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pip install langchain langchain-openai langchain-community

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from langchain_openai import ChatOpenAI
from langchain_community.llms import Ollama

# OpenAI
llm = ChatOpenAI(model="gpt-4o", api_key="your-key")

# 本地 Ollama
llm = Ollama(model="llama3.3")

# 呼叫
response = llm.invoke("什麼是 Kubernetes？")
print(response)

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from langchain_core.prompts import ChatPromptTemplate

prompt = ChatPromptTemplate.from_messages([
    ("system", "你是一位專業的{role}"),
    ("user", "{question}")
])

chain = prompt | llm
response = chain.invoke({
    "role": "資安顧問",
    "question": "如何防範 SQL Injection？"
})

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from langchain_community.document_loaders import (
    PyPDFLoader,
    TextLoader,
    WebBaseLoader
)

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

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

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from langchain.text_splitter import RecursiveCharacterTextSplitter

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

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from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

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

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from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_core.output_parsers import StrOutputParser

template = """根據以下內容回答問題：

{context}

問題：{question}
"""

prompt = ChatPromptTemplate.from_template(template)

rag_chain = (
    {"context": retriever, "question": RunnablePassthrough()}
    | prompt
    | llm
    | StrOutputParser()
)

response = rag_chain.invoke("什麼是零信任架構？")

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from langchain_core.tools import tool

@tool
def search_web(query: str) -> str:
    """搜尋網頁內容"""
    # 實作搜尋邏輯
    return f"搜尋結果：{query}"

@tool
def calculate(expression: str) -> str:
    """計算數學表達式"""
    return str(eval(expression))

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from langchain.agents import create_react_agent, AgentExecutor
from langchain import hub

# 取得 ReAct prompt
prompt = hub.pull("hwchase17/react")

# 建立 agent
agent = create_react_agent(llm, [search_web, calculate], prompt)
agent_executor = AgentExecutor(agent=agent, tools=[search_web, calculate])

# 執行
result = agent_executor.invoke({
    "input": "搜尋最新的 CVE 漏洞並計算今年已發布多少個"
})

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from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

history = ChatMessageHistory()

chain_with_history = RunnableWithMessageHistory(
    chain,
    lambda session_id: history,
    input_messages_key="question",
    history_messages_key="history"
)

response = chain_with_history.invoke(
    {"question": "我叫小明"},
    config={"configurable": {"session_id": "user123"}}
)

response = chain_with_history.invoke(
    {"question": "我叫什麼名字？"},
    config={"configurable": {"session_id": "user123"}}
)

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from fastapi import FastAPI
from langserve import add_routes

app = FastAPI()

add_routes(app, chain, path="/chat")

# 運行
# uvicorn main:app --reload