Choosing between multiple tools | Документация для разработчиков

Choosing between multiple tools

Обновлено 6 марта 2024

In our Quickstart we went over how to build a Chain that calls a single multiply tool. Now let's take a look at how we might augment this chain so that it can pick from a number of tools to call. We'll focus on Chains since Agents can route between multiple tools by default.


We'll need to install the following packages for this guide:

%pip install --upgrade --quiet langchain langchain-openai

And set these environment variables:

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()


Recall we already had a multiply tool:

from langchain_core.tools import tool

def multiply(first_int: int, second_int: int) -> int:
"""Multiply two integers together."""
return first_int * second_int

And now we can add to it a exponentiate and add tool:

def add(first_int: int, second_int: int) -> int:
"Add two integers."
return first_int + second_int

def exponentiate(base: int, exponent: int) -> int:
"Exponentiate the base to the exponent power."
return base**exponent

The main difference between using one Tool and many, is that in the case of many we can't be sure which Tool the model will invoke. So we cannot hardcode, like we did in the Quickstart, a specific tool into our chain. Instead we'll add call_tool_list, a RunnableLambda that takes the JsonOutputToolsParser output and actually builds the end of the chain based on it, meaning it appends the Tools that were envoked to the end of the chain at runtime. We can do this because LCEL has the cool property that in any Runnable (the core building block of LCEL) sequence, if one component returns more Runnables, those are run as part of the chain.

from operator import itemgetter
from typing import Union

from langchain.output_parsers import JsonOutputToolsParser
from langchain_core.runnables import (
from langchain_openai import ChatOpenAI

model = ChatOpenAI(model="gpt-3.5-turbo")
tools = [multiply, exponentiate, add]
model_with_tools = model.bind_tools(tools)
tool_map = {tool.name: tool for tool in tools}

def call_tool(tool_invocation: dict) -> Union[str, Runnable]:
"""Function for dynamically constructing the end of the chain based on the model-selected tool."""
tool = tool_map[tool_invocation["type"]]
return RunnablePassthrough.assign(output=itemgetter("args") | tool)

# .map() allows us to apply a function to a list of inputs.
call_tool_list = RunnableLambda(call_tool).map()
chain = model_with_tools | JsonOutputToolsParser() | call_tool_list
chain.invoke("What's 23 times 7")
    [{'type': 'multiply',
'args': {'first_int': 23, 'second_int': 7},
'output': 161}]
chain.invoke("add a million plus a billion")
    [{'type': 'add',
'args': {'first_int': 1000000, 'second_int': 1000000000},
'output': 1001000000}]
chain.invoke("cube thirty-seven")
    [{'type': 'exponentiate',
'args': {'base': 37, 'exponent': 3},
'output': 50653}]
ПАО Сбербанк использует cookie для персонализации сервисов и удобства пользователей.
Вы можете запретить сохранение cookie в настройках своего браузера.