Choosing between multiple tools

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.

Setup

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

%pip install --upgrade --quiet langchain-core

If you'd like to trace your runs in LangSmith uncomment and set the following environment variables:

import getpass
import os

# os.environ["LANGCHAIN_TRACING_V2"] = "true"
# os.environ["LANGCHAIN_API_KEY"] = getpass.getpass()

Tools

Recall we already had a multiply tool:

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

API Reference:

• tool

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

@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

The main difference between using one Tool and many is that we can't be sure which Tool the model will invoke upfront, so we cannot hardcode, like we did in the Quickstart, a specific tool into our chain. Instead we'll add call_tools, a RunnableLambda that takes the output AI message with tools calls and routes to the correct tools.

OpenAI

Install dependencies

pip install -qU langchain-openai

Set environment variables

import getpass
import os

os.environ["OPENAI_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-3.5-turbo-0125")

Anthropic

Install dependencies

pip install -qU langchain-anthropic

Set environment variables

import getpass
import os

os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()

from langchain_anthropic import ChatAnthropic

llm = ChatAnthropic(model="claude-3-sonnet-20240229")

Google

Install dependencies

pip install -qU langchain-google-vertexai

Set environment variables

import getpass
import os

os.environ["GOOGLE_API_KEY"] = getpass.getpass()

from langchain_google_vertexai import ChatVertexAI

llm = ChatVertexAI(model="gemini-pro")

Cohere

Install dependencies

pip install -qU langchain-cohere

Set environment variables

import getpass
import os

os.environ["COHERE_API_KEY"] = getpass.getpass()

from langchain_cohere import ChatCohere

llm = ChatCohere(model="command-r")

FireworksAI

Install dependencies

pip install -qU langchain-fireworks

Set environment variables

import getpass
import os

os.environ["FIREWORKS_API_KEY"] = getpass.getpass()

from langchain_fireworks import ChatFireworks

llm = ChatFireworks(model="accounts/fireworks/models/mixtral-8x7b-instruct")

MistralAI

Install dependencies

pip install -qU langchain-mistralai

Set environment variables

import getpass
import os

os.environ["MISTRAL_API_KEY"] = getpass.getpass()

from langchain_mistralai import ChatMistralAI

llm = ChatMistralAI(model="mistral-large-latest")

TogetherAI

Install dependencies

pip install -qU langchain-openai

Set environment variables

import getpass
import os

os.environ["TOGETHER_API_KEY"] = getpass.getpass()

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    base_url="https://api.together.xyz/v1",
    api_key=os.environ["TOGETHER_API_KEY"],
    model="mistralai/Mixtral-8x7B-Instruct-v0.1",)

from operator import itemgetter
from typing import Dict, List, Union

from langchain_core.messages import AIMessage
from langchain_core.runnables import (
    Runnable,
    RunnableLambda,
    RunnableMap,
    RunnablePassthrough,
)

tools = [multiply, exponentiate, add]
llm_with_tools = llm.bind_tools(tools)
tool_map = {tool.name: tool for tool in tools}


def call_tools(msg: AIMessage) -> Runnable:
    """Simple sequential tool calling helper."""
    tool_map = {tool.name: tool for tool in tools}
    tool_calls = msg.tool_calls.copy()
    for tool_call in tool_calls:
        tool_call["output"] = tool_map[tool_call["name"]].invoke(tool_call["args"])
    return tool_calls


chain = llm_with_tools | call_tools

API Reference:

• AIMessage
• Runnable
• RunnableLambda
• RunnableMap
• RunnablePassthrough

chain.invoke("What's 23 times 7")

[{'name': 'multiply',
  'args': {'first_int': 23, 'second_int': 7},
  'id': 'toolu_01Wf8kUs36kxRKLDL8vs7G8q',
  'output': 161}]

chain.invoke("add a million plus a billion")

[{'name': 'add',
  'args': {'first_int': 1000000, 'second_int': 1000000000},
  'id': 'toolu_012aK4xZBQg2sXARsFZnqxHh',
  'output': 1001000000}]

chain.invoke("cube thirty-seven")

[{'name': 'exponentiate',
  'args': {'base': 37, 'exponent': 3},
  'id': 'toolu_01VDU6X3ugDb9cpnnmCZFPbC',
  'output': 50653}]