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in this video weamp;#39;re going to learn how to evaluate the retrieval part of a rag pipeline weamp;#39;re going to do this using a library called ranks which defines itself as a library of fast ranking evaluation metrics implemented in Python leveraging number for high-speed Vector operations and automatic parallelization it has all the metrics youamp;#39;d expect you can do hits hits ratio Precision recall F1 and so on letamp;#39;s launch IPython and then weamp;#39;re going to import some things from the ranks Library weamp;#39;re going to start by initializing the curlamp;#39;s dictionary and so these are query relevance judgments and so in our dictionary weamp;#39;re going to have a key so for example q1 thatamp;#39;s the question like number and then as the value weamp;#39;re going to have a dictionary which is going to contain the documents that we expect to come back so in here weamp;#39;re going to say itamp;#39;s going to be D12 and weamp;#39;re just going to say
