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Curious about which RAG technique suits your project best? Here I Compare two chatbots and examine eight techniques from #Langchain and #llamaindex, I'll guide you through designing a pipeline to evaluate your RAG system's performance efficiently. And that's just the beginning! We'll analyze their effectiveness across various documents and tackle over 40 questions, putting these techniques to a tough test. 00:00 Intro 01:54 Demo of the 2 RAG chatbots 04:30 Sentence retrieval explanation 05:03 Auto-merging retrieval explanation 06:35 Basic RAG explanation 07:09 Page-wise RAG explanation 07:50 Different Langchain chunking and retrieval options 08:35 Other RAG frameworks 09:56 Evaluation pipeline explanation 10:04 Document preparation 13:58 First keynote about RAG evaluation (metrics) 14:40 Python environments 16:15 Selecting the proper temperature for RAG systems 18:05 Vectordb preparation 25:59 Executing RAG techniques on our questions 28:08 Second keynote about RAG systems (Prevent hallucination) 32:27 Run GPT for scoring 36:12 Analysis 44:57 Running tests on RAG chatbots 53:38 Third keynote (RAG optimization) GitHub repositories: \ud83d\udd17 LLM-zero-to-hundred Project: https://github.com/Farzad-R/LLM-Zero-to-Hundred \ud83d\udd17 RAGMASTER Project: https://github.com/Farzad-R/LLM-Zero-to-Hundred/tree/master/RAGMaster-LlamaIndex-vs-Langchain My Linkedin: https://www.linkedin.com/in/farzad-roozitalab/ Check out the papers used in this video: \ud83d\udd17 https://arxiv.org/abs/2312.10997v1 \ud83d\udd17 https://arxiv.org/abs/2312.10997 Plus, here are some handy resources from Langchain and llama-index to keep you on track: \ud83d\udcda Langchain: documentation: https://python.langchain.com/docs/get_started/introduction llama-index: \ud83d\udcda Llama-index documentation: https://docs.llamaindex.ai/en/stable/ \ud83c\udf93 Free courses: https://www.deeplearning.ai/short-courses/ #RAGTechniques #Chatbots #Langchain #llamaIndex #RAGSystem #EvaluationPipeline #chatgpt #gpt #RAG