7. Set up the RAG (Retrieval-Augmented Generation) chain
@saboo_shubham_
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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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Loading and Chunking Webpage Data with WebBaseLoader
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4. Load the webpage data using `WebBaseLoader` and split it into chunks.
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Building a Streamlit App with URL Input and Python UI
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3. Set up the Streamlit App Streamlit lets you create user interface with just python code, for this app we will:
• Add a title to the app using 'st.title()'
• Get the webpage URL from the user. -
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 -
Build Local RAG App with Llama-3 Offline
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Build a RAG app with Llama-3 running locally on your computer
(100% free and without internet): -
GPT-2 Positioned as Ultimate GPT-5 Alternative Solution
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GPT-2 is the GPT-5 you were waiting for and all you ever need.
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Contributing to LLM Apps Repository
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Thanks for sharing Ryan!
Happy to have you contribute to the awesome llm apps repo: -
Share and contribute to awesome LLM repository
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You’re very welcome Kiran. Feel free to share it, build on-top and contribute back to the awesome llm repo!