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another popular use case is of retrieval augmented generation or rag in here we are going to provide a file and this is a PDF and then we go through the steps of embedding the document and then asking questions so in this flow weamp;#39;re going to take the document split the text and use go here embeddings and we have options to select from in terms of the model so there is the English model there are some multilingual models since this document is in English Iamp;#39;m going to select the English option and then run the flow once I run the flow I can see that there are a number of embeddings those were generated and also saved now we can use the embedding so in this case I use chroma DB which is a local Vector database I am going to connect that with the embeddings again and a question from the users in this case Iamp;#39;m asking summarize the document for me please essentially our question goes to chroma DB component and we search using the query we get a few different responses
