How to evaluate LlamaIndex RAG with OpenAI model🔥: Python — LlamaIndex #3

In today’s tutorial, we're going to use LlamaIndex with an OpenAI model to evaluate the travel recommendation RAG that we built in the previous LlamaIndex tutorial. I'll show you an example in Python. ⭐ Code ⭐ ⦁ GitHub repository: https://github.com/rokbenko/ai-playground ⦁ Code for this tutorial: https://github.com/rokbenko/ai-playground/tree/main/llamaindex-tutorials/3-Eval_travel_recommendation_RAG 🙌 Support 🙌 ⦁ Subscribe: https://www.youtube.com/@rokbenko ⦁ Keep tutorials brewing: https://buymeacoffee.com/rokbenko 🌐 Profiles 🌐 ⦁ GitHub: https://github.com/rokbenko ⦁ LinkedIn: https://www.linkedin.com/in/rokbenko ⦁ X: https://x.com/rokbenko 🎞️ Timestamps 🎞️ 00:00 – Intro 00:14 – Code for this tutorial 00:30 – Previous tutorial 01:10 – Install LlamaIndex and Streamlit 01:17 – Load documents 01:30 – Generate evaluation questions 02:03 – Take the first 3 questions 02:11 – Create a vector index 02:16 – Initialize the OpenAI model 02:33 – LlamaIndex evaluators explained 02:57 – Initialize LlamaIndex evaluators 03:30 – Async function for evaluation 03:55 – Save evaluation results 04:01 – Extract evaluation results 04:21 – Create a pandas DataFrame and save it to an Excel file 04:27 – Run the Python example 04:39 – Excel file examination 04:59 – Outro #AI #LlamaIndex #OpenAI #RAG #Eval