如何处理工具调用错误¶
LLM 在调用工具方面并不完美。模型可能会尝试调用不存在的工具，或者未能返回与请求的模式匹配的参数。诸如保持模式简单、减少一次传递的工具数量以及拥有良好的名称和描述等策略可以帮助减轻这种风险，但并非万无一失。
本指南介绍了一些将错误处理构建到图中以减轻这些故障模式的方法。
设置¶
首先，让我们安装所需的软件包并设置我们的 API 密钥
import getpass
import os
def _set_env(var: str):
if not os.environ.get(var):
os.environ[var] = getpass.getpass(f"{var}: ")
_set_env("ANTHROPIC_API_KEY")
设置 LangSmith 以进行 LangGraph 开发
注册 LangSmith 以快速发现问题并提高 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——阅读更多关于如何开始使用 此处 的信息。
使用预构建的 ToolNode¶
首先，定义一个模拟天气工具，该工具对输入查询有一些隐藏的限制。这里的目的是模拟一个真实世界的案例，即模型未能正确调用工具
from langchain_core.tools import tool
@tool
def get_weather(location: str):
"""Call to get the current weather."""
if location == "san francisco":
raise ValueError("Input queries must be proper nouns")
elif location == "San Francisco":
return "It's 60 degrees and foggy."
else:
raise ValueError("Invalid input.")
API 参考：tool
接下来，设置 ReAct 代理 的图实现。此代理将一些查询作为输入，然后重复调用工具，直到它有足够的信息来解决查询。我们将使用预构建的 ToolNode 来执行调用的工具，以及由 Anthropic 提供支持的小型快速模型
from typing import Literal
from langchain_anthropic import ChatAnthropic
from langgraph.graph import StateGraph, MessagesState, START, END
from langgraph.prebuilt import ToolNode
tool_node = ToolNode([get_weather])
model_with_tools = ChatAnthropic(
model="claude-3-haiku-20240307", temperature=0
).bind_tools([get_weather])
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_edge("tools", "agent")
app = workflow.compile()
API 参考：ChatAnthropic | StateGraph | START | END | ToolNode
from IPython.display import Image, display
try:
display(Image(app.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
pass
当您尝试调用该工具时，您可以看到模型使用错误的输入调用该工具，导致该工具抛出错误。执行该工具的预构建 ToolNode 具有一些内置的错误处理功能，可以捕获错误并将其传递回模型，以便它可以重试
response = app.invoke(
{"messages": [("human", "what is the weather in san francisco?")]},
)
for message in response["messages"]:
string_representation = f"{message.type.upper()}: {message.content}\n"
print(string_representation)
HUMAN: what is the weather in san francisco?
AI: [{'id': 'toolu_01K5tXKVRbETcs7Q8U9PHy96', 'input': {'location': 'san francisco'}, 'name': 'get_weather', 'type': 'tool_use'}]
TOOL: Error: ValueError('Input queries must be proper nouns')
Please fix your mistakes.
AI: [{'text': 'Apologies, it looks like there was an issue with the weather lookup. Let me try that again with the proper format:', 'type': 'text'}, {'id': 'toolu_01KSCsme3Du2NBazSJQ1af4b', 'input': {'location': 'San Francisco'}, 'name': 'get_weather', 'type': 'tool_use'}]
TOOL: It's 60 degrees and foggy.
AI: The current weather in San Francisco is 60 degrees and foggy.
自定义策略¶
在许多情况下，这是一个很好的默认设置，但在某些情况下，自定义回退可能更好。
例如，下面的工具需要输入特定长度的元素列表 - 对于小型模型来说很棘手！我们还将有意避免将 topic 复数化，以欺骗模型认为它应该传递一个字符串
from langchain_core.output_parsers import StrOutputParser
from pydantic import BaseModel, Field
class HaikuRequest(BaseModel):
topic: list[str] = Field(
max_length=3,
min_length=3,
)
@tool
def master_haiku_generator(request: HaikuRequest):
"""Generates a haiku based on the provided topics."""
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
chain = model | StrOutputParser()
topics = ", ".join(request.topic)
haiku = chain.invoke(f"Write a haiku about {topics}")
return haiku
tool_node = ToolNode([master_haiku_generator])
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
model_with_tools = model.bind_tools([master_haiku_generator])
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
# Define the two nodes we will cycle between
workflow.add_node("agent", call_model)
workflow.add_node("tools", tool_node)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_edge("tools", "agent")
app = workflow.compile()
response = app.invoke(
{"messages": [("human", "Write me an incredible haiku about water.")]},
{"recursion_limit": 10},
)
for message in response["messages"]:
string_representation = f"{message.type.upper()}: {message.content}\n"
print(string_representation)
API 参考：StrOutputParser
HUMAN: Write me an incredible haiku about water.
AI: [{'text': 'Here is a haiku about water:', 'type': 'text'}, {'id': 'toolu_01L13Z3Gtaym5KKgPXVyZhYn', 'input': {'topic': ['water']}, 'name': 'master_haiku_generator', 'type': 'tool_use'}]
TOOL: Error: 1 validation error for master_haiku_generator
request
Field required [type=missing, input_value={'topic': ['water']}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.7/v/missing
Please fix your mistakes.
AI: [{'text': 'Oops, my apologies. Let me try that again with the correct format:', 'type': 'text'}, {'id': 'toolu_01HCQ5uXr5kXQHBQ3FyQ1Ysk', 'input': {'topic': ['water']}, 'name': 'master_haiku_generator', 'type': 'tool_use'}]
TOOL: Error: 1 validation error for master_haiku_generator
request
Field required [type=missing, input_value={'topic': ['water']}, input_type=dict]
For further information visit https://errors.pydantic.dev/2.7/v/missing
Please fix your mistakes.
AI: [{'text': 'Hmm, it seems there was an issue with the input format. Let me try a different approach:', 'type': 'text'}, {'id': 'toolu_01RF96nruwr4nMqhLBRsbfE5', 'input': {'request': {'topic': ['water']}}, 'name': 'master_haiku_generator', 'type': 'tool_use'}]
TOOL: Error: 1 validation error for master_haiku_generator
request.topic
List should have at least 3 items after validation, not 1 [type=too_short, input_value=['water'], input_type=list]
For further information visit https://errors.pydantic.dev/2.7/v/too_short
Please fix your mistakes.
AI: [{'text': 'Ah I see, the haiku generator requires at least 3 topics. Let me provide 3 topics related to water:', 'type': 'text'}, {'id': 'toolu_011jcgHuG2Kyr87By459huqQ', 'input': {'request': {'topic': ['ocean', 'rain', 'river']}}, 'name': 'master_haiku_generator', 'type': 'tool_use'}]
TOOL: Here is a haiku about ocean, rain, and river:
Vast ocean's embrace,
Raindrops caress the river,
Nature's symphony.
AI: I hope this haiku about water captures the essence you were looking for! Let me know if you would like me to generate another one.
更好的策略可能是修剪失败的尝试以减少干扰，然后回退到更高级的模型。这是一个例子。我们还使用自定义构建的节点来调用我们的工具，而不是预构建的 ToolNode
import json
from langchain_core.messages import AIMessage, ToolMessage
from langchain_core.messages.modifier import RemoveMessage
@tool
def master_haiku_generator(request: HaikuRequest):
"""Generates a haiku based on the provided topics."""
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
chain = model | StrOutputParser()
topics = ", ".join(request.topic)
haiku = chain.invoke(f"Write a haiku about {topics}")
return haiku
def call_tool(state: MessagesState):
tools_by_name = {master_haiku_generator.name: master_haiku_generator}
messages = state["messages"]
last_message = messages[-1]
output_messages = []
for tool_call in last_message.tool_calls:
try:
tool_result = tools_by_name[tool_call["name"]].invoke(tool_call["args"])
output_messages.append(
ToolMessage(
content=json.dumps(tool_result),
name=tool_call["name"],
tool_call_id=tool_call["id"],
)
)
except Exception as e:
# Return the error if the tool call fails
output_messages.append(
ToolMessage(
content="",
name=tool_call["name"],
tool_call_id=tool_call["id"],
additional_kwargs={"error": e},
)
)
return {"messages": output_messages}
model = ChatAnthropic(model="claude-3-haiku-20240307", temperature=0)
model_with_tools = model.bind_tools([master_haiku_generator])
better_model = ChatAnthropic(model="claude-3-5-sonnet-20240620", temperature=0)
better_model_with_tools = better_model.bind_tools([master_haiku_generator])
def should_continue(state: MessagesState):
messages = state["messages"]
last_message = messages[-1]
if last_message.tool_calls:
return "tools"
return END
def should_fallback(
state: MessagesState,
) -> Literal["agent", "remove_failed_tool_call_attempt"]:
messages = state["messages"]
failed_tool_messages = [
msg
for msg in messages
if isinstance(msg, ToolMessage)
and msg.additional_kwargs.get("error") is not None
]
if failed_tool_messages:
return "remove_failed_tool_call_attempt"
return "agent"
def call_model(state: MessagesState):
messages = state["messages"]
response = model_with_tools.invoke(messages)
return {"messages": [response]}
def remove_failed_tool_call_attempt(state: MessagesState):
messages = state["messages"]
# Remove all messages from the most recent
# instance of AIMessage onwards.
last_ai_message_index = next(
i
for i, msg in reversed(list(enumerate(messages)))
if isinstance(msg, AIMessage)
)
messages_to_remove = messages[last_ai_message_index:]
return {"messages": [RemoveMessage(id=m.id) for m in messages_to_remove]}
# Fallback to a better model if a tool call fails
def call_fallback_model(state: MessagesState):
messages = state["messages"]
response = better_model_with_tools.invoke(messages)
return {"messages": [response]}
workflow = StateGraph(MessagesState)
workflow.add_node("agent", call_model)
workflow.add_node("tools", call_tool)
workflow.add_node("remove_failed_tool_call_attempt", remove_failed_tool_call_attempt)
workflow.add_node("fallback_agent", call_fallback_model)
workflow.add_edge(START, "agent")
workflow.add_conditional_edges("agent", should_continue, ["tools", END])
workflow.add_conditional_edges("tools", should_fallback)
workflow.add_edge("remove_failed_tool_call_attempt", "fallback_agent")
workflow.add_edge("fallback_agent", "tools")
app = workflow.compile()
API 参考：AIMessage | ToolMessage | RemoveMessage
如果工具调用失败，tools 节点现在将返回 ToolMessage，并在 additional_kwargs 中包含 error 字段。如果发生这种情况，它将转到另一个节点，该节点删除失败的工具消息，并使用更好的模型重试工具调用生成。
下图以可视化方式显示了这一点
try:
display(Image(app.get_graph().draw_mermaid_png()))
except Exception:
# This requires some extra dependencies and is optional
pass
让我们试一试。为了强调删除步骤，让我们 stream 模型的响应，以便我们可以看到每个执行的节点
stream = app.stream(
{"messages": [("human", "Write me an incredible haiku about water.")]},
{"recursion_limit": 10},
)
for chunk in stream:
print(chunk)
{'agent': {'messages': [AIMessage(content=[{'text': 'Here is a haiku about water:', 'type': 'text'}, {'id': 'toolu_019mY8NX4t7YkJBWeHG6jE4T', 'input': {'topic': ['water']}, 'name': 'master_haiku_generator', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01RmoaLh38DnRX2fv7E8vCFh', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 384, 'output_tokens': 67}}, id='run-a1511215-1a62-49b5-b5b3-b2c8f8c7920e-0', tool_calls=[{'name': 'master_haiku_generator', 'args': {'topic': ['water']}, 'id': 'toolu_019mY8NX4t7YkJBWeHG6jE4T', 'type': 'tool_call'}], usage_metadata={'input_tokens': 384, 'output_tokens': 67, 'total_tokens': 451})]}}
{'tools': {'messages': [ToolMessage(content='', name='master_haiku_generator', id='69f85339-dbc2-4341-8c4d-26300dfe31a5', tool_call_id='toolu_019mY8NX4t7YkJBWeHG6jE4T')]}}
{'remove_failed_tool_call_attempt': {'messages': [RemoveMessage(content='', additional_kwargs={}, response_metadata={}, id='run-a1511215-1a62-49b5-b5b3-b2c8f8c7920e-0'), RemoveMessage(content='', additional_kwargs={}, response_metadata={}, id='69f85339-dbc2-4341-8c4d-26300dfe31a5')]}}
{'fallback_agent': {'messages': [AIMessage(content=[{'text': 'Certainly! I\'d be happy to help you create an incredible haiku about water. To do this, I\'ll use the master_haiku_generator function, which requires three topics. Since you\'ve specified water as the main theme, I\'ll add two related concepts to create a more vivid and interesting haiku. Let\'s use "water," "flow," and "reflection" as our three topics.', 'type': 'text'}, {'id': 'toolu_01FxSxy8LeQ5PjdNYq8vLFTd', 'input': {'request': {'topic': ['water', 'flow', 'reflection']}}, 'name': 'master_haiku_generator', 'type': 'tool_use'}], additional_kwargs={}, response_metadata={'id': 'msg_01U5HV3pt1NVm6syGbxx29no', 'model': 'claude-3-5-sonnet-20240620', 'stop_reason': 'tool_use', 'stop_sequence': None, 'usage': {'input_tokens': 414, 'output_tokens': 158}}, id='run-3eb746c7-b607-4ad3-881a-1c11a7638af7-0', tool_calls=[{'name': 'master_haiku_generator', 'args': {'request': {'topic': ['water', 'flow', 'reflection']}}, 'id': 'toolu_01FxSxy8LeQ5PjdNYq8vLFTd', 'type': 'tool_call'}], usage_metadata={'input_tokens': 414, 'output_tokens': 158, 'total_tokens': 572})]}}
{'tools': {'messages': [ToolMessage(content='"Here is a haiku about water, flow, and reflection:\\n\\nRippling waters flow,\\nMirroring the sky above,\\nTranquil reflection."', name='master_haiku_generator', id='fdfc497d-939a-42c0-8748-31371b98a3a7', tool_call_id='toolu_01FxSxy8LeQ5PjdNYq8vLFTd')]}}
{'agent': {'messages': [AIMessage(content='I hope you enjoy this haiku about the beauty and serenity of water. Please let me know if you would like me to generate another one.', additional_kwargs={}, response_metadata={'id': 'msg_012rXWHapc8tPfBPEonpAT6W', 'model': 'claude-3-haiku-20240307', 'stop_reason': 'end_turn', 'stop_sequence': None, 'usage': {'input_tokens': 587, 'output_tokens': 35}}, id='run-ab6d412d-9374-4a4b-950d-6dcc43d87cf5-0', usage_metadata={'input_tokens': 587, 'output_tokens': 35, 'total_tokens': 622})]}}
您还可以检查此 LangSmith 跟踪，其中显示了对较小模型的初始调用失败。
下一步¶
您现在已经了解了如何实施一些策略来处理工具调用错误。
接下来，查看 此处的其他 LangGraph 操作指南。