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Update app.py
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app.py
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@@ -50,7 +50,7 @@ def invoke(openai_api_key, youtube_url, process_video, prompt):
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description = """<strong>Overview:</strong> The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong>
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(RAG) on external data (YouTube videos in this case, but it could be PDFs, URLs, databases, or other structured/unstructured and private/public
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<a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
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<strong>Instructions:</strong> Enter an OpenAI API key and perform semantic search, sentiment analysis, summarization, translation, etc.
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<ul style="list-style-type:square;">
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<li>Set "Process Video" to "False" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li>
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<li>Set "Process Video" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
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description = """<strong>Overview:</strong> The app demonstrates how to use a <strong>Large Language Model</strong> (LLM) with <strong>Retrieval Augmented Generation</strong>
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(RAG) on external data (YouTube videos in this case, but it could be PDFs, URLs, databases, or other structured/unstructured and private/public
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<a href='https://raw.githubusercontent.com/bstraehle/ai-ml-dl/c38b224c196fc984aab6b6cc6bdc666f8f4fbcff/langchain/document-loaders.png'>data sources</a>).\n\n
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<strong>Instructions:</strong> Enter an OpenAI API key and perform LLM use cases (semantic search, sentiment analysis, summarization, translation, etc.)
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<ul style="list-style-type:square;">
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<li>Set "Process Video" to "False" and submit prompt "what is gpt-4". The LLM <strong>without</strong> RAG does not know the answer.</li>
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<li>Set "Process Video" to "True" and submit prompt "what is gpt-4". The LLM <strong>with</strong> RAG knows the answer.</li>
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