Workflows and Agents¶

from typing_extensions import TypedDict
from langgraph.graph import StateGraph, START, END
from IPython.display import Image, display


# Graph state
class State(TypedDict):
    topic: str
    joke: str
    improved_joke: str
    final_joke: str


# Nodes
def generate_joke(state: State):
    """首次调用 LLM 以生成初始笑话"""

    msg = llm.invoke(f"写一个关于 {state['topic']} 的简短笑话")
    return {"joke": msg.content}


def check_punchline(state: State):
    """检查笑话是否有包袱的 gate 函数"""

    # 简单检查 - 笑话是否包含 "?" 或 "!"
    if "?" in state["joke"] or "!" in state["joke"]:
        return "Pass"
    return "Fail"


def improve_joke(state: State):
    """第二次调用 LLM 来改进笑话"""

    msg = llm.invoke(f"通过添加文字游戏来让这个笑话更有趣：{state['joke']}")
    return {"improved_joke": msg.content}


def polish_joke(state: State):
    """第三次调用 LLM 进行最终润色"""

    msg = llm.invoke(f"为这个笑话添加一个意想不到的转折：{state['improved_joke']}")
    return {"final_joke": msg.content}


# 构建工作流
workflow = StateGraph(State)

# 添加节点
workflow.add_node("generate_joke", generate_joke)
workflow.add_node("improve_joke", improve_joke)
workflow.add_node("polish_joke", polish_joke)

# 添加边以连接节点
workflow.add_edge(START, "generate_joke")
workflow.add_conditional_edges(
    "generate_joke", check_punchline, {"Fail": "improve_joke", "Pass": END}
)
workflow.add_edge("improve_joke", "polish_joke")
workflow.add_edge("polish_joke", END)

# 编译
chain = workflow.compile()

# 显示工作流
display(Image(chain.get_graph().draw_mermaid_png()))

# 调用
state = chain.invoke({"topic": "cats"})
print("Initial joke:")
print(state["joke"])
print("\n--- --- ---\n")
if "improved_joke" in state:
    print("Improved joke:")
    print(state["improved_joke"])
    print("\n--- --- ---\n")

    print("Final joke:")
    print(state["final_joke"])
else:
    print("笑话未能通过质量检测 - 未检测到包袱！")

from langgraph.func import entrypoint, task


# Tasks
@task
def generate_joke(topic: str):
    """首次调用 LLM 以生成初始笑话"""
    msg = llm.invoke(f"写一个关于 {topic} 的简短笑话")
    return msg.content


def check_punchline(joke: str):
    """检查笑话是否有包袱的 gate 函数"""
    # 简单检查 - 笑话是否包含 "?" 或 "!"
    if "?" in joke or "!" in joke:
        return "Fail"

    return "Pass"


@task
def improve_joke(joke: str):
    """第二次调用 LLM 来改进笑话"""
    msg = llm.invoke(f"通过添加文字游戏来让这个笑话更有趣：{joke}")
    return msg.content


@task
def polish_joke(joke: str):
    """第三次调用 LLM 进行最终润色"""
    msg = llm.invoke(f"为这个笑话添加一个意想不到的转折：{joke}")
    return msg.content


@entrypoint()
def prompt_chaining_workflow(topic: str):
    original_joke = generate_joke(topic).result()
    if check_punchline(original_joke) == "Pass":
        return original_joke

    improved_joke = improve_joke(original_joke).result()
    return polish_joke(improved_joke).result()

# 调用
for step in prompt_chaining_workflow.stream("cats", stream_mode="updates"):
    print(step)
    print("\n")

# Graph state
class State(TypedDict):
    topic: str
    joke: str
    story: str
    poem: str
    combined_output: str


# Nodes
def call_llm_1(state: State):
    """首次调用 LLM 以生成初始笑话"""

    msg = llm.invoke(f"写一个关于 {state['topic']} 的笑话")
    return {"joke": msg.content}


def call_llm_2(state: State):
    """第二次调用 LLM 来生成故事"""

    msg = llm.invoke(f"写一个关于 {state['topic']} 的故事")
    return {"story": msg.content}


def call_llm_3(state: State):
    """第三次调用 LLM 来生成诗歌"""

    msg = llm.invoke(f"写一首关于 {state['topic']} 的诗")
    return {"poem": msg.content}


def aggregator(state: State):
    """将笑话和故事合并为一个输出"""

    combined = f"这是关于 {state['topic']} 的故事、笑话和诗！\n\n"
    combined += f"STORY:\n{state['story']}\n\n"
    combined += f"JOKE:\n{state['joke']}\n\n"
    combined += f"POEM:\n{state['poem']}"
    return {"combined_output": combined}


# 构建工作流
parallel_builder = StateGraph(State)

# 添加节点
parallel_builder.add_node("call_llm_1", call_llm_1)
parallel_builder.add_node("call_llm_2", call_llm_2)
parallel_builder.add_node("call_llm_3", call_llm_3)
parallel_builder.add_node("aggregator", aggregator)

# 添加边以连接节点
parallel_builder.add_edge(START, "call_llm_1")
parallel_builder.add_edge(START, "call_llm_2")
parallel_builder.add_edge(START, "call_llm_3")
parallel_builder.add_edge("call_llm_1", "aggregator")
parallel_builder.add_edge("call_llm_2", "aggregator")
parallel_builder.add_edge("call_llm_3", "aggregator")
parallel_builder.add_edge("aggregator", END)
parallel_workflow = parallel_builder.compile()

# 显示工作流
display(Image(parallel_workflow.get_graph().draw_mermaid_png()))

# 调用
state = parallel_workflow.invoke({"topic": "cats"})
print(state["combined_output"])

@task
def call_llm_1(topic: str):
    """首次调用 LLM 以生成初始笑话"""
    msg = llm.invoke(f"写一个关于 {topic} 的笑话")
    return msg.content


@task
def call_llm_2(topic: str):
    """第二次调用 LLM 来生成故事"""
    msg = llm.invoke(f"写一个关于 {topic} 的故事")
    return msg.content


@task
def call_llm_3(topic):
    """第三次调用 LLM 来生成诗歌"""
    msg = llm.invoke(f"写一首关于 {topic} 的诗")
    return msg.content


@task
def aggregator(topic, joke, story, poem):
    """将笑话和故事合并为一个输出"""

    combined = f"这是关于 {topic} 的故事、笑话和诗！\n\n"
    combined += f"STORY:\n{story}\n\n"
    combined += f"JOKE:\n{joke}\n\n"
    combined += f"POEM:\n{poem}"
    return combined


# 构建工作流
@entrypoint()
def parallel_workflow(topic: str):
    joke_fut = call_llm_1(topic)
    story_fut = call_llm_2(topic)
    poem_fut = call_llm_3(topic)
    return aggregator(
        topic, joke_fut.result(), story_fut.result(), poem_fut.result()
    ).result()

# 调用
for step in parallel_workflow.stream("cats", stream_mode="updates"):
    print(step)
    print("\n")

from typing_extensions import Literal
from langchain_core.messages import HumanMessage, SystemMessage


# 用于路由逻辑的结构化输出模式
class Route(BaseModel):
    step: Literal["poem", "story", "joke"] = Field(
        None, description="路由过程的下一步"
    )


# 使用模式增强 LLM
router = llm.with_structured_output(Route)


# State
class State(TypedDict):
    input: str
    decision: str
    output: str


# Nodes
def llm_call_1(state: State):
    """写一个故事"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_2(state: State):
    """写一个笑话"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_3(state: State):
    """写一首诗"""

    result = llm.invoke(state["input"])
    return {"output": result.content}


def llm_call_router(state: State):
    """将输入路由到适当的节点"""

    # 使用结构化输出调用增强型 LLM 作为路由逻辑
    decision = router.invoke(
        [
            SystemMessage(
                content="根据用户请求，将输入路由到故事、笑话或诗歌。"
            ),
            HumanMessage(content=state["input"]),
        ]
    )

    return {"decision": decision.step}


# 用于将输入路由到适当节点的条件边函数
def route_decision(state: State):
    # 返回要访问的下一个节点名称
    if state["decision"] == "story":
        return "llm_call_1"
    elif state["decision"] == "joke":
        return "llm_call_2"
    elif state["decision"] == "poem":
        return "llm_call_3"


# 构建工作流
router_builder = StateGraph(State)

# 添加节点
router_builder.add_node("llm_call_1", llm_call_1)
router_builder.add_node("llm_call_2", llm_call_2)
router_builder.add_node("llm_call_3", llm_call_3)
router_builder.add_node("llm_call_router", llm_call_router)

# 添加边以连接节点
router_builder.add_edge(START, "llm_call_router")
router_builder.add_conditional_edges(
    "llm_call_router",
    route_decision,
    {  # route_decision 返回的名称 : 要访问的下一个节点名称
        "llm_call_1": "llm_call_1",
        "llm_call_2": "llm_call_2",
        "llm_call_3": "llm_call_3",
    },
)
router_builder.add_edge("llm_call_1", END)
router_builder.add_edge("llm_call_2", END)
router_builder.add_edge("llm_call_3", END)

# 编译工作流
router_workflow = router_builder.compile()

# 显示工作流
display(Image(router_workflow.get_graph().draw_mermaid_png()))

# 调用
state = router_workflow.invoke({"input": "给我写个关于猫的笑话"})
print(state["output"])

from typing_extensions import Literal
from pydantic import BaseModel
from langchain_core.messages import HumanMessage, SystemMessage


# 用于路由逻辑的结构化输出模式
class Route(BaseModel):
    step: Literal["poem", "story", "joke"] = Field(
        None, description="路由过程的下一步"
    )


# 使用模式增强 LLM
router = llm.with_structured_output(Route)


@task
def llm_call_1(input_: str):
    """写一个故事"""
    result = llm.invoke(input_)
    return result.content


@task
def llm_call_2(input_: str):
    """写一个笑话"""
    result = llm.invoke(input_)
    return result.content


@task
def llm_call_3(input_: str):
    """写一首诗"""
    result = llm.invoke(input_)
    return result.content


def llm_call_router(input_: str):
    """将输入路由到适当的节点"""
    # 使用结构化输出调用增强型 LLM 作为路由逻辑
    decision = router.invoke(
        [
            SystemMessage(
                content="根据用户请求，将输入路由到故事、笑话或诗歌。"
            ),
            HumanMessage(content=input_),
        ]
    )
    return decision.step


# 创建工作流
@entrypoint()
def router_workflow(input_: str):
    next_step = llm_call_router(input_)
    if next_step == "story":
        llm_call = llm_call_1
    elif next_step == "joke":
        llm_call = llm_call_2
    elif next_step == "poem":
        llm_call = llm_call_3

    return llm_call(input_).result()

# 调用
for step in router_workflow.stream("给我写个关于猫的笑话", stream_mode="updates"):
    print(step)
    print("\n")

from typing import Annotated, List
import operator


# 用于计划的结构化输出模式
class Section(BaseModel):
    name: str = Field(
        description="此报告部分的名称。",
    )
    description: str = Field(
        description="将在本节中介绍的主要主题和概念的简要概述。",
    )


class Sections(BaseModel):
    sections: List[Section] = Field(
        description="报告的各部分。",
    )


# 使用模式增强 LLM
planner = llm.with_structured_output(Sections)

from langgraph.types import Send


# Graph state
class State(TypedDict):
    topic: str  # 报告主题
    sections: list[Section]  # 报告部分列表
    completed_sections: Annotated[
        list, operator.add
    ]  # 所有工作器都并行写入此键
    final_report: str  # 最终报告


# Worker state
class WorkerState(TypedDict):
    section: Section
    completed_sections: Annotated[list, operator.add]


# Nodes
def orchestrator(state: State):
    """生成报告计划的协调器"""

    # 生成查询
    report_sections = planner.invoke(
        [
            SystemMessage(content="生成报告计划。"),
            HumanMessage(content=f"报告主题是：{state['topic']}"),
        ]
    )

    return {"sections": report_sections.sections}


def llm_call(state: WorkerState):
    """工作器编写报告的某一部分"""

    # 生成部分
    section = llm.invoke(
        [
            SystemMessage(
                content="编写报告的某一部分，遵循提供的名称和描述。每个部分不要包含前言。使用 markdown 格式。"
            ),
            HumanMessage(
                content=f"此部分名称为：{state['section'].name} 描述为：{state['section'].description}"
            ),
        ]
    )

    # 将更新后的部分写入已完成的部分
    return {"completed_sections": [section.content]}


def synthesizer(state: State):
    """从各个部分综合完整报告"""

    # 已完成的部分列表
    completed_sections = state["completed_sections"]

    # 将已完成的部分格式化为字符串，用作最终部分的上下文
    completed_report_sections = "\n\n---\n\n".join(completed_sections)

    return {"final_report": completed_report_sections}


# 用于创建每个部分编写的 llm_call 工作器的条件边函数
def assign_workers(state: State):
    """为计划中的每个部分分配一个工作器"""

    # 通过 Send() API 并行启动部分编写
    return [Send("llm_call", {"section": s}) for s in state["sections"]]


# 构建工作流
orchestrator_worker_builder = StateGraph(State)

# 添加节点
orchestrator_worker_builder.add_node("orchestrator", orchestrator)
orchestrator_worker_builder.add_node("llm_call", llm_call)
orchestrator_worker_builder.add_node("synthesizer", synthesizer)

# 添加边以连接节点
orchestrator_worker_builder.add_edge(START, "orchestrator")
orchestrator_worker_builder.add_conditional_edges(
    "orchestrator", assign_workers, ["llm_call"]
)
orchestrator_worker_builder.add_edge("llm_call", "synthesizer")
orchestrator_worker_builder.add_edge("synthesizer", END)

# 编译工作流
orchestrator_worker = orchestrator_worker_builder.compile()

# 显示工作流
display(Image(orchestrator_worker.get_graph().draw_mermaid_png()))

# 调用
state = orchestrator_worker.invoke({"topic": "创建一份关于 LLM 标度定律的报告"})

from IPython.display import Markdown
Markdown(state["final_report"])

from typing import List


# 用于计划的结构化输出模式
class Section(BaseModel):
    name: str = Field(
        description="此报告部分的名称。",
    )
    description: str = Field(
        description="将在本节中介绍的主要主题和概念的简要概述。",
    )


class Sections(BaseModel):
    sections: List[Section] = Field(
        description="报告的各部分。",
    )


# 使用模式增强 LLM
planner = llm.with_structured_output(Sections)


@task
def orchestrator(topic: str):
    """生成报告计划的协调器"""
    # 生成查询
    report_sections = planner.invoke(
        [
            SystemMessage(content="生成报告计划。"),
            HumanMessage(content=f"报告主题是：{topic}"),
        ]
    )

    return report_sections.sections


@task
def llm_call(section: Section):
    """工作器编写报告的某一部分"""

    # 生成部分
    result = llm.invoke(
        [
            SystemMessage(content="编写报告的某一部分。"),
            HumanMessage(
                content=f"此部分名称为：{section.name} 描述为：{section.description}"
            ),
        ]
    )

    # 将更新后的部分写入已完成的部分
    return result.content


@task
def synthesizer(completed_sections: list[str]):
    """从各个部分综合完整报告"""
    final_report = "\n\n---\n\n".join(completed_sections)
    return final_report


@entrypoint()
def orchestrator_worker(topic: str):
    sections = orchestrator(topic).result()
    section_futures = [llm_call(section) for section in sections]
    final_report = synthesizer(
        [section_fut.result() for section_fut in section_futures]
    ).result()
    return final_report

# 调用
report = orchestrator_worker.invoke("创建一份关于 LLM 标度定律的报告")
from IPython.display import Markdown
Markdown(report)

# Graph state
class State(TypedDict):
    joke: str
    topic: str
    feedback: str
    funny_or_not: str


# 用于评估的结构化输出模式
class Feedback(BaseModel):
    grade: Literal["funny", "not funny"] = Field(
        description="决定笑话是否好笑。",
    )
    feedback: str = Field(
        description="如果笑话不好笑，请提供改进的反馈。",
    )


# 使用模式增强 LLM
evaluator = llm.with_structured_output(Feedback)


# Nodes
def llm_call_generator(state: State):
    """LLM 生成笑话"""

    if state.get("feedback"):
        msg = llm.invoke(
            f"写一个关于 {state['topic']} 的笑话，但请考虑反馈：{state['feedback']}"
        )
    else:
        msg = llm.invoke(f"写一个关于 {state['topic']} 的笑话")
    return {"joke": msg.content}


def llm_call_evaluator(state: State):
    """LLM 评估笑话"""

    grade = evaluator.invoke(f"给笑话打分 {state['joke']}")
    return {"funny_or_not": grade.grade, "feedback": grade.feedback}


# 用于根据评估器的反馈将决策路由回笑话生成器或结束的条件边函数
def route_joke(state: State):
    """根据评估器的反馈决定是路由回笑话生成器还是结束"""

    if state["funny_or_not"] == "funny":
        return "Accepted"
    elif state["funny_or_not"] == "not funny":
        return "Rejected + Feedback"


# 构建工作流
optimizer_builder = StateGraph(State)

# 添加节点
optimizer_builder.add_node("llm_call_generator", llm_call_generator)
optimizer_builder.add_node("llm_call_evaluator", llm_call_evaluator)

# 添加边以连接节点
optimizer_builder.add_edge(START, "llm_call_generator")
optimizer_builder.add_edge("llm_call_generator", "llm_call_evaluator")
optimizer_builder.add_conditional_edges(
    "llm_call_evaluator",
    route_joke,
    {  # route_joke 返回的名称 : 要访问的下一个节点名称
        "Accepted": END,
        "Rejected + Feedback": "llm_call_generator",
    },
)

# 编译工作流
optimizer_workflow = optimizer_builder.compile()

# 显示工作流
display(Image(optimizer_workflow.get_graph().draw_mermaid_png()))

# 调用
state = optimizer_workflow.invoke({"topic": "Cats"})
print(state["joke"])

# 用于评估的结构化输出模式
class Feedback(BaseModel):
    grade: Literal["funny", "not funny"] = Field(
        description="决定笑话是否好笑。",
    )
    feedback: str = Field(
        description="如果笑话不好笑，请提供改进的反馈。",
    )


# 使用模式增强 LLM
evaluator = llm.with_structured_output(Feedback)


# Nodes
@task
def llm_call_generator(topic: str, feedback: Feedback):
    """LLM 生成笑话"""
    if feedback:
        msg = llm.invoke(
            f"写一个关于 {topic} 的笑话，但请考虑反馈：{feedback}"
        )
    else:
        msg = llm.invoke(f"写一个关于 {topic} 的笑话")
    return msg.content


@task
def llm_call_evaluator(joke: str):
    """LLM 评估笑话"""
    feedback = evaluator.invoke(f"给笑话打分 {joke}")
    return feedback


@entrypoint()
def optimizer_workflow(topic: str):
    feedback = None
    while True:
        joke = llm_call_generator(topic, feedback).result()
        feedback = llm_call_evaluator(joke).result()
        if feedback.grade == "funny":
            break

    return joke

# 调用
for step in optimizer_workflow.stream("Cats", stream_mode="updates"):
    print(step)
    print("\n")

from langgraph.graph import MessagesState
from langchain_core.messages import SystemMessage, HumanMessage, ToolMessage


# Nodes
def llm_call(state: MessagesState):
    """LLM 决定是否调用工具"""

    return {
        "messages": [
            llm_with_tools.invoke(
                [
                    SystemMessage(
                        content="您是一个乐于助人的助手，负责对一组输入执行算术运算。"
                    )
                ]
                + state["messages"]
            )
        ]
    }


def tool_node(state: dict):
    """执行工具调用"""

    result = []
    for tool_call in state["messages"][-1].tool_calls:
        tool = tools_by_name[tool_call["name"]]
        observation = tool.invoke(tool_call["args"])
        result.append(ToolMessage(content=observation, tool_call_id=tool_call["id"]))
    return {"messages": result}


# 用于根据 LLM 是否进行了工具调用来路由到工具节点或结束的条件边函数
def should_continue(state: MessagesState) -> Literal["environment", END]:
    """根据 LLM 是否进行了工具调用来决定是继续循环还是停止"""

    messages = state["messages"]
    last_message = messages[-1]
    # 如果 LLM 进行工具调用，则执行操作
    if last_message.tool_calls:
        return "Action"
    # 否则，我们停止（回复用户）
    return END


# 构建工作流
agent_builder = StateGraph(MessagesState)

# 添加节点
agent_builder.add_node("llm_call", llm_call)
agent_builder.add_node("environment", tool_node)

# 添加边以连接节点
agent_builder.add_edge(START, "llm_call")
agent_builder.add_conditional_edges(
    "llm_call",
    should_continue,
    {
        # should_continue 返回的名称 : 要访问的下一个节点名称
        "Action": "environment",
        END: END,
    },
)
agent_builder.add_edge("environment", "llm_call")

# 编译 Agent
agent = agent_builder.compile()

# 显示 Agent
display(Image(agent.get_graph(xray=True).draw_mermaid_png()))

# 调用
messages = [HumanMessage(content="将 3 和 4 相加。")]
messages = agent.invoke({"messages": messages})
for m in messages["messages"]:
    m.pretty_print()

from langgraph.graph import add_messages
from langchain_core.messages import (
    SystemMessage,
    HumanMessage,
    BaseMessage,
    ToolCall,
)


@task
def call_llm(messages: list[BaseMessage]):
    """LLM 决定是否调用工具"""
    return llm_with_tools.invoke(
        [
            SystemMessage(
                content="您是一个乐于助人的助手，负责对一组输入执行算术运算。"
            )
        ]
        + messages
    )


@task
def call_tool(tool_call: ToolCall):
    """执行工具调用"""
    tool = tools_by_name[tool_call["name"]]
    return tool.invoke(tool_call)


@entrypoint()
def agent(messages: list[BaseMessage]):
    llm_response = call_llm(messages).result()

    while True:
        if not llm_response.tool_calls:
            break

        # 执行工具
        tool_result_futures = [
            call_tool(tool_call) for tool_call in llm_response.tool_calls
        ]
        tool_results = [fut.result() for fut in tool_result_futures]
        messages = add_messages(messages, [llm_response, *tool_results])
        llm_response = call_llm(messages).result()

    messages = add_messages(messages, llm_response)
    return messages

# 调用
messages = [HumanMessage(content="将 3 和 4 相加。")]
for chunk in agent.stream(messages, stream_mode="updates"):
    print(chunk)
    print("\n")