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如何使用少样本提示进行工具调用

对于更复杂的工具使用,向提示添加少样本示例非常有用。我们可以通过将带有 ToolCall 和相应的 ToolMessageAIMessage 添加到我们的提示中来实现此目的。

首先,让我们定义我们的工具和模型。

from langchain_core.tools import tool


@tool
def add(a: int, b: int) -> int:
"""Adds a and b."""
return a + b


@tool
def multiply(a: int, b: int) -> int:
"""Multiplies a and b."""
return a * b


tools = [add, multiply]
API 参考:tool
import os
from getpass import getpass

from langchain_openai import ChatOpenAI

if "OPENAI_API_KEY" not in os.environ:
os.environ["OPENAI_API_KEY"] = getpass()

llm = ChatOpenAI(model="gpt-4o-mini", temperature=0)
llm_with_tools = llm.bind_tools(tools)
API 参考:ChatOpenAI

让我们运行一下我们的模型,我们可以注意到,即使有一些特殊的指令,我们的模型也可能在运算顺序上出错。

llm_with_tools.invoke(
"Whats 119 times 8 minus 20. Don't do any math yourself, only use tools for math. Respect order of operations"
).tool_calls
[{'name': 'Multiply',
'args': {'a': 119, 'b': 8},
'id': 'call_T88XN6ECucTgbXXkyDeC2CQj'},
{'name': 'Add',
'args': {'a': 952, 'b': -20},
'id': 'call_licdlmGsRqzup8rhqJSb1yZ4'}]

模型现在还不应该尝试进行任何加法运算,因为它在技术上还不知道 119 * 8 的结果。

通过添加一些示例的提示,我们可以纠正这种行为。

from langchain_core.messages import AIMessage, HumanMessage, ToolMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough

examples = [
HumanMessage(
"What's the product of 317253 and 128472 plus four", name="example_user"
),
AIMessage(
"",
name="example_assistant",
tool_calls=[
{"name": "Multiply", "args": {"x": 317253, "y": 128472}, "id": "1"}
],
),
ToolMessage("16505054784", tool_call_id="1"),
AIMessage(
"",
name="example_assistant",
tool_calls=[{"name": "Add", "args": {"x": 16505054784, "y": 4}, "id": "2"}],
),
ToolMessage("16505054788", tool_call_id="2"),
AIMessage(
"The product of 317253 and 128472 plus four is 16505054788",
name="example_assistant",
),
]

system = """You are bad at math but are an expert at using a calculator.

Use past tool usage as an example of how to correctly use the tools."""
few_shot_prompt = ChatPromptTemplate.from_messages(
[
("system", system),
*examples,
("human", "{query}"),
]
)

chain = {"query": RunnablePassthrough()} | few_shot_prompt | llm_with_tools
chain.invoke("Whats 119 times 8 minus 20").tool_calls
[{'name': 'Multiply',
'args': {'a': 119, 'b': 8},
'id': 'call_9MvuwQqg7dlJupJcoTWiEsDo'}]

这次我们得到了正确的输出。

这是 LangSmith 跟踪 的样子。


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