LangChain 入门：用统一接口连接模型、消息、提示词与解析器

python -m pip install "langchain>=1,<2" "langchain-openai>=1,<2"
export MODEL_API_KEY="replace-with-your-key"
export MODEL_API_BASE="https://provider.example/v1"
export MODEL_NAME="provider-model-name"

import os
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI


prompt = ChatPromptTemplate.from_messages([
    ("system", "只根据输入整理事实，不推测原因；输出不超过60字。"),
    ("human", "事件：{event}"),
])

model = ChatOpenAI(
    model=os.environ["MODEL_NAME"],
    api_key=os.environ["MODEL_API_KEY"],
    base_url=os.environ["MODEL_API_BASE"],
    temperature=0,
    timeout=30,
    max_retries=2,
)

chain = prompt | model | StrOutputParser()
result = chain.invoke(
    {"event": "节点 AX-3 网络抖动，10:42 切换备用线路后恢复。"},
    config={
        "tags": ["stage07", "summary"],
        "metadata": {"prompt_version": "summary-v1"},
    },
)
print(result)

messages = prompt.invoke({"event": "节点 AX-3 已恢复"})
for message in messages.messages:
    print(type(message).__name__, message.content)

python -m pip freeze | grep -E '^(langchain|pydantic)'