LangChain RAG over Youtube Videos | LlaMa-3
Hi, My name is Sunny Solanki, and in this video, I provide a step-by-step guide to building a RAG app that answers questions from YouTube videos. We use the famous LLM Apps building framework LangChain for coding. We access LLM (LlaMa-3-70B) through Groq API. You can easily extend this app and add streamlit front end.
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CODE - https://github.com/sunny2309/langchai...
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SUPPORT US - https://buymeacoffee.com/coderzcolumn
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NEWSLETTER - http://eepurl.com/gRW2u9
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WEBSITE - https://coderzcolumn.com
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Important Chapters:
0:00 - LlaMa-3 RAG QA Engine over Youtube Videos
0:50 - Code Start
2:48 - Load LLM Llama-3 70B
4:42 - Load Youtube Video Transcripts
8:51 - Generate Embeddings
10:22 - Split Documents
11:28 - Build Index in Vector Store (Chroma)
14:21 - Create RAG QA Engine
#python #datascience #datasciencetutorial #python #pythonprogramming #pythoncode #pythontutorial #llama3 #langchain #langchain-llama3 #groqapi #langchain-open-source-llms #langchain-rag #rag-over-videos #rag-app-for-youtube-videos