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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:
🔗 LLM-zero-to-hundred Project: github.com/Far...
🔗 RAGMASTER Project: github.com/Far...
My Linkedin: / farzad-roozitalab
📚 The papers used in this video:
🔗 arxiv.org/abs/...
🔗 arxiv.org/abs/...
📚 Extra Resources for Langchain and llama-index:
Langchain: documentation: python.langcha...
llama-index:
Llama-index documentation: docs.llamainde...
🎓 Free courses: www.deeplearni...
#RAGTechniques #Chatbots #Langchain #llamaIndex #RAGSystem #EvaluationPipeline #chatgpt #gpt #RAG