LangChain Tutorial

LangChain is a development framework for building AI agents and large language model (LLM) applications.

LangChain helps developers quickly build complex AI applications based on large models such as GPT, Claude, and Gemini.

LangChain was launched by Harrison Chase in October 2022, with the core goals of:Simplifying the development process of large language model applications。

LangChain provides a unified interface that can connect: large models, prompts, vector databases, tool calls, memory systems, and Agent workflows.

Currently, LangChain has become one of the most popular LLM application development frameworks, widely used in scenarios such as intelligent chatbots, RAG knowledge bases, document analysis, code generation, and AI automation.

Who is this tutorial for?

This tutorial is suitable for developers who have a basic knowledge of Python and want to learn AI application development.

  • Beginners with Python basics who want to learn AI and large language model development
  • Developers who want to build AI chatbots, knowledge bases, and Agent applications
  • Learners interested in GPT, Claude, RAG, and vector databases
  • Engineers who want to use LangChain to quickly build AI projects
  • Programmers who want to transition from traditional development to AI application development

What you need to know before starting this tutorial

Before starting this tutorial, you need to have some Python basics and understand basic Web and API concepts.

  • Python basics: variables, functions, classes, module imports, exception handling
  • HTTP and API basics: GET/POST requests, JSON data format
  • Prompt basics: understand what Prompt and large language models are
  • Basic database knowledge: understand basic SQLite / MySQL operations
  • Understand basic command-line operations and pip package management

What can LangChain do?

  • AI chatbot (ChatBot)
  • RAG enterprise knowledge base
  • PDF document Q&A system
  • AI Agent automatic task execution
  • Code generation and code analysis
  • Multi-turn dialogue and context memory
  • Web search and tool calling
  • Workflow automation systems

Your first LangChain program

The following code uses LangChain to call OpenAI models and output AI-generated content:

Example

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate

# Initialize the model
llm = ChatOpenAI(
    model="gpt-4o-mini"
)

# Prompt template
prompt = ChatPromptTemplate.from_template(
    "Please explain: {topic}"
)

# Create Chain
chain = prompt | llm

# Call
result = chain.invoke({
    "topic": "What is Transformer"
})

print(result.content)

After running, the large model will automatically generate explanatory content about Transformer.

LangChain Core Components

  • LLM: Connect to large models such as OpenAI, Claude, Gemini
  • PromptTemplate: Manage prompt templates
  • Chains: Build multi-step AI workflows
  • Memory: Implement multi-turn dialogue memory
  • Tools: Call tools such as search, database, API
  • Agents: Let AI make decisions and execute tasks automatically
  • Vector Store: Connect vector databases to implement RAG

Reference Documentation

Other extensions