LangChain Deep Dive: From Chain to Agent, Build an AI Customer Service System
🩺 Summary
LangChain is the most popular AI agent framework. This tutorial walks through building an e-commerce customer service bot, from basic Chain to full Agent with Tools and Memory.
📝 Details
## Start with a Simple Chain
```python
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
llm = ChatOpenAI(model='gpt-4o-mini')
prompt = ChatPromptTemplate.from_template('User asks: {question}')
chain = prompt | llm
print(chain.invoke({'question': 'When will my order ship?'}))
```
This runs immediately. LangChain's real power is in the next three layers.
## Layer 2: Add Tools
```python
from langchain.tools import tool
@tool
def check_order(order_id: str) -> str:
"""Check order status"""
orders = {'ORD001': 'Shipped, arriving in 3 days', 'ORD002': 'Delivered'}
return orders.get(order_id, 'Order not found')
tools = [check_order]
```
## Summary
LangChain: Chain is foundation, Tool is capability, Agent is brain, Memory is context. Combine all four layers for a complete AI Agent.
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