What is LangChain?
Overview
Welcome to your first lesson on LangChain. In this session, you’ll get a clear understanding of what LangChain is, why it's gaining popularity, and how it can help you build powerful applications with Large Language Models (LLMs) like OpenAI’s GPT.
Learning Objectives
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Understand what LangChain is
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Explore its core components
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Learn where and how LangChain is used
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Discover why it’s useful in real-world AI app development
What is LangChain?
LangChain is an open-source framework designed to help developers build context-aware, data-augmented, and tool-using applications powered by LLMs.
It provides:
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Prompt templating for dynamic instruction generation
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Memory to maintain conversation context
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Agent integrations to enable LLMs to decide and act
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Retrieval-augmented generation (RAG) for document-based answers
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A modular, chain-based structure for real-world AI workflows
Why Do We Need LangChain?
| Challenge | LangChain Solution |
|---|---|
| LLMs are stateless | Adds memory to track conversations |
| Manual prompt formatting | Uses PromptTemplate |
| Can't connect external tools | Uses Agents & Tools |
| Need external knowledge | Enables RAG (Retrieval-Augmented Generation) |
| Difficult to modularize workflows | Structures logic using Chains |
Core Components of LangChain
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LLM / ChatModel: Interface with OpenAI, Claude, HuggingFace models
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PromptTemplate: Construct dynamic prompts with variables
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Chains: Organize multi-step tasks using SequentialChain, SimpleChain
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Memory: Store and recall context from past interactions
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Agents & Tools: Let the LLM choose which tools to use to complete a task
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Document Loaders: Ingest content from PDFs, websites, Notion, CSVs
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Vector Stores: Store and search data chunks using embeddings with FAISS, Chroma, Pinecone
Real-World Use Cases
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Chat with Documents: Upload PDFs or websites and ask questions about their content
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Customer Support Bots: Integrate CRM tools and automate intelligent responses
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Legal and Financial Summarization: Extract insights from contracts or annual reports
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Coding Assistant: Let an agent write and execute code
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Multi-tool AI Assistants: Combine web search, calculator, calendar, or custom APIs
Why Learn LangChain in 2025?
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Leading framework for LLM-based application development
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Allows seamless integration of models, tools, and external data
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Actively maintained with a growing ecosystem
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Ideal for students, startups, and enterprises to build intelligent assistants
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Bridges the gap between raw LLMs and real-world use cases
Mini Project
Objective: Build a simple LangChain app that asks OpenAI to explain a topic.
from langchain.chat_models import ChatOpenAI
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
llm = ChatOpenAI()
prompt = PromptTemplate(
input_variables=["topic"],
template="Explain {topic} in simple terms."
)
chain = LLMChain(llm=llm, prompt=prompt)
print(chain.run("Quantum Computing"))
Quick Recap Quiz
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What are the core building blocks of LangChain?
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How does a Chain differ from an Agent?
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Why is LangChain better than just using OpenAI’s API directly?
