I Built a Mini ChatGPT in Just 10 Lines Using LangChain (Part 1)

#llm #python #langchain #ai

🚀 I Built a Mini ChatGPT in Just 10 Lines Using LangChain — Here’s the Real Engineering Breakdown

Everyone wants to build an AI assistant today — a chatbot, a personal agent, a support bot, or a micro-GPT.

But beginners often assume they need:

Complex architectures
Fine-tuned models
Heavy GPUs
RAG pipelines
Vector databases
Advanced prompt engineering

And because of that belief, they never even start.

The truth?

You can build a functioning conversational AI — a mini ChatGPT — in less than 10 lines of Python using LangChain.

And it’s not a toy.

It remembers context, responds smoothly, and becomes the foundation for any real AI application.

Let me break it down clearly.

🤔 Why This Mini-ChatGPT Project Matters

Most new AI developers get stuck because everything online feels too big:

Endless tutorials
Massive MLOps diagrams
Overwhelming frameworks
1-hour YouTube tutorials for a 5-minute concept
“Build a full RAG pipeline” before learning the basics

When really, the fastest way to understand AI engineering is:

Build something tiny. Then improve it step by step.

This 10-line chatbot is the perfect first step before:

RAG
Agents
Memory systems
LLM apps
Automation workflows

🧠 What We’re Building (Mini ChatGPT)

This mini chatbot supports:

✔ Conversational responses

✔ Automatic memory

✔ Context retention

✔ Continuous interaction

✔ Clean and expandable architecture

✔ Runs entirely with a simple Python file

Architecture (simple but powerful):

User → LangChain ConversationChain → LLM → Response

Exactly how major assistants work at a small scale.

🧪 The Real “10-Line Mini ChatGPT” Code

from langchain.llms import OpenAI
from langchain.chains import ConversationChain

llm = OpenAI(openai_api_key="YOUR_API_KEY")

chat = ConversationChain(llm=llm)

while True:
    message = input("You: ")
    print("Bot:", chat.run(message))

That’s it.

A functional AI chatbot with stateful memory.

Example Interaction

You: hey there
Bot: Hello! How can I help you today?

You: remember my name is Ashish
Bot: Got it! Nice to meet you, Ashish.

You: what's my name?
Bot: You just told me your name is Ashish.

It understands context and stores memory — without you writing a single state machine.

🧠 How It Works Internally

Component	Role
OpenAI()	The actual language model generating responses
ConversationChain	Handles dialog flow + memory
while loop	Keeps interaction alive
chat.run()	Passes input → LLM → memory → output