Agents
链条在我们知道任何用户输入所需工具使用的具体顺序时非常好。但对于某些用例，我们使用工具的次数取决于输入。在这些情况下，我们希望让模型自己决定使用工具的次数和顺序。 代理让我们做到这一点。
LangChain内置了多种针对不同用例进行优化的内置代理。在这里阅读所有代理类型。
作为一个例子，让我们尝试使用 OpenAI 工具代理，该代理利用了新的 OpenAI 工具调用 API（这仅在最新的 OpenAI 模型中可用，并不同于函数调用，因为模型可以一次返回多个函数调用）。
Setup
我们需要安装以下软件包：
%pip install --upgrade --quiet langchain langchain-openai
并设置这些环境变量：
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
# If you'd like to use LangSmith, uncomment the below
# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()
Create tools
首先，我们需要创建一些要调用的工具。在本示例中，我们将从函数创建自定义工具。有关创建自定义工具的更多信息，请参阅此指南。
from langchain_core.tools import tool
@tool
def multiply(first_int: int, second_int: int) -> int:
"""Multiply two integers together."""
return first_int * second_int
@tool
def add(first_int: int, second_int: int) -> int:
"Add two integers."
return first_int + second_int
@tool
def exponentiate(base: int, exponent: int) -> int:
"Exponentiate the base to the exponent power."
return base**exponent
tools = [multiply, add, exponentiate]
Create prompt
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_tools_agent
from langchain_openai import ChatOpenAI
# Get the prompt to use - you can modify this!
prompt = hub.pull("hwchase17/openai-tools-agent")
prompt.pretty_print()
================================ System Message ================================
You are a helpful assistant
============================= Messages Placeholder =============================
{chat_history}
================================ Human Message =================================
{input}
============================= Messages Placeholder =============================
{agent_scratchpad}
Create agent
# Choose the LLM that will drive the agent
# Only certain models support this
model = ChatOpenAI(model="gpt-3.5-turbo-1106", temperature=0)
# Construct the OpenAI Tools agent
agent = create_openai_tools_agent(model, tools, prompt)
# Create an agent executor by passing in the agent and tools
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
Invoke agent
agent_executor.invoke(
{
"input": "Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result"
}
)
> Entering new AgentExecutor chain...
Invoking: `exponentiate` with `{'base': 3, 'exponent': 5}`
243
Invoking: `add` with `{'first_int': 12, 'second_int': 3}`
15
Invoking: `multiply` with `{'first_int': 243, 'second_int': 15}`
3645
Invoking: `exponentiate` with `{'base': 3645, 'exponent': 2}`
13286025The result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.
> Finished chain.
{'input': 'Take 3 to the fifth power and multiply that by the sum of twelve and three, then square the whole result',
'output': 'The result of raising 3 to the fifth power and multiplying that by the sum of twelve and three, then squaring the whole result is 13,286,025.'}