Introduction

In this article, we’ll explore how to build a Q&A chatbot using LangChain, OpenAI, and Gradio. We’ll cover:

• Setting up the environment
• Creating a simple chatbot
• Debugging common errors
• Transitioning from Streamlit to Gradio

This guide is based on real troubleshooting steps, making it ideal for beginners and intermediate learners.

1. Understanding the Goal

We want to build a chatbot that:
✅ Takes user questions
✅ Sends them to OpenAI’s GPT-3.5-turbo
✅ Returns AI-generated answers

Tools Used

• LangChain (for AI workflow)
• OpenAI (for the LLM)
• Gradio (for the web interface)
• Python (for scripting)

2. Initial Setup & Common Errors

A. Installing Dependencies

First, install the required libraries:

pip install langchain-community gradio python-dotenv openai

B. Setting Up OpenAI API Key

Create a .env file:

OPENAI_API_KEY=your_api_key_here

Load it in Python:

from dotenv import load_dotenv  
load_dotenv()  

C. Choosing the Right Model

⚠️ Common Mistake: Using OpenAI() instead of ChatOpenAI() for GPT-3.5-turbo.

✅ Correct Approach:

from langchain_community.chat_models import ChatOpenAI  

llm = ChatOpenAI(
    model="gpt-3.5-turbo",
    temperature=0.5,
    openai_api_key=os.getenv("OPENAI_API_KEY")
)

3. Building the Chatbot

A. Simple Q&A Function

def get_response(question):
    response = llm([HumanMessage(content=question)])
    return response.content

B. Creating a Web Interface

Option 1: Using Streamlit (Simple but Limited)

import streamlit as st  

st.text_input("Ask a question", key="question")
if st.button("Submit"):
    st.write(get_response(st.session_state.question))

Option 2: Using Gradio (More Flexible & Interactive)

import gradio as gr  

def respond(question):
    return get_response(question)  

gr.Interface(
    fn=respond,
    inputs=gr.Textbox(label="Ask anything"),
    outputs=gr.Textbox(label="AI Response")
).launch()

✅ Why Gradio is Better?

• Supports chat history
• Easier to customize UI
• Built-in API endpoints

4. Debugging Common Issues

5. Key Learnings

A. LangChain Basics

• Use ChatOpenAI for chat models (GPT-3.5, GPT-4)
• HumanMessage for user inputs
• load_dotenv() for secure API key management

B. Gradio vs. Streamlit

C. Best Practices

✔ Always handle API errors
✔ Use environment variables for API keys
✔ Test models separately before UI integration

6. Final Working Code (Gradio Version)

import gradio as gr
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import HumanMessage
from dotenv import load_dotenv
import os

load_dotenv()

llm = ChatOpenAI(
    model="gpt-3.5-turbo",
    temperature=0.5,
    openai_api_key=os.getenv("OPENAI_API_KEY")
)

def respond(question):
    response = llm([HumanMessage(content=question)])
    return response.content

gr.Interface(
    fn=respond,
    inputs=gr.Textbox(label="Ask anything"),
    outputs=gr.Textbox(label="AI Response"),
    title="AI Chatbot"
).launch()

7. Conclusion & Next Steps

✅ Built a functional AI chatbot
✅ Learned LangChain + OpenAI integration
✅ Compared Gradio vs. Streamlit

Next Steps:
➡ Add chat memory (for multi-turn conversations)
➡ Deploy on Hugging Face Spaces
➡ Experiment with other LLMs (Llama 3, Claude, etc.)

Final Thoughts

This journey covered real-world debugging, best practices, and UI choices. Whether you’re a beginner or an experienced developer, these concepts will help in building better AI applications.

🔗 Try it yourself and share your results! 🚀