AI Practitioner

Introduction This post walks through how to implement a lightweight yet powerful chatbot backend using  Google Gemini 2.5 Pro  and  LangChain . It also covers how to deploy a chat-friendly frontend interface and understand the internal architecture powering this conversational AI. Whether you're prototyping or integrating LLMs into enterprise-scale apps, this pattern gives you a solid foundation to build on. Step 1: Install Dependencies Here's the minimal tech stack we’ll use: Python Packages pip install flask flask-cors langchain langchain-google-genai python-dotenv Make sure you have a .env file with your Google API key: GOOGLE_API_KEY=your_google_api_key_here Step 2: Chatbot Architecture Here’s a high-level diagram of how the system works: User (Web UI) │ ▼ HTTP POST /chat │ ▼ Flask API │ ▼ LangChain Prompt Template → Gemini 2.5 Pro (via Google Generative AI) │ ▼ Response → JSON → UI Frontend  sends a POST reque...