LLAMA 3 & GROQ - Build The Future of Instant-Response Chatbots

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AI Summary

Summary: Building a Chat Website with Grok and Streamlit

• Introduction
  • Learn to build a fast chat website with memory using Grok, Llama 3, and Streamlit.
  • Grok processes data faster than GPT.
  • Streamlit is a Python library for building apps with a simple API.
• Setting Up
  • Follow Grok documentation for a basic chat completion.
  • Create app.py and set up Grok API key after registering on Grok Cloud.
  • Start with a minimalistic example and incrementally add complexity.
• Building the Chatbot
  • Install Grok with pip and run a simple chat completion example.
  • Replace the Mixtrol model with Llama 3 (70B variant) after finding the correct model name in the documentation.
  • Rename app.py to basic_call.py for GitHub repository access.
• Streamlit Web UI
  • Create a simplified version of the conversational chatbot found in the documentation.
  • Use Streamlit to create a simple web UI that interacts with Python code.
  • Fix indentation errors and start the web application on localhost.
• Adding Memory to Chatbot
  • Implement buffer memory to hold the last 10 messages using the Lang chain library.
  • Set up user input and session state for storing chat history.
  • Initialize chat Grok with hardcoded API key and model.
• Finalizing the Chatbot
  • Create a conversation chain object to manage interaction context.
  • Process user questions and display AI responses.
  • Fix missing dependencies and set up a virtual environment for clean library installation.
• Testing and Verification
  • Test the chatbot’s memory by having it remember and use the user’s name.
  • Confirm the chatbot’s functionality and speed.
• Conclusion
  • Successfully built a fast and memory-capable chatbot controlled by the user.