102 lines
4.4 KiB
Python
102 lines
4.4 KiB
Python
# requires: langchain, langchain_core, pydantic
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"""LangChain 1.x 基础：LCEL 链、结构化输出解析 + 重试 + 备用模型、create_agent 工具调用 + 中间件。
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用假模型离线运行（GenericFakeChatModel 按顺序返回预设消息）。换成真实模型：
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from langchain.chat_models import init_chat_model
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model = init_chat_model("openai:gpt-4.1-mini") # 模型名和提供方写法以官方文档为准
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基于 langchain 1.4 / langchain-core 1.7 编写，以官方文档为准：https://docs.langchain.com/oss/python/langchain/overview
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运行：python lc_basics.py
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"""
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from __future__ import annotations
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from langchain.agents import create_agent
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from langchain.agents.middleware import ModelCallLimitMiddleware
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from langchain_core.language_models.fake_chat_models import GenericFakeChatModel
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from langchain_core.messages import AIMessage
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from langchain_core.output_parsers import PydanticOutputParser, StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_core.runnables import RunnableLambda
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from langchain_core.tools import tool
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from pydantic import BaseModel, Field
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def fake(*texts_or_msgs) -> GenericFakeChatModel:
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msgs = [m if isinstance(m, AIMessage) else AIMessage(content=m) for m in texts_or_msgs]
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return GenericFakeChatModel(messages=iter(msgs))
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# ---------- 1. LCEL：提示词 | 模型 | 解析器 ----------
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def demo_lcel() -> str:
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prompt = ChatPromptTemplate.from_messages([
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("system", "你是客服，用一句话回答。"),
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("human", "{question}"),
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])
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chain = prompt | fake("您好，开发票请在订单详情页申请。") | StrOutputParser()
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return chain.invoke({"question": "怎么开发票"})
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# ---------- 2. 结构化输出：解析 + 失败重试 + 备用模型 ----------
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class Triage(BaseModel):
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category: str = Field(pattern="^(refund|bug|question)$")
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urgent: bool
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def demo_structured() -> Triage:
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parser = PydanticOutputParser(pydantic_object=Triage)
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prompt = ChatPromptTemplate.from_messages([
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("system", "对工单分类。{format_instructions}"),
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("human", "{ticket}"),
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]).partial(format_instructions=parser.get_format_instructions())
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# 主模型第一次返回了不合法的 JSON，第二次正确：with_retry 对解析失败重试
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primary = prompt | fake("类别是退款", '{"category": "refund", "urgent": true}') | parser
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robust = primary.with_retry(stop_after_attempt=2)
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# 主链彻底失败时切换到备用链（真实项目中是另一个模型 / 提供方）
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def broken(_):
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raise TimeoutError("主模型超时")
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backup = prompt | fake('{"category": "question", "urgent": false}') | parser
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with_fallback = RunnableLambda(broken).with_fallbacks([backup])
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assert with_fallback.invoke({"ticket": "怎么改地址"}).category == "question"
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return robust.invoke({"ticket": "我要退款，很急"})
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# ---------- 3. create_agent：工具调用 + 调用次数限制 ----------
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class ToolCallingFake(GenericFakeChatModel):
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"""假模型不支持 bind_tools，这里直接返回自身；真实模型会把工具定义发给接口。"""
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def bind_tools(self, tools, **kwargs):
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return self
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@tool
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def get_order_status(order_id: str) -> str:
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"""查询订单的物流状态。order_id 形如 A1001。"""
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return {"A1001": "已发货，预计明天送达"}.get(order_id, "订单不存在")
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def demo_agent() -> list:
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model = ToolCallingFake(messages=iter([
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AIMessage(content="", tool_calls=[{"name": "get_order_status", "args": {"order_id": "A1001"}, "id": "c1"}]),
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AIMessage(content="您的订单 A1001 已发货，预计明天送达。"),
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]))
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agent = create_agent(
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model,
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tools=[get_order_status],
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system_prompt="你是售后客服，查询订单要用工具。",
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middleware=[ModelCallLimitMiddleware(run_limit=5)], # 防止无限循环
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)
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out = agent.invoke({"messages": [{"role": "user", "content": "A1001 到哪了"}]})
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return out["messages"]
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if __name__ == "__main__":
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print("LCEL：", demo_lcel())
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t = demo_structured()
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print("结构化输出：", t)
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assert t.category == "refund" and t.urgent
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msgs = demo_agent()
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for m in msgs:
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print(f"{type(m).__name__:12s}", m.content or getattr(m, "tool_calls", ""))
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assert [type(m).__name__ for m in msgs] == ["HumanMessage", "AIMessage", "ToolMessage", "AIMessage"]
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