分层代理团队¶
在我们之前的示例（代理监督者）中，我们介绍了单个监督节点的概念，用于在不同的工作节点之间分配任务。
但如果单个工人的任务变得过于复杂怎么办？如果工人的数量变得太大怎么办？
对于某些应用程序，系统可能更有效率地将工作分发为_分层_的方式。
你可以通过组合不同的子图并创建一个顶级监督者，以及中间层监督者来实现这一点。
为了实现这一点，让我们构建一个简单的研究助手！该图将看起来类似于以下内容：
这个笔记本受论文AutoGen: 通过多代理对话实现下一代LLM应用，由Wu等人撰写启发。在本笔记本的剩余部分中，你将：
- 定义代理的工具以访问网络和写文件
- 定义一些工具以帮助创建图和代理
- 创建并定义每个团队（网络研究 + 文档编写）
- 将所有内容组合在一起。
环境设置¶
首先，让我们安装所需的软件包并设置我们的API密钥
%%capture --no-stderr
%pip install -U langgraph langchain_community langchain_anthropic langchain_experimental
import getpass
import os
def _set_if_undefined(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"Please provide your {var}")
_set_if_undefined("OPENAI_API_KEY")
_set_if_undefined("TAVILY_API_KEY")
为LangGraph开发设置LangSmith
注册LangSmith，可以快速发现并解决项目中的问题，提高LangGraph项目的性能。LangSmith允许您使用跟踪数据来调试、测试和监控使用LangGraph构建的LLM应用程序——更多关于如何开始的内容，请参阅这里。
创建工具¶
每个团队将由一个或多个代理组成，每个代理可以使用一个或多个工具。下面定义所有将由不同团队使用的工具。
我们先从研究团队开始。
研究团队工具
研究团队可以使用搜索引擎和URL抓取器来在网络上查找信息。请随意添加其他功能以提高团队的性能！
from typing import Annotated, List
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_core.tools import tool
tavily_tool = TavilySearchResults(max_results=5)
@tool
def scrape_webpages(urls: List[str]) -> str:
"""Use requests and bs4 to scrape the provided web pages for detailed information."""
loader = WebBaseLoader(urls)
docs = loader.load()
return "\n\n".join(
[
f'<Document name="{doc.metadata.get("title", "")}">\n{doc.page_content}\n</Document>'
for doc in docs
]
)
API Reference: WebBaseLoader | TavilySearchResults | tool
文档编写团队工具
接下来，我们将提供一些供文档编写团队使用的工具。 我们定义了一些最基本的文件访问工具如下。
请注意，这将使代理能够访问您的文件系统，这可能会带来安全隐患。我们也没有对工具描述进行性能优化。
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import Dict, Optional
from langchain_experimental.utilities import PythonREPL
from typing_extensions import TypedDict
_TEMP_DIRECTORY = TemporaryDirectory()
WORKING_DIRECTORY = Path(_TEMP_DIRECTORY.name)
@tool
def create_outline(
points: Annotated[List[str], "List of main points or sections."],
file_name: Annotated[str, "File path to save the outline."],
) -> Annotated[str, "Path of the saved outline file."]:
"""Create and save an outline."""
with (WORKING_DIRECTORY / file_name).open("w") as file:
for i, point in enumerate(points):
file.write(f"{i + 1}. {point}\n")
return f"Outline saved to {file_name}"
@tool
def read_document(
file_name: Annotated[str, "File path to read the document from."],
start: Annotated[Optional[int], "The start line. Default is 0"] = None,
end: Annotated[Optional[int], "The end line. Default is None"] = None,
) -> str:
"""Read the specified document."""
with (WORKING_DIRECTORY / file_name).open("r") as file:
lines = file.readlines()
if start is None:
start = 0
return "\n".join(lines[start:end])
@tool
def write_document(
content: Annotated[str, "Text content to be written into the document."],
file_name: Annotated[str, "File path to save the document."],
) -> Annotated[str, "Path of the saved document file."]:
"""Create and save a text document."""
with (WORKING_DIRECTORY / file_name).open("w") as file:
file.write(content)
return f"Document saved to {file_name}"
@tool
def edit_document(
file_name: Annotated[str, "Path of the document to be edited."],
inserts: Annotated[
Dict[int, str],
"Dictionary where key is the line number (1-indexed) and value is the text to be inserted at that line.",
],
) -> Annotated[str, "Path of the edited document file."]:
"""Edit a document by inserting text at specific line numbers."""
with (WORKING_DIRECTORY / file_name).open("r") as file:
lines = file.readlines()
sorted_inserts = sorted(inserts.items())
for line_number, text in sorted_inserts:
if 1 <= line_number <= len(lines) + 1:
lines.insert(line_number - 1, text + "\n")
else:
return f"Error: Line number {line_number} is out of range."
with (WORKING_DIRECTORY / file_name).open("w") as file:
file.writelines(lines)
return f"Document edited and saved to {file_name}"
# Warning: This executes code locally, which can be unsafe when not sandboxed
repl = PythonREPL()
@tool
def python_repl_tool(
code: Annotated[str, "The python code to execute to generate your chart."],
):
"""Use this to execute python code. If you want to see the output of a value,
you should print it out with `print(...)`. This is visible to the user."""
try:
result = repl.run(code)
except BaseException as e:
return f"Failed to execute. Error: {repr(e)}"
return f"Successfully executed:\n\`\`\`python\n{code}\n\`\`\`\nStdout: {result}"
API Reference: PythonREPL
辅助工具¶
我们将创建一些实用函数，以便在以下情况下使代码更加简洁：
- 创建一个工作代理。
- 为子图创建一个监督者。
这些实用函数将简化最终的图组合代码，使其更容易理解。
from typing import List, Optional, Literal
from langchain_core.language_models.chat_models import BaseChatModel
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.types import Command
from langchain_core.messages import HumanMessage, trim_messages
class State(MessagesState):
next: str
def make_supervisor_node(llm: BaseChatModel, members: list[str]) -> str:
options = ["FINISH"] + members
system_prompt = (
"You are a supervisor tasked with managing a conversation between the"
f" following workers: {members}. Given the following user request,"
" respond with the worker to act next. Each worker will perform a"
" task and respond with their results and status. When finished,"
" respond with FINISH."
)
class Router(TypedDict):
"""Worker to route to next. If no workers needed, route to FINISH."""
next: Literal[*options]
def supervisor_node(state: State) -> Command[Literal[*members, "__end__"]]:
"""An LLM-based router."""
messages = [
{"role": "system", "content": system_prompt},
] + state["messages"]
response = llm.with_structured_output(Router).invoke(messages)
goto = response["next"]
if goto == "FINISH":
goto = END
return Command(goto=goto, update={"next": goto})
return supervisor_node
API Reference: BaseChatModel | StateGraph | START | END | Command | HumanMessage | trim_messages
定义代理团队¶
现在我们可以定义我们的层级团队。“选择你的玩家！”
研究团队¶
研究团队将包括一个搜索代理和一个网络抓取“研究代理”作为两个工作节点。让我们创建这些代理，以及团队主管。
from langchain_core.messages import HumanMessage
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
llm = ChatOpenAI(model="gpt-4o")
search_agent = create_react_agent(llm, tools=[tavily_tool])
def search_node(state: State) -> Command[Literal["supervisor"]]:
result = search_agent.invoke(state)
return Command(
update={
"messages": [
HumanMessage(content=result["messages"][-1].content, name="search")
]
},
# We want our workers to ALWAYS "report back" to the supervisor when done
goto="supervisor",
)
web_scraper_agent = create_react_agent(llm, tools=[scrape_webpages])
def web_scraper_node(state: State) -> Command[Literal["supervisor"]]:
result = web_scraper_agent.invoke(state)
return Command(
update={
"messages": [
HumanMessage(content=result["messages"][-1].content, name="web_scraper")
]
},
# We want our workers to ALWAYS "report back" to the supervisor when done
goto="supervisor",
)
research_supervisor_node = make_supervisor_node(llm, ["search", "web_scraper"])
API Reference: HumanMessage | ChatOpenAI | create_react_agent
现在我们已经创建了必要的组件，定义它们之间的交互就变得很简单。将节点添加到团队图中，并定义边，这些边决定了转换条件。
research_builder = StateGraph(State)
research_builder.add_node("supervisor", research_supervisor_node)
research_builder.add_node("search", search_node)
research_builder.add_node("web_scraper", web_scraper_node)
research_builder.add_edge(START, "supervisor")
research_graph = research_builder.compile()
from IPython.display import Image, display
display(Image(research_graph.get_graph().draw_mermaid_png()))
我们可以直接将这项工作交给团队。请在下方尝试。
for s in research_graph.stream(
{"messages": [("user", "when is Taylor Swift's next tour?")]},
{"recursion_limit": 100},
):
print(s)
print("---")
{'supervisor': {'next': 'search'}}
---
{'search': {'messages': [HumanMessage(content="Taylor Swift's next tour is The Eras Tour, which includes both U.S. and international dates. She announced additional U.S. dates for 2024. You can find more details about the tour and ticket information on platforms like Ticketmaster and official announcements.", additional_kwargs={}, response_metadata={}, name='search', id='4df8687b-50a8-4342-aad5-680732c4a10f')]}}
---
{'supervisor': {'next': 'web_scraper'}}
---
{'web_scraper': {'messages': [HumanMessage(content='Taylor Swift\'s next tour is "The Eras Tour." Here are some of the upcoming international dates for 2024 that were listed on Ticketmaster:\n\n1. **Toronto, ON, Canada** at Rogers Centre\n - November 21, 2024\n - November 22, 2024\n - November 23, 2024\n\n2. **Vancouver, BC, Canada** at BC Place\n - December 6, 2024\n - December 7, 2024\n - December 8, 2024\n\nFor the most current information and additional dates, you can check platforms like Ticketmaster or Taylor Swift\'s [official website](https://www.taylorswift.com/events).', additional_kwargs={}, response_metadata={}, name='web_scraper', id='27524ebc-d179-4733-831d-ee10a58a2528')]}}
---
{'supervisor': {'next': '__end__'}}
---
文档编写团队¶
使用类似的方法创建文档编写团队如下。这一次，我们将为每个代理提供不同的文件编写工具访问权限。
请注意，我们在这里给代理提供了文件系统访问权限，这在某些情况下是不安全的。
llm = ChatOpenAI(model="gpt-4o")
doc_writer_agent = create_react_agent(
llm,
tools=[write_document, edit_document, read_document],
prompt=(
"You can read, write and edit documents based on note-taker's outlines. "
"Don't ask follow-up questions."
),
)
def doc_writing_node(state: State) -> Command[Literal["supervisor"]]:
result = doc_writer_agent.invoke(state)
return Command(
update={
"messages": [
HumanMessage(content=result["messages"][-1].content, name="doc_writer")
]
},
# We want our workers to ALWAYS "report back" to the supervisor when done
goto="supervisor",
)
note_taking_agent = create_react_agent(
llm,
tools=[create_outline, read_document],
prompt=(
"You can read documents and create outlines for the document writer. "
"Don't ask follow-up questions."
),
)
def note_taking_node(state: State) -> Command[Literal["supervisor"]]:
result = note_taking_agent.invoke(state)
return Command(
update={
"messages": [
HumanMessage(content=result["messages"][-1].content, name="note_taker")
]
},
# We want our workers to ALWAYS "report back" to the supervisor when done
goto="supervisor",
)
chart_generating_agent = create_react_agent(
llm, tools=[read_document, python_repl_tool]
)
def chart_generating_node(state: State) -> Command[Literal["supervisor"]]:
result = chart_generating_agent.invoke(state)
return Command(
update={
"messages": [
HumanMessage(
content=result["messages"][-1].content, name="chart_generator"
)
]
},
# We want our workers to ALWAYS "report back" to the supervisor when done
goto="supervisor",
)
doc_writing_supervisor_node = make_supervisor_node(
llm, ["doc_writer", "note_taker", "chart_generator"]
)
在创建了这些对象之后，我们可以形成图。
# Create the graph here
paper_writing_builder = StateGraph(State)
paper_writing_builder.add_node("supervisor", doc_writing_supervisor_node)
paper_writing_builder.add_node("doc_writer", doc_writing_node)
paper_writing_builder.add_node("note_taker", note_taking_node)
paper_writing_builder.add_node("chart_generator", chart_generating_node)
paper_writing_builder.add_edge(START, "supervisor")
paper_writing_graph = paper_writing_builder.compile()
from IPython.display import Image, display
display(Image(paper_writing_graph.get_graph().draw_mermaid_png()))
for s in paper_writing_graph.stream(
{
"messages": [
(
"user",
"Write an outline for poem about cats and then write the poem to disk.",
)
]
},
{"recursion_limit": 100},
):
print(s)
print("---")
{'supervisor': {'next': 'note_taker'}}
---
{'note_taker': {'messages': [HumanMessage(content='The outline for the poem about cats has been created and saved as "cats_poem_outline.txt".', additional_kwargs={}, response_metadata={}, name='note_taker', id='14a5d8ca-9092-416f-96ee-ba16686e8658')]}}
---
{'supervisor': {'next': 'doc_writer'}}
---
{'doc_writer': {'messages': [HumanMessage(content='The poem about cats has been written and saved as "cats_poem.txt".', additional_kwargs={}, response_metadata={}, name='doc_writer', id='c4e31a94-63ae-4632-9e80-1166f3f138b2')]}}
---
{'supervisor': {'next': '__end__'}}
---
添加层级¶
在这个设计中，我们强制执行自顶向下的规划策略。我们已经创建了两个图，但必须决定如何在两个图之间路由工作。
我们将创建一个_第三个_图来协调前两个图，并添加一些连接器来定义这个顶层状态是如何在不同的图之间共享的。
from langchain_core.messages import BaseMessage
llm = ChatOpenAI(model="gpt-4o")
teams_supervisor_node = make_supervisor_node(llm, ["research_team", "writing_team"])
API Reference: BaseMessage
def call_research_team(state: State) -> Command[Literal["supervisor"]]:
response = research_graph.invoke({"messages": state["messages"][-1]})
return Command(
update={
"messages": [
HumanMessage(
content=response["messages"][-1].content, name="research_team"
)
]
},
goto="supervisor",
)
def call_paper_writing_team(state: State) -> Command[Literal["supervisor"]]:
response = paper_writing_graph.invoke({"messages": state["messages"][-1]})
return Command(
update={
"messages": [
HumanMessage(
content=response["messages"][-1].content, name="writing_team"
)
]
},
goto="supervisor",
)
# Define the graph.
super_builder = StateGraph(State)
super_builder.add_node("supervisor", teams_supervisor_node)
super_builder.add_node("research_team", call_research_team)
super_builder.add_node("writing_team", call_paper_writing_team)
super_builder.add_edge(START, "supervisor")
super_graph = super_builder.compile()
from IPython.display import Image, display
display(Image(super_graph.get_graph().draw_mermaid_png()))
for s in super_graph.stream(
{
"messages": [
("user", "Research AI agents and write a brief report about them.")
],
},
{"recursion_limit": 150},
):
print(s)
print("---")
{'supervisor': {'next': 'research_team'}}
---
{'research_team': {'messages': [HumanMessage(content="**AI Agents Overview 2023**\n\nAI agents are sophisticated technologies that automate and enhance various processes across industries, becoming increasingly integral to business operations. In 2023, these agents are notable for their advanced capabilities in communication, data visualization, and language processing.\n\n**Popular AI Agents in 2023:**\n1. **Auto GPT**: This agent is renowned for its seamless integration abilities, significantly impacting industries by improving communication and operational workflows.\n2. **ChartGPT**: Specializing in data visualization, ChartGPT enables users to interact with data innovatively, providing deeper insights and comprehension.\n3. **LLMops**: With advanced language capabilities, LLMops is a versatile tool seeing widespread use across multiple sectors.\n\n**Market Trends:**\nThe AI agents market is experiencing rapid growth, with significant advancements anticipated by 2030. There's a growing demand for AI agents in personalized interactions, particularly within customer service, healthcare, and marketing sectors. This trend is fueled by the need for more efficient and tailored customer experiences.\n\n**Key Players:**\nLeading companies such as Microsoft, IBM, Google, Oracle, and AWS are key players in the AI agents market, highlighting the widespread adoption and investment in these technologies.\n\n**Technological Innovations:**\nAI agents are being developed alongside simulation technologies for robust testing and deployment environments. Innovations in generative AI are accelerating, supported by advancements in large language models and platforms like ChatGPT.\n\n**Applications in Healthcare:**\nIn healthcare, AI agents are automating routine tasks, allowing medical professionals to focus more on patient care. They're poised to significantly enhance healthcare delivery and efficiency.\n\n**Future Prospects:**\nThe future of AI agents is promising, with continued evolution and integration into various platforms and ecosystems, offering more seamless and intelligent interactions. As these technologies advance, they are expected to redefine business operations and customer interactions.", additional_kwargs={}, response_metadata={}, name='research_team', id='5f6606e0-838c-406c-b50d-9f9f6a076322')]}}
---
{'supervisor': {'next': 'writing_team'}}
---
{'writing_team': {'messages': [HumanMessage(content="Here are the contents of the documents:\n\n### AI Agents Overview 2023\n\n**AI Agents Overview 2023**\n\nAI agents are sophisticated technologies that automate and enhance various processes across industries, becoming increasingly integral to business operations. In 2023, these agents are notable for their advanced capabilities in communication, data visualization, and language processing.\n\n**Popular AI Agents in 2023:**\n1. **Auto GPT**: This agent is renowned for its seamless integration abilities, significantly impacting industries by improving communication and operational workflows.\n2. **ChartGPT**: Specializing in data visualization, ChartGPT enables users to interact with data innovatively, providing deeper insights and comprehension.\n3. **LLMops**: With advanced language capabilities, LLMops is a versatile tool seeing widespread use across multiple sectors.\n\n**Market Trends:**\nThe AI agents market is experiencing rapid growth, with significant advancements anticipated by 2030. There's a growing demand for AI agents in personalized interactions, particularly within customer service, healthcare, and marketing sectors. This trend is fueled by the need for more efficient and tailored customer experiences.\n\n**Key Players:**\nLeading companies such as Microsoft, IBM, Google, Oracle, and AWS are key players in the AI agents market, highlighting the widespread adoption and investment in these technologies.\n\n**Technological Innovations:**\nAI agents are being developed alongside simulation technologies for robust testing and deployment environments. Innovations in generative AI are accelerating, supported by advancements in large language models and platforms like ChatGPT.\n\n**Applications in Healthcare:**\nIn healthcare, AI agents are automating routine tasks, allowing medical professionals to focus more on patient care. They're poised to significantly enhance healthcare delivery and efficiency.\n\n**Future Prospects:**\nThe future of AI agents is promising, with continued evolution and integration into various platforms and ecosystems, offering more seamless and intelligent interactions. As these technologies advance, they are expected to redefine business operations and customer interactions.\n\n### AI_Agents_Overview_2023_Outline\n\n1. Introduction to AI Agents in 2023\n2. Popular AI Agents: Auto GPT, ChartGPT, LLMops\n3. Market Trends and Growth\n4. Key Players in the AI Agents Market\n5. Technological Innovations: Simulation and Generative AI\n6. Applications of AI Agents in Healthcare\n7. Future Prospects of AI Agents", additional_kwargs={}, response_metadata={}, name='writing_team', id='851bd8a6-740e-488c-8928-1f9e05e96ea0')]}}
---
{'supervisor': {'next': 'writing_team'}}
---
{'writing_team': {'messages': [HumanMessage(content='The documents have been successfully created and saved:\n\n1. **AI_Agents_Overview_2023.txt** - Contains the detailed overview of AI agents in 2023.\n2. **AI_Agents_Overview_2023_Outline.txt** - Contains the outline of the document.', additional_kwargs={}, response_metadata={}, name='writing_team', id='c87c0778-a085-4a8e-8ee1-9b43b9b0b143')]}}
---
{'supervisor': {'next': '__end__'}}
---