While the RAG technique doesn't require the use of embeddings or vector databases, adding an information retrieval component to your RAG system can improve overall performance of compound AI systems. To create a vector database, first the documents -or data is converted into a mathematical representation of meaning, a text embedding, -that is a set of numeric vectors that are used for matching and creating relationships.
+or data is converted into a mathematical representation as a text embedding, +or set of numeric vectors, that are used for matching and creating relationships.These text embeddings are then stored in a database or datastore and then can be queried by first converting a query into a text embedding and then finding the stored embeddings that most closely match the query. The resulting embeddings are then converted back to diff --git a/exploring-llms/retrieval-augmented-generation.md b/exploring-llms/retrieval-augmented-generation.md index a04212d..acd5563 100644 --- a/exploring-llms/retrieval-augmented-generation.md +++ b/exploring-llms/retrieval-augmented-generation.md @@ -16,8 +16,8 @@ better contextualizing the generative abilities of these models. While the RAG technique doesn't require the use of embeddings or vector databases, adding an information retrieval component to your RAG system can improve overall performance of compound AI systems. To create a vector database, first the documents -or data is converted into a mathematical representation of meaning, a text embedding, -that is a set of numeric vectors that are used for matching and creating relationships. +or data is converted into a mathematical representation as a text embedding, +or set of numeric vectors, that are used for matching and creating relationships. These text embeddings are then stored in a database or datastore and then can be queried by first converting a query into a text embedding and then finding the stored embeddings