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...

What is LangChain and Why Use It? LangChain  is an open-source framework that simplifies the use of  Large Language Models (LLMs)  like OpenAI, Gemini (Google), and others by adding structure, tools, and memory to help build real-world applications such as chatbots, assistants, agents, or AI-enhanced software. Why Use LangChain for LLM Projects? Chainable Components : Easily build pipelines combining prompts, LLMs, tools, and memory. Multi-Model Support : Work with Gemini, OpenAI, Anthropic, Hugging Face, etc. Built-in Templates : Manage prompts more effectively. Supports Multi-Turn Chat : Manage complex interactions with memory and roles. Tool and API Integration : Let the model interact with external APIs or functions. Let's Walk Through the Code: Gemini + LangChain I will break the code into  4 main parts , each showcasing different features of LangChain and Gemini API. Part 1: Basic Gemini API Call Using LangChain import os from dotenv import load_dotenv load_dot...