The post provides a comprehensive guide on building and deploying an LLM-powered chat application with memory using Streamlit. It includes steps for setting up a GitHub repository, installing dependencies, creating a virtual environment, and obtaining an API key. It also explains the features of different models, the importance of prompt engineering, and how to adjust parameters like temperature. The tutorial culminates in deploying the app on Streamlit and monitoring API usage on Google Cloud Console.
Table of contents
1. Create a New GitHub repository2. Clone the repository locally3. Set Up a Virtual Environment (optional)4. Project Structure5. Get API Key6. Store your API Key7. Choose the model8. Build the chat9. Prompt Engineering10. Choose Generate Content Parameters11. Display chat history12. Chat with memory13. Create a Reset Button (optional)14. Deploy15. Monitor API usage on Google ConsoleSort: