LangChain

# pip install langchain langchain-openai langchain-community
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain.agents import create_tool_calling_agent, AgentExecutor
from langchain_community.tools import DuckDuckGoSearchRun

# 1. 기본 Chain (LCEL - LangChain Expression Language)
llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "당신은 {role} 전문가입니다."),
    ("human", "{question}")
])
chain = prompt | llm | StrOutputParser()
result = chain.invoke({"role": "Python", "question": "데코레이터 설명해줘"})

# 2. RAG Chain (문서 기반 QA)
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain

embeddings = OpenAIEmbeddings()
vectorstore = FAISS.load_local("my_index", embeddings)
retriever = vectorstore.as_retriever(search_kwargs={"k": 3})

rag_prompt = ChatPromptTemplate.from_template(
    "컨텍스트를 참고해 답변하세요:\n{context}\n\n질문: {input}"
)
document_chain = create_stuff_documents_chain(llm, rag_prompt)
rag_chain = create_retrieval_chain(retriever, document_chain)
answer = rag_chain.invoke({"input": "우리 회사 휴가 정책은?"})

# 3. Agent with Tools
search_tool = DuckDuckGoSearchRun()
tools = [search_tool]

agent_prompt = ChatPromptTemplate.from_messages([
    ("system", "도구를 사용해 최신 정보를 검색하세요."),
    ("human", "{input}"),
    ("placeholder", "{agent_scratchpad}")
])
agent = create_tool_calling_agent(llm, tools, agent_prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
result = executor.invoke({"input": "오늘 비트코인 가격은?"})