Kick start - Langchain

📘 Introduction

LangChain is the missing layer between large language models (LLMs) and real-world applications. Whether you’re building a chatbot, a RAG-powered knowledge base, or an intelligent agent — LangChain gives you the tools to do it fast, flexibly, and at scale.

In this series, you’ll learn LangChain from the ground up — starting with what it is, why it’s useful, and how to get your first project running in minutes.

🚀 What You’ll Learn in This Module

• What LangChain is and where it fits in the LLM stack
• Real-world use cases LangChain simplifies
• Key benefits over using raw LLM APIs
• How to install and configure LangChain for your projects

🤖 What is LangChain?

LangChain is a framework for building applications with LLMs that require interaction with:

• External data (APIs, files, databases)
• User memory and context
• Multi-step workflows
• Tool-augmented reasoning

Instead of handling prompts and responses manually, LangChain provides abstractions for:

• Prompt templates
• Chains of logic
• Conversational memory
• Tool integration (e.g., Google Search, SQL)

🧠 Core Use Cases

• Conversational agents (chatbots with memory)
• RAG pipelines (retrieve + generate)
• Assistants for coding, research, finance, etc.

🔍 Why Use LangChain?

LangChain solves many of the limitations of direct LLM usage:

LangChain is like the Express.js or Django for LLM-based apps.

⚙️ Installation and Setup

Let’s get your environment ready.

1. Install LangChain

pip install langchain openai

Note: You’ll also need an OpenAI API key. Sign up at platform.openai.com

2. Set Environment Variables

If Macbook used

export OPENAI_API_KEY="your-api-key-here"

3. Minimal Setup

from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

llm = ChatOpenAI()
prompt = PromptTemplate.from_template("Explain {concept} like I'm 5")
chain = LLMChain(llm=llm, prompt=prompt)

print(chain.run("black holes"))

This is your “Hello World” with LangChain. In 5 lines, you built a smart explainer bot.

What’s Next?

In the next module, you’ll explore the core building blocks of LangChain:

• Chains
• Memory
• Agents
• Tools
• Document Loaders
• Vector Stores

Let’s go from simple prompts to full workflows — with real-world, hands-on code.