[PYTHON/LANGCHAIN] 모델에서 임시 도구 호출 기능 추가하기 4

■ 모델에서 임시 도구 호출 기능을 추가하는 방법을 보여준다.

※ 도구 출력뿐만 아니라 도구 입력도 반환하는 것이 도움이 될 수 있다.
※ RunnablePassthrough 클래스의 assign 메소드를 사용해 도구 출력을 사용하여 LCEL로 쉽게 이를 수행할 수 있다.
※ 이렇게 하면 RunnablePassrthrough 구성 요소에 대한 입력이 무엇이든(사전으로 가정) 키를 추가하고 현재 입력에 있는 모든 것을 계속 전달한다.

▶ main.py


from langchain_core.tools          import tool
from langchain_core.tools          import render_text_description
from langchain_core.prompts        import ChatPromptTemplate
from langchain_community.llms      import Ollama
from langchain_core.output_parsers import JsonOutputParser
from typing                        import TypedDict
from typing                        import Dict
from typing                        import Any
from typing                        import Optional
from langchain_core.runnables      import RunnableConfig
from langchain_core.runnables      import RunnablePassthrough

@tool
def multiply(x : float, y : float) -> float:
    """Multiply two numbers together."""
    return x * y

@tool
def add(x : int, y : int) -> int:
    "Add two numbers."
    return x + y

toolList = [multiply, add]

toolListTextDescription = render_text_description(toolList)

systemPromptTemplateString = f"""\
You are an assistant that has access to the following set of tools.
Here are the names and descriptions for each tool:

{toolListTextDescription}

Given the user input, return the name and input of the tool to use.
Return your response as a JSON blob with 'name' and 'argumentDictionary' keys.

The `argumentDictionary` should be a dictionary, with keys corresponding
to the argument names and the values corresponding to the requested values.
"""

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system", systemPromptTemplateString),
        ("user"  , "{input}")
    ]
)

ollama = Ollama(model = "phi3:latest")

jsonOutputParser = JsonOutputParser()

class ToolCallRequest(TypedDict):
    """A typed dict that shows the inputs into the invoke_tool function."""

    name               : str
    argumentDictionary : Dict[str, Any]

def invokeTool(toolCallRequest : ToolCallRequest, runnableConfig : Optional[RunnableConfig] = None):
    """A function that we can use the perform a tool invocation.

    Args :
        toolCallRequest : a dict that contains the keys name and arguments.
            The name must match the name of a tool that exists.
            The argumentDictionary are the arguments to that tool.
        runnableConfig : This is configuration information that LangChain uses that contains
            things like callbacks, metadata, etc.See LCEL documentation about RunnableConfig.

    Returns :
        output from the requested tool
    """
    toolNameDictionary = {tool.name : tool for tool in toolList}
    name = toolCallRequest["name"]
    tool = toolNameDictionary[name]
    return tool.invoke(toolCallRequest["argumentDictionary"], config = runnableConfig)

runnableSequence = chatPromptTemplate | ollama | jsonOutputParser | RunnablePassthrough.assign(output = invokeTool)

responseDictionary = runnableSequence.invoke({"input" : "what's thirteen times 4.14137281"})

print(responseDictionary)

"""
{'name': 'multiply', 'argumentDictionary': {'x': 13, 'y': 4.14137281}, 'output': 53.83784653}
"""

from langchain_core.tools import tool

from langchain_core.tools import render_text_description

from langchain_core.prompts import ChatPromptTemplate

from langchain_community.llms import Ollama

from langchain_core.output_parsers import JsonOutputParser

from typing import TypedDict

from typing import Dict

from typing import Any

from typing import Optional

from langchain_core.runnables import RunnableConfig

from langchain_core.runnables import RunnablePassthrough

@tool

def multiply(x : float, y : float) -> float:

"""Multiply two numbers together."""

return x * y

@tool

def add(x : int, y : int) -> int:

"Add two numbers."

return x + y

toolList = [multiply, add]

toolListTextDescription = render_text_description(toolList)

systemPromptTemplateString = f"""\

You are an assistant that has access to the following set of tools.

Here are the names and descriptions for each tool:

{toolListTextDescription}

Given the user input, return the name and input of the tool to use.

Return your response as a JSON blob with 'name' and 'argumentDictionary' keys.

The `argumentDictionary` should be a dictionary, with keys corresponding

to the argument names and the values corresponding to the requested values.

"""

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system", systemPromptTemplateString),

("user" , "{input}")

]

)

ollama = Ollama(model = "phi3:latest")

jsonOutputParser = JsonOutputParser()

class ToolCallRequest(TypedDict):

"""A typed dict that shows the inputs into the invoke_tool function."""

name : str

argumentDictionary : Dict[str, Any]

def invokeTool(toolCallRequest : ToolCallRequest, runnableConfig : Optional[RunnableConfig] = None):

"""A function that we can use the perform a tool invocation.

Args :

toolCallRequest : a dict that contains the keys name and arguments.

The name must match the name of a tool that exists.

The argumentDictionary are the arguments to that tool.

runnableConfig : This is configuration information that LangChain uses that contains

things like callbacks, metadata, etc.See LCEL documentation about RunnableConfig.

Returns :

output from the requested tool

"""

toolNameDictionary = {tool.name : tool for tool in toolList}

name = toolCallRequest["name"]

tool = toolNameDictionary[name]

return tool.invoke(toolCallRequest["argumentDictionary"], config = runnableConfig)

runnableSequence = chatPromptTemplate | ollama | jsonOutputParser | RunnablePassthrough.assign(output = invokeTool)

responseDictionary = runnableSequence.invoke({"input" : "what's thirteen times 4.14137281"})

print(responseDictionary)

"""

{'name': 'multiply', 'argumentDictionary': {'x': 13, 'y': 4.14137281}, 'output': 53.83784653}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
dataclasses-json==0.6.7
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.6
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-community==0.3.1
langchain-core==0.3.8
langchain-text-splitters==0.3.0
langsmith==0.1.130
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
typing-inspect==0.9.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.13.1

aiohappyeyeballs==2.4.3

aiohttp==3.10.8

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

dataclasses-json==0.6.7

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.6

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-community==0.3.1

langchain-core==0.3.8

langchain-text-splitters==0.3.0

langsmith==0.1.130

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install langchain langchain-community 명령을 실행했다.