如何使用工具调用的少样本提示
对于更复杂的工具使用，将少量示例添加到提示中非常有用。我们可以通过将带有ToolCall的AIMessage和相应的ToolMessage添加到我们的提示中来实现这一点。
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 Reference: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 Reference:ChatOpenAI
让我们运行我们的模型，我们可以注意到，即使有一些特殊的指令，我们的模型也可能因为操作顺序而出现问题。
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'}]
模型不应该尝试添加任何东西，因为从技术上讲，它还不能知道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。