<ul><li>The article provides a detailed guide on building and deploying a chat powered by LLM Gemini in Streamlit and monitoring API usage on Google Cloud Console.</li><li>It covers steps like setting up a new GitHub repository, cloning it locally, creating a virtual environment, defining the project structure, and getting an API key.</li><li>The article explains the importance of storing API keys securely and provides code snippets for setting up the project structure and obtaining the API key.</li><li>It discusses prompt engineering, model selection, generating content parameters like temperature and maxOutputTokens, and displaying chat history within the app.</li><li>The guide includes setting system prompts, setting context for the chat with memory, adding a reset button, and deploying the chat app on Streamlit.</li><li>It also explains how to monitor API usage on Google Cloud Console by checking metrics like requests, errors, and latency.</li><li>The article aims to help developers create interactive chat applications using Streamlit and LLM technology, emphasizing security and efficient API management.</li></ul>

Step-by-Step Guide to Build and Deploy an LLM-Powered Chat with Memory in Streamlit

Discover more