure vector database. Moreover, keeping the vector embeddings and original data together better facilitates multi-modal data operations, and enables greater data consistency, scale, and performance.
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## What are some vector database use cases?
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