Running on Macbook M1 pro (16GB RAM) using the @ollama
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@saboo_shubham_
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Running Ollama on MacBook M1 Pro with 16GB RAM
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Building Local AI Agents with Phidata Made Simple
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Thank you so much Ashpreet! It's amazingly straight-forward to even build local AI agents with @phidatahq
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LLM Tips and Tutorials for Sharing
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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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LLM Apps with RAG Demos 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 Demo: Research Agent with Llama3
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Working Application demo using Streamlit
— Shubham Saboo (@Saboo_Shubham_) 6 juin 2024
Paste the above code in vscode or pycharm and run the following command: 'streamlit run research_agent_llama3.py' pic.twitter.com/W67HwFCYQ3Working Application demo using Streamlit Paste the above code in vscode or pycharm and run the following command: 'streamlit run research_agent_llama3.py'
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Building AI Research Agent Chat Interface with Streamlit
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6. Chat with AI Research Agent
• Create a text input for the user to enter their query using 'st.text_input()'
• If a question is asked, run the assistant to get the answer and display it using 'st.write()' -
Initialize AI Assistants Team for HackerNews Research
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5. Initialize the AI assistants
• Create instances of Assistants: HackerNews Story Researcher and HackerNews User Researcher
• Combine the assistants into a team called "Hackernews Team" -
Setting Up Streamlit App User Interface with Python
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4. Set up the Streamlit App Streamlit lets you create a user interface with just Python code. For this app, we will:
• Add a title to the app using 'st.title()'
• Add a description for the app using 'st.caption()' -
Essential Libraries for Building AI Agent Web Applications
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3. Import necessary libraries • Streamlit for building the web app
• Phidata for building AI agents
