build GPTÔºö from scratch, in code, spelled out..m4a"    [ExtractAudio] Not converting audio /Users/31treehaus/Desktop/AI/langchain-fork/docs/modules/indexes/document_loaders/examples/Let's build GPTÔºö from scratch, in code, spelled out..m4a; file is already in target format m4a    [youtube] Extracting URL: https://youtu.be/VMj-3S1tku0    [youtube] VMj-3S1tku0: Downloading webpage    [youtube] VMj-3S1tku0: Downloading android player API JSON    [info] VMj-3S1tku0: Downloading 1 format(s): 140    [download] /Users/31treehaus/Desktop/AI/langchain-fork/docs/modules/indexes/document_loaders/examples/The spelled-out intro to neural networks and backpropagationÔºö building micrograd.m4a has already been downloaded    [download] 100% of  134.98MiB    [ExtractAudio] Not converting audio /Users/31treehaus/Desktop/AI/langchain-fork/docs/modules/indexes/document_loaders/examples/The spelled-out intro to neural networks and backpropagationÔºö building micrograd.m4a; file is already in target format m4a# Returns a list of Documents, which can be easily viewed or parseddocs[0].page_content[0:500]    "Hello, my name is Andrej and I've been training deep neural networks for a bit more than a decade. And in this lecture I'd like to show you what neural network training looks like under the hood. So in particular we are going to start with a blank Jupyter notebook and by the end of this lecture we will define and train a neural net and you'll get to see everything that goes on under the hood and exactly sort of how that works on an intuitive level. Now specifically what I would like to do is I w"Building a chat app from YouTube video‚ÄãGiven Documents, we can easily enable chat / question+answering.from langchain.chains import RetrievalQAfrom langchain.vectorstores import FAISSfrom langchain.chat_models import ChatOpenAIfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitter# Combine doccombined_docs = [doc.page_content
Building chat or QA applications on YouTube videos is a topic of high interest.
Building chat or QA applications on YouTube videos is a topic of high interest. ->: build GPTÔºö from scratch, in code, spelled out..m4a"    [ExtractAudio] Not converting audio /Users/31treehaus/Desktop/AI/langchain-fork/docs/modules/indexes/document_loaders/examples/Let's build GPTÔºö from scratch, in code, spelled out..m4a; file is already in target format m4a    [youtube] Extracting URL: https://youtu.be/VMj-3S1tku0    [youtube] VMj-3S1tku0: Downloading webpage    [youtube] VMj-3S1tku0: Downloading android player API JSON    [info] VMj-3S1tku0: Downloading 1 format(s): 140    [download] /Users/31treehaus/Desktop/AI/langchain-fork/docs/modules/indexes/document_loaders/examples/The spelled-out intro to neural networks and backpropagationÔºö building micrograd.m4a has already been downloaded    [download] 100% of  134.98MiB    [ExtractAudio] Not converting audio /Users/31treehaus/Desktop/AI/langchain-fork/docs/modules/indexes/document_loaders/examples/The spelled-out intro to neural networks and backpropagationÔºö building micrograd.m4a; file is already in target format m4a# Returns a list of Documents, which can be easily viewed or parseddocs[0].page_content[0:500]    "Hello, my name is Andrej and I've been training deep neural networks for a bit more than a decade. And in this lecture I'd like to show you what neural network training looks like under the hood. So in particular we are going to start with a blank Jupyter notebook and by the end of this lecture we will define and train a neural net and you'll get to see everything that goes on under the hood and exactly sort of how that works on an intuitive level. Now specifically what I would like to do is I w"Building a chat app from YouTube video‚ÄãGiven Documents, we can easily enable chat / question+answering.from langchain.chains import RetrievalQAfrom langchain.vectorstores import FAISSfrom langchain.chat_models import ChatOpenAIfrom langchain.embeddings import OpenAIEmbeddingsfrom langchain.text_splitter import RecursiveCharacterTextSplitter# Combine doccombined_docs = [doc.page_content