LangChain 실전

# LangChain 실전 가이드

## 학습 목표
LangChain으로 LLM 기반 애플리케이션을 개발합니다.

## 1. LangChain이란?
LLM 애플리케이션 개발 프레임워크입니다.
- 다양한 LLM 통합
- 체인과 에이전트
- RAG 구현 지원
- 메모리 관리

## 2. 설치
pip install langchain langchain-openai

환경변수:
export OPENAI_API_KEY=sk-xxxxx

## 3. 기본 LLM 호출
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o")
response = llm.invoke("Hello")
print(response.content)

## 4. 프롬프트 템플릿
from langchain.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
("system", "You are a {role}."),
("user", "{input}")
])

chain = prompt | llm
response = chain.invoke({"role": "teacher", "input": "Explain Python"})
print(response.content)

## 5. 출력 파서
from langchain.output_parsers import JsonOutputParser
from langchain_core.pydantic_v1 import BaseModel

class Product(BaseModel):
name: str
price: float
description: str

parser = JsonOutputParser(pydantic_object=Product)

prompt = ChatPromptTemplate.from_messages([
("system", "Extract product info. {format_instructions}"),
("user", "{text}")
])

chain = prompt | llm | parser
result = chain.invoke({
"text": "iPhone 15 Pro costs 1499 dollars. Great camera.",
"format_instructions": parser.get_format_instructions()
})

## 6. 문서 로더
from langchain_community.document_loaders import (
PyPDFLoader,
TextLoader,
WebBaseLoader
)

PDF:
loader = PyPDFLoader("document.pdf")
docs = loader.load()

텍스트:
loader = TextLoader("file.txt")
docs = loader.load()

웹페이지:
loader = WebBaseLoader("https://example.com")
docs = loader.load()

## 7. 텍스트 분할
from langchain.text_splitter import RecursiveCharacterTextSplitter

splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200
)
chunks = splitter.split_documents(docs)

## 8. 임베딩과 벡터 저장소
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

embeddings = OpenAIEmbeddings()

vectorstore = Chroma.from_documents(
documents=chunks,
embedding=embeddings,
persist_directory="./chroma_db"
)

검색:
results = vectorstore.similarity_search("query", k=3)

## 9. RAG 체인
from langchain.chains import RetrievalQA

retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=retriever,
return_source_documents=True
)

result = qa_chain.invoke({"query": "What is the main topic?"})
print(result["result"])

## 10. 대화 메모리
from langchain.memory import ConversationBufferMemory
from langchain.chains import ConversationChain

memory = ConversationBufferMemory()

chain = ConversationChain(
llm=llm,
memory=memory
)

chain.invoke({"input": "My name is Kim"})
chain.invoke({"input": "What is my name?"})

## 11. 에이전트
from langchain.agents import create_openai_tools_agent, AgentExecutor
from langchain.tools import tool

@tool
def calculator(expression: str) -> str:
"""Calculate math expression"""
return str(eval(expression))

@tool
def search(query: str) -> str:
"""Search the web"""
return "Search results for: " + query

tools = [calculator, search]

prompt = ChatPromptTemplate.from_messages([
("system", "You are a helpful assistant."),
("user", "{input}"),
("placeholder", "{agent_scratchpad}")
])

agent = create_openai_tools_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools)

result = executor.invoke({"input": "What is 123 * 456?"})

## 12. LCEL (LangChain Expression Language)
from langchain_core.runnables import RunnablePassthrough

chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| llm
| parser
)

result = chain.invoke("What is the summary?")

## 13. 스트리밍
for chunk in chain.stream({"input": "Tell me a story"}):
print(chunk.content, end="")

## 추천 학습 순서
1. 기본 LLM 호출
2. 프롬프트 템플릿
3. 문서 로더와 분할
4. RAG 구현
5. 에이전트 개발