Add LangGraph streaming and error handling examples, demonstrating node updates, model token flow, retry policies, and error recovery mechanisms.
This commit is contained in:
Eric Wang
254
08_streaming.py
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254
08_streaming.py
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"""LangGraph Streaming 示例：节点更新 + 模型 Token 流。
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图结构：
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START -> model
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| 有 tool_calls
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v
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tools
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+---------> model
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model -- 无 tool_calls --> END
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本课重点不是工具本身，而是三种 stream_mode：
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1. updates —— 每个节点执行完后，推送该节点的状态增量
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2. messages —— 模型生成时，逐 token 推送 AIMessageChunk
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3. 同时开两者 —— 一边打字机输出，一边看到节点何时完成
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运行：
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uv run --with langgraph --with langchain-openai --with python-dotenv \
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python 08_streaming.py
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"""
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import os
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from typing import Literal
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from dotenv import load_dotenv
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from langchain_core.tools import tool
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from langchain_openai import ChatOpenAI
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from langgraph.graph import END, START, MessagesState, StateGraph
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from langgraph.prebuilt import ToolNode
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load_dotenv()
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# -----------------------------------------------------------------------------
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# 1. 工具与模型（沿用上一课的天气查询，方便观察节点切换）
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# -----------------------------------------------------------------------------
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@tool
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def get_weather(city: str) -> str:
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"""查询指定城市当前的天气。城市名称使用中文，例如北京、上海。"""
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weather_data = {
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"北京": "晴，25°C，北风 2 级",
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"上海": "多云，28°C，东南风 3 级",
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"广州": "雷阵雨，30°C，南风 2 级",
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"深圳": "阴，29°C，东风 2 级",
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}
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return weather_data.get(city, f"暂时没有 {city} 的天气数据")
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tools = [get_weather]
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model = ChatOpenAI(
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model=os.getenv("OPENAI_MODEL", "openai/gpt-5.4"),
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api_key=os.getenv("OPENAI_API_KEY"),
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base_url=os.getenv("OPENAI_BASE_URL", "https://api.qnaigc.com/v1"),
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temperature=0,
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max_retries=2,
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)
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model_with_tools = model.bind_tools(tools)
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def call_model(state: MessagesState) -> dict:
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# 即使这里写的是 .invoke，stream_mode="messages" 仍能拿到 token 流。
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# LangGraph 会自动把内部 LLM 调用的流式事件透传出来。
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response = model_with_tools.invoke(state["messages"])
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return {"messages": [response]}
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def route_after_model(state: MessagesState) -> Literal["tools", "__end__"]:
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last_message = state["messages"][-1]
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if last_message.tool_calls:
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return "tools"
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return END
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tool_node = ToolNode(tools)
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builder = StateGraph(MessagesState)
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builder.add_node("model", call_model)
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builder.add_node("tools", tool_node)
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builder.add_edge(START, "model")
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builder.add_conditional_edges(
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"model",
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route_after_model,
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{"tools": "tools", END: END},
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)
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builder.add_edge("tools", "model")
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graph = builder.compile()
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# -----------------------------------------------------------------------------
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# 2. 演示 A：stream_mode="updates" —— 看节点级增量
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# -----------------------------------------------------------------------------
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def demo_updates(question: str) -> None:
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"""每个节点跑完才出一包数据：{节点名: 该节点返回的状态增量}。"""
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print("\n--- stream_mode='updates' ---")
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for event in graph.stream(
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{"messages": [{"role": "user", "content": question}]},
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stream_mode="updates",
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):
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# event 形如：{"model": {"messages": [AIMessage(...)]}}
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# 或：{"tools": {"messages": [ToolMessage(...)]}}
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for node_name, update in event.items():
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print(f"\n[{node_name}] 节点完成，增量：")
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for message in update.get("messages", []):
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message.pretty_print()
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# -----------------------------------------------------------------------------
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# 3. 演示 B：stream_mode="messages" —— 看模型 Token 流
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# -----------------------------------------------------------------------------
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def demo_messages(question: str) -> None:
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"""模型一边生成，一边吐 AIMessageChunk；适合做打字机效果。"""
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print("\n--- stream_mode='messages' ---")
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print("助手：", end="", flush=True)
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for chunk in graph.stream(
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{"messages": [{"role": "user", "content": question}]},
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stream_mode="messages",
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version="v2",
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):
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# v2 统一格式：{"type": "messages", "data": (message_chunk, metadata)}
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if chunk["type"] != "messages":
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continue
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message_chunk, metadata = chunk["data"]
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# 只打印 model 节点吐出的文本 token；工具调用阶段 content 常为空。
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if metadata.get("langgraph_node") != "model":
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continue
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content = message_chunk.content
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if content:
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# content 可能是 str，也可能是 content blocks 列表，这里只处理文本。
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if isinstance(content, str):
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print(content, end="", flush=True)
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elif isinstance(content, list):
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for block in content:
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if isinstance(block, dict) and block.get("type") == "text":
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print(block.get("text", ""), end="", flush=True)
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elif isinstance(block, str):
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print(block, end="", flush=True)
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print() # 换行
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# -----------------------------------------------------------------------------
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# 4. 演示 C：同时开 updates + messages —— 最实用的组合
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# -----------------------------------------------------------------------------
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def demo_both(question: str) -> None:
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"""一边流式打印 token，一边在节点结束时打印节点名。"""
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print("\n--- stream_mode=['updates', 'messages'] ---")
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print("助手：", end="", flush=True)
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printed_tokens = False
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for chunk in graph.stream(
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{"messages": [{"role": "user", "content": question}]},
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stream_mode=["updates", "messages"],
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version="v2",
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):
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if chunk["type"] == "messages":
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message_chunk, metadata = chunk["data"]
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if metadata.get("langgraph_node") != "model":
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continue
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content = message_chunk.content
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if isinstance(content, str) and content:
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print(content, end="", flush=True)
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printed_tokens = True
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elif isinstance(content, list):
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for block in content:
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text = ""
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if isinstance(block, dict) and block.get("type") == "text":
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text = block.get("text", "")
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elif isinstance(block, str):
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text = block
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if text:
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print(text, end="", flush=True)
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printed_tokens = True
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elif chunk["type"] == "updates":
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# 节点结束后再换行并标注，避免和 token 混在同一行。
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for node_name in chunk["data"]:
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if printed_tokens:
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print()
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printed_tokens = False
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print(f" ✓ 节点 [{node_name}] 完成")
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if node_name == "model":
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print("助手：", end="", flush=True)
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if printed_tokens:
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print()
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# -----------------------------------------------------------------------------
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# 5. 入口：依次跑三种模式，对比输出差异
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# -----------------------------------------------------------------------------
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def main() -> None:
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print("=== LangGraph Streaming：节点更新 + Token 流 ===")
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print("建议先问：北京天气怎么样？ （会触发 tools -> model 两跳）")
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print("或问：用一句话介绍 LangGraph。 （只走 model）")
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print("输入 quit 退出。\n")
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print("命令：")
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print(" /u 只看 updates")
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print(" /m 只看 messages（token）")
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print(" /b 同时看两者（默认）")
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mode = "both"
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while True:
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question = input(f"\n用户[{mode}]：").strip()
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if question.lower() in {"quit", "exit", "q"}:
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break
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if not question:
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continue
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if question == "/u":
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mode = "updates"
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print("已切换到 updates")
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continue
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if question == "/m":
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mode = "messages"
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print("已切换到 messages")
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continue
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if question == "/b":
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mode = "both"
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print("已切换到 both")
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continue
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try:
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if mode == "updates":
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demo_updates(question)
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elif mode == "messages":
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demo_messages(question)
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else:
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demo_both(question)
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except Exception as exc:
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print(f"\n执行失败：{type(exc).__name__}: {exc}")
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if __name__ == "__main__":
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main()
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185
09_retry_and_errors.py
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185
09_retry_and_errors.py
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"""LangGraph 容错示例：重试、错误恢复与循环保护。
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本文件不调用真实 LLM/API，可以直接运行并稳定复现实验：
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1. transient：前两次 ConnectionError，第三次成功
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2. permanent：ConnectionError 重试耗尽后进入 error_handler
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3. invalid：ValueError 不重试，直接进入 error_handler
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4. loop：故意无限循环，由 recursion_limit 终止
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需要 langgraph >= 1.2（节点级 error_handler / NodeError 是 1.2+ 能力）。
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运行：
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python 09_retry_and_errors.py
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"""
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from typing import Literal, TypedDict
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from langgraph.errors import GraphRecursionError, NodeError
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from langgraph.graph import END, START, StateGraph
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from langgraph.runtime import Runtime
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from langgraph.types import Command, RetryPolicy
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# -----------------------------------------------------------------------------
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# 1. 节点重试 + 重试耗尽后的错误恢复
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# -----------------------------------------------------------------------------
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class ApiState(TypedDict):
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mode: Literal["transient", "permanent", "invalid"]
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result: str
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status: str
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error: str
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def call_unstable_api(
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state: ApiState,
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runtime: Runtime,
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) -> Command[Literal["finalize"]]:
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"""模拟一个不稳定的外部 API。
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注意：失败尝试产生的 State 更新不会提交，因此不要用 State 字段记录
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当前重试次数。LangGraph 已通过 execution_info 提供 node_attempt。
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"""
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attempt = runtime.execution_info.node_attempt
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mode = state["mode"]
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print(f" call_unstable_api：第 {attempt} 次尝试，mode={mode}")
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if mode == "transient" and attempt < 3:
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raise ConnectionError("模拟网络抖动")
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if mode == "permanent":
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raise ConnectionError("模拟上游服务持续不可用")
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if mode == "invalid":
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# ValueError 通常代表参数/业务错误，重复请求不会自行恢复。
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raise ValueError("模拟无效请求参数")
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return Command(
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update={
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"result": "API 调用成功",
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"status": "success",
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"error": "",
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},
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goto="finalize",
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)
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def api_error_handler(
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state: ApiState,
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error: NodeError,
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) -> Command[Literal["finalize"]]:
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"""在不重试或重试耗尽后，将异常转成可控的业务结果。"""
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print(
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f" error_handler：节点 {error.node} 最终失败："
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f"{type(error.error).__name__}: {error.error}"
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)
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return Command(
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update={
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"result": "已启用降级结果，请稍后重试",
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"status": "recovered",
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"error": f"{type(error.error).__name__}: {error.error}",
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},
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goto="finalize",
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)
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def finalize(state: ApiState) -> dict:
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"""统一收尾节点；成功和降级路径都会到达这里。"""
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print(f" finalize：status={state['status']}, result={state['result']}")
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return {}
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api_builder = StateGraph(ApiState)
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api_builder.add_node(
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"call_api",
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call_unstable_api,
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retry_policy=RetryPolicy(
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max_attempts=3, # 包含第一次，共最多执行 3 次
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initial_interval=0.1, # 第一次重试前等待 0.1 秒
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backoff_factor=2.0, # 后续等待时间指数增长
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max_interval=1.0,
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jitter=False, # 教学输出保持可预测
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retry_on=ConnectionError,
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),
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error_handler=api_error_handler,
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)
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api_builder.add_node("finalize", finalize)
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api_builder.add_edge(START, "call_api")
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api_builder.add_edge("finalize", END)
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api_graph = api_builder.compile()
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def run_api_case(mode: Literal["transient", "permanent", "invalid"]) -> None:
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"""运行一个重试/错误恢复场景，并打印最终状态。"""
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print(f"\n=== 场景：{mode} ===")
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result = api_graph.invoke(
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{
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"mode": mode,
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"result": "",
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"status": "pending",
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"error": "",
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}
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)
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print(" 最终状态：", result)
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# -----------------------------------------------------------------------------
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# 2. 循环保护：recursion_limit
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# -----------------------------------------------------------------------------
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class LoopState(TypedDict):
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count: int
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def loop_forever(state: LoopState) -> dict:
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"""故意不提供终止条件，用于演示 GraphRecursionError。"""
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next_count = state["count"] + 1
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print(f" loop：count={next_count}")
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return {"count": next_count}
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loop_builder = StateGraph(LoopState)
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loop_builder.add_node("loop", loop_forever)
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loop_builder.add_edge(START, "loop")
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loop_builder.add_edge("loop", "loop")
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loop_graph = loop_builder.compile()
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def run_loop_case() -> None:
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"""用较小的递归限制安全终止错误设计的循环图。"""
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print("\n=== 场景：loop / recursion_limit ===")
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try:
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loop_graph.invoke(
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{"count": 0},
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config={"recursion_limit": 4},
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)
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except GraphRecursionError as exc:
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print(f" 已捕获 {type(exc).__name__}")
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print(" 原因：图在限制步数内没有到达 END。")
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print(" 正确做法：为循环增加业务终止条件；recursion_limit 只是最后防线。")
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# -----------------------------------------------------------------------------
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# 3. 入口
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# -----------------------------------------------------------------------------
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def main() -> None:
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print("=== LangGraph Retry & Errors ===")
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run_api_case("transient")
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run_api_case("permanent")
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run_api_case("invalid")
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run_loop_case()
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print("\n=== 观察结论 ===")
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print("1. ConnectionError 会按照 RetryPolicy 自动重试。")
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print("2. ValueError 不符合 retry_on，第一次失败后立即交给 error_handler。")
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print("3. 重试耗尽后，error_handler 可用 Command 更新状态并走降级路径。")
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print("4. recursion_limit 能阻止失控循环，但不能替代正确的终止条件。")
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if __name__ == "__main__":
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main()
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