feat: example for rag evaluator pack (#882)

llama_hub/llama_packs/rag_evaluator/example.py

@@ -0,0 +1,27 @@
+# Required Environment Variables: OPENAI_API_KEY
+
+from llama_index.llama_dataset import download_llama_dataset
+from llama_index.llama_pack import download_llama_pack
+from llama_index import VectorStoreIndex
+
+# download a LabelledRagDataset from llama-hub
+rag_dataset, documents = download_llama_dataset(
+    "PaulGrahamEssayDataset", "./paul_graham"
+)
+
+# build a basic RAG pipeline off of the source documents
+index = VectorStoreIndex.from_documents(documents=documents)
+query_engine = index.as_query_engine()
+
+# Time to benchmark/evaluate this RAG pipeline
+# Download and install dependencies
+RagEvaluatorPack = download_llama_pack("RagEvaluatorPack", "./rag_evaluator_pack")
+
+# construction requires a query_engine, a rag_dataset, and optionally a judge_llm
+rag_evaluator_pack = RagEvaluatorPack(
+    query_engine=query_engine, rag_dataset=rag_dataset
+)
+
+# PERFORM EVALUATION
+benchmark_df = rag_evaluator_pack.run()  # async arun() also supported
+print(benchmark_df)