Build Your Own AI Chatbot in 10 Minutes Using LangChain and Gemini

In today's fast-paced digital landscape, AI chatbots have become indispensable tools for businesses and individuals alike. They enhance customer service, provide instant responses, and can be tailored for various applications. This guide will walk you through creating a conversational AI chatbot using LangChain and Google's Gemini model in just 10 minutes.

Types of Chatbots You Can Build

Before diving into the implementation, let's consider what type of chatbot you might want to create:

Types of Chatbots

• Knowledge-Base Chatbot: Answers questions based on specific documents or data you provide

• Customer Service Chatbot: Handles common customer inquiries and support requests

• Personal Assistant Chatbot: Helps with scheduling, reminders, and daily tasks

• E-commerce Chatbot: Guides users through products and purchasing processes

• Content Creation Chatbot: Assists with generating written content or ideas

For this tutorial, we'll focus on building a knowledge-base chatbot that can answer questions about specific documents you provide, but the framework is adaptable to any of these types.

Simple Workflow:

Simple Workflow

Simple Workflow

• Document Processing:

  • Load PDF/TXT documents.

  • Split text into small chunks.

  • Generate embeddings and store them in FAISS.

    
    

• User Interaction:

  • User asks a question.

  • Convert the query into an embedding.

  • Search FAISS for the most relevant chunk.

    
    

• AI Response Generation:

  • Retrieve the best-matching information.

  • Generate a response and display it in the chat.

    
    

• Cyclic Flow:

  • The user can continue asking questions.

  • The chatbot keeps searching the FAISS database for relevant answers.

Requirements

To follow along, you'll need:

• Python 3.8+ installed

• Basic Python knowledge

• Google API key for Gemini (free tier available)

• A text editor or IDE (VS Code)

• Your own documents (PDF, TXT, etc.) that you want the chatbot to use

Step-by-Step Process

Step 1: Set Up Your Environment

First, let's create a virtual environment and install the necessary packages:

# Create and activate a virtual environment
python -m venv chatbot-env

source chatbot-env/bin/activate  
# On Windows: chatbot-env\Scripts\activate

# Install required packages
pip install langchain langchain-google-genai pypdf python-dotenv gradio

Step 2: Obtain Your API Key

1. Go to the Google AI Studio (https://ai.google.dev/)

2. Create an account or sign in

3. Navigate to "API keys" and create a new key

4. Copy your API key for later use

Step 3: Set Up Your Project Structure

Create a new directory for your project with the following files:

chatbot-project/
├── .env
├── app.py
└── documents/
    └── (your PDF or text files)

In your .env file, Add your API Key.

GOOGLE_API_KEY="<Your API Key>"

Step 4: Create the ChatBot Application:

Now let's write the code for our chatbot. Open app.py and add the followings:

Step 5: Add Your Documents

Place your PDF or text documents in the documents folder. These will be the knowledge base for your chatbot.

Step 6: Run Your Chatbot

Run your application with:

python app.py

Your chatbot will be available at http://127.0.0.1:7860 in your web browser.

chatbot in your web browser

Conclusion

In just 10 minutes, you've built a powerful, custom AI chatbot using LangChain and Google's Gemini model. This foundation can be expanded and customized to suit virtually any use case you can imagine. The barrier to entry for creating AI-powered tools has never been lower, making it the perfect time to experiment with building your own intelligent applications.

Happy building!