Copy and paste these ChatGPT productivity prompts to get 10 hours of work done in 2:
LLMS
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LLMs Tips and Tutorials – Follow for More Content
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If you find this useful, RT to share it with your friends. Don't forget to follow me @Saboo_Shubham_ for more such LLMs tips and tutorials.
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Awesome LLM Apps with RAG in GitHub Repository
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Find all the awesome LLM Apps demo with RAG in the following Github Repo. P.S: Don't forget to star the repo to show your support
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Streamlit Local RAG Demo with Llama3
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Working Application demo using Streamlit
— Shubham Saboo (@Saboo_Shubham_) 2 mai 2024
Paste the above code in vscode or pycharm and run the following command: 'streamlit run llama3_local_rag.py' pic.twitter.com/9oqWGMHVk5Working Application demo using Streamlit Paste the above code in vscode or pycharm and run the following command: 'streamlit run llama3_local_rag.py'
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Build RAG App with Streamlit Text Input and Answer Display
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8. Ask question about the website and get the answer
• Create a text input for the user to enter their question using 'st.text_input()'
• If a question is asked, get the answer from the RAG app and display it using 'st.write()' -
Setting up RAG chain for AI applications
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7. Set up the RAG (Retrieval-Augmented Generation) chain
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Implementing Ollama Llama-3 Model Locally with Context
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6. Define a function to call the Ollama Llama-3 model locally with context.
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Create Ollama Embeddings and Vector Store with Chroma
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5. Create Ollama embeddings and a vector store using Chroma.
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Building RAG Web Apps with Streamlit LangChain Ollama
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2. Import necessary libraries • Streamlit for building the web app
• LangChain for the RAG functionality
• Ollama for running local LLM like Llama-3
