LangChain 2026：构建 LLM 应用的完整框架指南

from langchain.llms import OpenAI

llm = OpenAI(model="gpt-4", temperature=0)
response = llm("解释量子计算")

from langchain.chat_models import ChatOpenAI

chat = ChatOpenAI(model="gpt-4")
response = chat.invoke([
    {"role": "system", "content": "你是个 helpful assistant"},
    {"role": "human", "content": "解释下方代码的作用："},
    {"role": "human", "content": "for i in range(10): print(i)"}
])

from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
memory.save_context({"input": "你好"}, {"output": "你好啊"})

from langchain.agents import initialize_agent, Tool
from langchain.tools import DuckDuckGoSearchRun

search = DuckDuckGoSearchRun()
tools = [Tool(name="search", func=search.run, description="搜索")]
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")

from langchain.document_loaders import PyPDFLoader

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

from langchain.text_splitter import RecursiveCharacterTextSplitter

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

from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma

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

from langchain.chains import RetrievalQA

qa = RetrievalQA.from_chain_type(
    llm, 
    retriever=db.as_retriever()
)
result = qa.run("这个文档说了什么？")

pip install langchain[all]

FROM python:3.10
RUN pip install langchain openai
CMD ["python", "app.py"]