TOOL Archives - icodebroker

[PYTHON/LANGCHAIN] @tool 데코레이터 : args_schema 인자에 BaseModel 파생 객체 설정하기

■ @tool 데코레이터의 args_schema 인자에 BaseModel 파생 객체를 설정하는 방법을 보여준다. ▶ main.py


from pydantic             import BaseModel
from pydantic             import Field
from langchain_core.tools import tool

class CalculatorInput(BaseModel):
    a : int = Field(description = "first number" )
    b : int = Field(description = "second number")

@tool("multiplication-tool", args_schema = CalculatorInput, return_direct = True)
def multiply(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

print(multiply.name)
print(multiply.description)
print(multiply.args_schema.model_json_schema())
print(multiply.return_direct)

"""
multiplication-tool
Multiply two numbers.
{'properties': {'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}, 'required': ['a', 'b'], 'title': 'CalculatorInput', 'type': 'object'}
True
"""

from pydantic import BaseModel

from pydantic import Field

from langchain_core.tools import tool

class CalculatorInput(BaseModel):

a : int = Field(description = "first number" )

b : int = Field(description = "second number")

@tool("multiplication-tool", args_schema = CalculatorInput, return_direct = True)

def multiply(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

print(multiply.name)

print(multiply.description)

print(multiply.args_schema.model_json_schema())

print(multiply.return_direct)

"""

multiplication-tool

Multiply two numbers.

{'properties': {'a': {'description': 'first number', 'title': 'A', 'type': 'integer'}, 'b': {'description': 'second number', 'title': 'B', 'type': 'integer'}}, 'required': ['a', 'b'], 'title': 'CalculatorInput', 'type': 'object'}

True

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.11.7
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.2.post1
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.8
langchain-core==0.3.21
langchain-text-splitters==0.3.2
langsmith==0.1.145
multidict==6.1.0
numpy==1.26.4
orjson==3.10.12
packaging==24.2
propcache==0.2.0
pydantic==2.10.1
pydantic_core==2.27.1
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.36
tenacity==9.0.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.18.0

aiohappyeyeballs==2.4.3

aiohttp==3.11.7

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.2.post1

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.8

langchain-core==0.3.21

langchain-text-splitters==0.3.2

langsmith==0.1.145

multidict==6.1.0

numpy==1.26.4

orjson==3.10.12

packaging==24.2

propcache==0.2.0

pydantic==2.10.1

pydantic_core==2.27.1

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.36

tenacity==9.0.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.18.0

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] @tool 데코레이터 : 대상 함수의 인자에 Annotated 객체를 사용해 인자 구문 분석 지원하기

■ @tool 데코레이터를 사용해 대상 함수의 인자에 Annotated 객체를 사용해 인자 구문 분석을 지원하는 방법을 보여준다. ▶ main.py


from langchain_core.tools import tool
from typing               import Annotated
from typing               import List

@tool
def multiplyByMaximumValue(
    value     : Annotated[str      , "scale factor"],
    valueList : Annotated[List[int], "list of ints over which to take maximum"]
) -> int:
    """Multiply a by the maximum of b."""
    return value * max(valueList)

modelMetaclass = multiplyByMaximumValue.args_schema

modelJSONSchemaDictionary = modelMetaclass.model_json_schema()

print(modelJSONSchemaDictionary)

"""
{
    'description' : 'Multiply a by the maximum of b.',
    'properties'  : {
        'value'     : {
            'description' : 'scale factor',
            'title'       : 'Value',
            'type'        : 'string'
        },
        'valueList' : {
            'description' : 'list of ints over which to take maximum',
            'items'       : {'type' : 'integer'},
            'title'       : 'Valuelist',
            'type'        : 'array'
        }
    },
    'required'    : ['value', 'valueList'],
    'title'       :  'multiplyByMaximumValue',
    'type'        : 'object'
}
"""

from langchain_core.tools import tool

from typing import Annotated

from typing import List

@tool

def multiplyByMaximumValue(

value : Annotated[str , "scale factor"],

valueList : Annotated[List[int], "list of ints over which to take maximum"]

) -> int:

"""Multiply a by the maximum of b."""

return value * max(valueList)

modelMetaclass = multiplyByMaximumValue.args_schema

modelJSONSchemaDictionary = modelMetaclass.model_json_schema()

print(modelJSONSchemaDictionary)

"""

{

'description' : 'Multiply a by the maximum of b.',

'properties' : {

'value' : {

'description' : 'scale factor',

'title' : 'Value',

'type' : 'string'

'valueList' : {

'description' : 'list of ints over which to take maximum',

'items' : {'type' : 'integer'},

'title' : 'Valuelist',

'type' : 'array'

}

'required' : ['value', 'valueList'],

'title' : 'multiplyByMaximumValue',

'type' : 'object'

}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.11.7
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.2.post1
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.8
langchain-core==0.3.21
langchain-text-splitters==0.3.2
langsmith==0.1.145
multidict==6.1.0
numpy==1.26.4
orjson==3.10.12
packaging==24.2
propcache==0.2.0
pydantic==2.10.1
pydantic_core==2.27.1
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.36
tenacity==9.0.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.18.0

aiohappyeyeballs==2.4.3

aiohttp==3.11.7

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.2.post1

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.8

langchain-core==0.3.21

langchain-text-splitters==0.3.2

langsmith==0.1.145

multidict==6.1.0

numpy==1.26.4

orjson==3.10.12

packaging==24.2

propcache==0.2.0

pydantic==2.10.1

pydantic_core==2.27.1

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.36

tenacity==9.0.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.18.0

[PYTHON/LANGCHAIN] @tool 데코레이터 : 비동기 함수를 StructuredTool 객체 만들기

■ @tool 데코레이터를 사용해 비동기 함수를 StructuredTool 객체로 만드는 방법을 보여준다. ▶ main.py


from langchain_core.tools import tool

@tool
async def multiplyAsync(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

print(type(multiplyAsync))
print(multiplyAsync.name)
print(multiplyAsync.description)
print(multiplyAsync.args)

"""
<class 'langchain_core.tools.structured.StructuredTool'>
multiplyAsync
Multiply two numbers.
{'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}
"""

from langchain_core.tools import tool

@tool

async def multiplyAsync(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

print(type(multiplyAsync))

print(multiplyAsync.name)

print(multiplyAsync.description)

print(multiplyAsync.args)

"""

multiplyAsync

Multiply two numbers.

{'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.11.7
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.2.post1
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.8
langchain-core==0.3.21
langchain-text-splitters==0.3.2
langsmith==0.1.145
multidict==6.1.0
numpy==1.26.4
orjson==3.10.12
packaging==24.2
propcache==0.2.0
pydantic==2.10.1
pydantic_core==2.27.1
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.36
tenacity==9.0.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.18.0

aiohappyeyeballs==2.4.3

aiohttp==3.11.7

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.2.post1

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.8

langchain-core==0.3.21

langchain-text-splitters==0.3.2

langsmith==0.1.145

multidict==6.1.0

numpy==1.26.4

orjson==3.10.12

packaging==24.2

propcache==0.2.0

pydantic==2.10.1

pydantic_core==2.27.1

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.36

tenacity==9.0.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.18.0

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] @tool 데코레이터 : 함수를 StructuredTool 객체 만들기

■ @tool 데코레이터를 사용해 함수를 StructuredTool 객체로 만드는 방법을 보여준다. ▶ main.py


from langchain_core.tools import tool

@tool
def multiply(a : int, b : int) -> int:
    """Multiply two numbers."""
    return a * b

print(type(multiply))
print(multiply.name)
print(multiply.description)
print(multiply.args)

"""
<class 'langchain_core.tools.structured.StructuredTool'>
multiply
Multiply two numbers.
{'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}
"""

from langchain_core.tools import tool

@tool

def multiply(a : int, b : int) -> int:

"""Multiply two numbers."""

return a * b

print(type(multiply))

print(multiply.name)

print(multiply.description)

print(multiply.args)

"""

multiply

Multiply two numbers.

{'a': {'title': 'A', 'type': 'integer'}, 'b': {'title': 'B', 'type': 'integer'}}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.11.7
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.2.post1
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.4.0
frozenlist==1.5.0
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.7
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.8
langchain-core==0.3.21
langchain-text-splitters==0.3.2
langsmith==0.1.145
multidict==6.1.0
numpy==1.26.4
orjson==3.10.12
packaging==24.2
propcache==0.2.0
pydantic==2.10.1
pydantic_core==2.27.1
PyYAML==6.0.2
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
SQLAlchemy==2.0.36
tenacity==9.0.0
typing_extensions==4.12.2
urllib3==2.2.3
yarl==1.18.0

aiohappyeyeballs==2.4.3

aiohttp==3.11.7

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.6.2.post1

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.4.0

frozenlist==1.5.0

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.7

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.8

langchain-core==0.3.21

langchain-text-splitters==0.3.2

langsmith==0.1.145

multidict==6.1.0

numpy==1.26.4

orjson==3.10.12

packaging==24.2

propcache==0.2.0

pydantic==2.10.1

pydantic_core==2.27.1

PyYAML==6.0.2

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

SQLAlchemy==2.0.36

tenacity==9.0.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.18.0

※ pip install langchain 명령을 실행했다.

[TOOL/CURL] curl 명령 : OLLAMA 서버 통신하기

■ curl 명령을 사용해 OLLAMA 서버와 통신하는 방법을 보여준다. ▶ 실행 명령


[윈도우즈]

curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d "{\"model\":\"bnksys/eeve-yanolja-v1:latest\",\"prompt\":\"Hello\"}"

[리눅스]

curl http://127.0.0.1:11434/api/generate -d "{'model': 'bnksys/eeve-yanolja-v1:latest', 'prompt': 'Hello'}"

[윈도우즈]

curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d "{\"model\":\"bnksys/eeve-yanolja-v1:latest\",\"prompt\":\"Hello\"}"

[리눅스]

curl http://127.0.0.1:11434/api/generate -d "{'model': 'bnksys/eeve-yanolja-v1:latest', 'prompt': 'Hello'}"

[PYTHON/LANGCHAIN] create_react_agent 함수 : state_modifier 인자를 사용해 장기 실행 에이전트의 중간 단계 제거하기

■ create_react_agent 함수의 state_modifier 인자를 사용해 장기 실행 에이전트의 중간 단계를 제거하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : 생성자에서 trim_intermediate_steps 인자를 사용해 장기 실행 에이전트의 중간 단계 제거하기

■ AgentExecutor 클래스의 생성자에서 trim_intermediate_steps 인자를 사용해 장기 실행 에이전트의 중간 단계를 제거하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : 생성자에서 early_stopping_method/max_iterations 인자를 사용해 반복 제한/시간 제한 중단 문자열 반환하기

■ AgentExecutor 클래스의 생성자에서 early_stopping_method/max_iterations 인자를 사용해 반복 제한/시간 제한 중단 문자열을 반환하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에

[PYTHON/LANGCHAIN] CompiledStateGraph 클래스 : asyncio 모듈의 create_task/wait_for 함수를 사용해 최대 실행 시간 설정하기

■ CompiledStateGraph 클래스에서 asyncio 패키지의 create_task/wait_for 함수를 사용해 최대 실행 시간을 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.

[PYTHON/LANGCHAIN] CompiledStateGraph 클래스 : step_timeout 속성을 사용해 최대 실행 시간 설정하기

■ CompiledStateGraph 클래스의 step_timeout 속성을 사용해 최대 실행 시간을 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : 생성자에서 max_execution_time 인자를 사용해 최대 실행 시간 설정하기

■ AgentExecutor 클래스의 생성자에서 max_execution_time 인자를 사용해 최대 실행 시간을 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶

[PYTHON/LANGCHAIN] CompiledStateGraph 클래스 : stream 메소드에서 재귀 제한 수 설정하기

■ CompiledStateGraph 클래스의 stream 메소드에서 재귀 제한 수를 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv               import load_dotenv
from langchain_openai     import ChatOpenAI
from langchain_core.tools import tool
from langgraph.prebuilt   import create_react_agent
from langgraph.errors     import GraphRecursionError

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

query = "what is the value of magicFunction(3)?"

RECURSION_LIMIT_COUNT = 2 * 3 + 1

compiledStateGraph = create_react_agent(chatOpenAI, tools = toolList)

try:
    for addableValuesDict in compiledStateGraph.stream(
        {"messages" : [("human", query)]},
        {"recursion_limit" : RECURSION_LIMIT_COUNT},
        stream_mode = "values"
    ):
        print(addableValuesDict["messages"][-1])
except GraphRecursionError:
    print({"input" : query, "output" : "Agent stopped due to max iterations."})

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langgraph.prebuilt import create_react_agent

from langgraph.errors import GraphRecursionError

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

query = "what is the value of magicFunction(3)?"

RECURSION_LIMIT_COUNT = 2 * 3 + 1

compiledStateGraph = create_react_agent(chatOpenAI, tools = toolList)

try:

for addableValuesDict in compiledStateGraph.stream(

{"messages" : [("human", query)]},

{"recursion_limit" : RECURSION_LIMIT_COUNT},

stream_mode = "values"

print(addableValuesDict["messages"][-1])

except GraphRecursionError:

print({"input" : query, "output" : "Agent stopped due to max iterations."})

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : 생성자에서 max_iterations 인자를 사용해 사용자 지정 반복 횟수 초과 실행시 중단하기

■ AgentExecutor 클래스의 생성자에서 max_iterations 인자를 사용해 사용자 지정 반복 횟수 초과 실행시 중단하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : 생성자에서 return_intermediate_steps 인자를 사용해 중단 단계 반환하기

■ AgentExecutor 클래스의 생성자에서 return_intermediate_steps 인자를 사용해 중단 단계를 반환하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : stream 메소드를 사용해 질의 응답하기

■ AgentExecutor 클래스의 stream 메소드를 사용해 질의 응답하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                 import load_dotenv
from langchain_openai       import ChatOpenAI
from langchain_core.tools   import tool
from langchain_core.prompts import ChatPromptTemplate
from langchain.agents       import create_tool_calling_agent
from langchain.agents       import AgentExecutor

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system"     , "You are a helpful assistant."),
        ("human"      , "{input}"                     ),
        ("placeholder", "{agent_scratchpad}"          )
    ]
)

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, prompt = chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

query = "what is the value of magic_function(3)?"

for addableDict in agentExecutor.stream({"input" : query}):
    print(addableDict)
    print("-" * 100)

"""
{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log="\nInvoking: `magic_function` with `{'input': 3}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'function': {'arguments': '{"input":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-15ad5b62-46c6-411d-b7f6-fcee0f04d6cd', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{"input":3}', 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_7iH5SstP0NB75GGRzulZZ53J')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'function': {'arguments': '{"input":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-15ad5b62-46c6-411d-b7f6-fcee0f04d6cd', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{"input":3}', 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'index': 0, 'type': 'tool_call_chunk'}])]}
{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log="\nInvoking: `magic_function` with `{'input': 3}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'function': {'arguments': '{"input":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-15ad5b62-46c6-411d-b7f6-fcee0f04d6cd', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{"input":3}', 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_7iH5SstP0NB75GGRzulZZ53J'), observation=5)], 'messages': [FunctionMessage(content='5', additional_kwargs={}, response_metadata={}, name='magic_function')]}
{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', additional_kwargs={}, response_metadata={})]}
(env) king@cosmos:~/testproject$ python main.py
<class 'langchain_core.runnables.utils.AddableDict'>
(env) king@cosmos:~/testproject$ python main.py
{'actions': [ToolAgentAction(tool='magicFunction', tool_input={'input': 3}, log="\nInvoking: `magicFunction` with `{'input': 3}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'function': {'arguments': '{"input":3}', 'name': 'magicFunction'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-e8b2e03f-f3a8-4b68-90ab-5dcfbb35659e', tool_calls=[{'name': 'magicFunction', 'args': {'input': 3}, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magicFunction', 'args': '{"input":3}', 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_Qv8SUJFIEtg02siem3SE7Nxj')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'function': {'arguments': '{"input":3}', 'name': 'magicFunction'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-e8b2e03f-f3a8-4b68-90ab-5dcfbb35659e', tool_calls=[{'name': 'magicFunction', 'args': {'input': 3}, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magicFunction', 'args': '{"input":3}', 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'index': 0, 'type': 'tool_call_chunk'}])]}

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langchain_core.prompts import ChatPromptTemplate

from langchain.agents import create_tool_calling_agent

from langchain.agents import AgentExecutor

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system" , "You are a helpful assistant."),

("human" , "{input}" ),

("placeholder", "{agent_scratchpad}" )

]

)

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, prompt = chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

query = "what is the value of magic_function(3)?"

for addableDict in agentExecutor.stream({"input" : query}):

print(addableDict)

print("-" * 100)

"""

{'actions': [ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log="\nInvoking: `magic_function` with `{'input': 3}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'function': {'arguments': '{"input":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-15ad5b62-46c6-411d-b7f6-fcee0f04d6cd', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{"input":3}', 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_7iH5SstP0NB75GGRzulZZ53J')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'function': {'arguments': '{"input":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-15ad5b62-46c6-411d-b7f6-fcee0f04d6cd', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{"input":3}', 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'index': 0, 'type': 'tool_call_chunk'}])]}

{'steps': [AgentStep(action=ToolAgentAction(tool='magic_function', tool_input={'input': 3}, log="\nInvoking: `magic_function` with `{'input': 3}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'function': {'arguments': '{"input":3}', 'name': 'magic_function'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-15ad5b62-46c6-411d-b7f6-fcee0f04d6cd', tool_calls=[{'name': 'magic_function', 'args': {'input': 3}, 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magic_function', 'args': '{"input":3}', 'id': 'call_7iH5SstP0NB75GGRzulZZ53J', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_7iH5SstP0NB75GGRzulZZ53J'), observation=5)], 'messages': [FunctionMessage(content='5', additional_kwargs={}, response_metadata={}, name='magic_function')]}

{'output': 'The value of `magic_function(3)` is 5.', 'messages': [AIMessage(content='The value of `magic_function(3)` is 5.', additional_kwargs={}, response_metadata={})]}

(env) king@cosmos:~/testproject$ python main.py

(env) king@cosmos:~/testproject$ python main.py

{'actions': [ToolAgentAction(tool='magicFunction', tool_input={'input': 3}, log="\nInvoking: `magicFunction` with `{'input': 3}`\n\n\n", message_log=[AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'function': {'arguments': '{"input":3}', 'name': 'magicFunction'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-e8b2e03f-f3a8-4b68-90ab-5dcfbb35659e', tool_calls=[{'name': 'magicFunction', 'args': {'input': 3}, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magicFunction', 'args': '{"input":3}', 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'index': 0, 'type': 'tool_call_chunk'}])], tool_call_id='call_Qv8SUJFIEtg02siem3SE7Nxj')], 'messages': [AIMessageChunk(content='', additional_kwargs={'tool_calls': [{'index': 0, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'function': {'arguments': '{"input":3}', 'name': 'magicFunction'}, 'type': 'function'}]}, response_metadata={'finish_reason': 'tool_calls', 'model_name': 'gpt-4o-2024-08-06', 'system_fingerprint': 'fp_6b68a8204b'}, id='run-e8b2e03f-f3a8-4b68-90ab-5dcfbb35659e', tool_calls=[{'name': 'magicFunction', 'args': {'input': 3}, 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'type': 'tool_call'}], tool_call_chunks=[{'name': 'magicFunction', 'args': '{"input":3}', 'id': 'call_Qv8SUJFIEtg02siem3SE7Nxj', 'index': 0, 'type': 'tool_call_chunk'}])]}

{'steps':

[PYTHON/LANGCHAIN] create_react_agent 함수 : checkpointer 인자에서 MemorySaver 객체 설정하기

■ create_react_agent 함수의 checkpointer 인자에서 MemorySaver 객체를 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                           import load_dotenv
from langchain_openai                 import ChatOpenAI
from langchain_core.tools             import tool
from langchain_core.messages          import SystemMessage
from langgraph.checkpoint.memory      import MemorySaver # 인 메모리 체크 포인터
from langgraph.prebuilt               import create_react_agent

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

systemMessage = SystemMessage(content = "You are a helpful assistant. Respond only in Korean.")

memorySaver = MemorySaver()

compiledStateGraph = create_react_agent(
    chatOpenAI,
    toolList,
    state_modifier = systemMessage,
    checkpointer   = memorySaver
)

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}}

addableValuesDict1 = compiledStateGraph.invoke(
    {"messages" : [("user", "Hi, I'm polly! What's the output of magic_function of 3?")]},
    configurationDictionary,
)

print(addableValuesDict1["messages"][-1].content)
print("-" * 100)

addableValuesDict2 = compiledStateGraph.invoke(
    {"messages" : [("user", "Remember my name?")]},
    configurationDictionary
)

print(addableValuesDict2["messages"][-1].content)
print("-" * 100)

addableValuesDict3 = compiledStateGraph.invoke(
    {"messages" : [("user", "what was that output again?")]},
    configurationDictionary
)

print(addableValuesDict3["messages"][-1].content)

"""
매직 함수에 입력값 3을 넣으면 출력값은 5입니다.

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langchain_core.messages import SystemMessage

from langgraph.checkpoint.memory import MemorySaver # 인 메모리 체크 포인터

from langgraph.prebuilt import create_react_agent

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

systemMessage = SystemMessage(content = "You are a helpful assistant. Respond only in Korean.")

memorySaver = MemorySaver()

compiledStateGraph = create_react_agent(

chatOpenAI,

toolList,

state_modifier = systemMessage,

checkpointer = memorySaver

)

configurationDictionary = {"configurable" : {"thread_id" : "thread1"}}

addableValuesDict1 = compiledStateGraph.invoke(

{"messages" : [("user", "Hi, I'm polly! What's the output of magic_function of 3?")]},

configurationDictionary,

)

print(addableValuesDict1["messages"][-1].content)

print("-" * 100)

addableValuesDict2 = compiledStateGraph.invoke(

{"messages" : [("user", "Remember my name?")]},

configurationDictionary

)

print(addableValuesDict2["messages"][-1].content)

print("-" * 100)

addableValuesDict3 = compiledStateGraph.invoke(

{"messages" : [("user", "what was that output again?")]},

configurationDictionary

)

print(addableValuesDict3["messages"][-1].content)

"""

매직 함수에 입력값 3을 넣으면 출력값은 5입니다.

당신의

[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : invoke 메소드를 사용해 채팅하기 2

■ RunnableWithMessageHistory 클래스의 invoke 메소드를 사용해 채팅하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                           import load_dotenv
from langchain_openai                 import ChatOpenAI
from langchain_core.tools             import tool
from langchain_core.prompts           import ChatPromptTemplate
from langchain.agents                 import create_tool_calling_agent
from langchain.agents                 import AgentExecutor
from langchain_core.chat_history      import InMemoryChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system"     , "You are a helpful assistant."),
        ("placeholder", "{chat_history}"              ),
        ("human"      , "{input}"                     ),
        ("placeholder", "{agent_scratchpad}"          )
    ]
)

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

inMemoryChatMessageHistory = InMemoryChatMessageHistory(session_id = "test-session")

runnableWithMessageHistory = RunnableWithMessageHistory(
    agentExecutor,
    lambda session_id : inMemoryChatMessageHistory,
    input_messages_key   = "input",
    history_messages_key = "chat_history"
)

configurationDictionary = {"configurable" : {"session_id" : "test-session"}}

responseDictionary1 = runnableWithMessageHistory.invoke({"input" : "Hi, I'm polly! What's the output of magic_function of 3?"}, configurationDictionary)

outputString1 = responseDictionary1["output"]

print(outputString1)
print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke({"input" : "Remember my name?"}, configurationDictionary)

outputString2 = responseDictionary2["output"]

print(outputString2)
print("-" * 100)

responseDictionary3 = runnableWithMessageHistory.invoke({"input": "what was that output again?"}, configurationDictionary)

outputString3 = responseDictionary3["output"]

print(outputString3)

"""
Hello Polly! The output of the magic function for the input 3 is 5.

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langchain_core.prompts import ChatPromptTemplate

from langchain.agents import create_tool_calling_agent

from langchain.agents import AgentExecutor

from langchain_core.chat_history import InMemoryChatMessageHistory

from langchain_core.runnables.history import RunnableWithMessageHistory

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system" , "You are a helpful assistant."),

("placeholder", "{chat_history}" ),

("human" , "{input}" ),

("placeholder", "{agent_scratchpad}" )

]

)

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

inMemoryChatMessageHistory = InMemoryChatMessageHistory(session_id = "test-session")

runnableWithMessageHistory = RunnableWithMessageHistory(

agentExecutor,

lambda session_id : inMemoryChatMessageHistory,

input_messages_key = "input",

history_messages_key = "chat_history"

)

configurationDictionary = {"configurable" : {"session_id" : "test-session"}}

responseDictionary1 = runnableWithMessageHistory.invoke({"input" : "Hi, I'm polly! What's the output of magic_function of 3?"}, configurationDictionary)

outputString1 = responseDictionary1["output"]

print(outputString1)

print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke({"input" : "Remember my name?"}, configurationDictionary)

outputString2 = responseDictionary2["output"]

print(outputString2)

print("-" * 100)

responseDictionary3 = runnableWithMessageHistory.invoke({"input": "what was that output again?"}, configurationDictionary)

outputString3 = responseDictionary3["output"]

print(outputString3)

"""

Hello Polly! The output of the magic function for the input 3 is 5.

Yes, you

[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : 생성자에서 input_messages_key/history_messages_key 인자를 사용해 RunnableWithMessageHistory 객체 만들기 2

■ RunnableWithMessageHistory 클래스의 생성자에서 input_messages_key/history_messages_key 인자를 사용해 RunnableWithMessageHistory 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] create_react_agent 함수 : state_modifier 인자를 사용해 시스템 메시지 설정하기 2

■ create_react_agent 함수의 state_modifier 인자를 사용해 시스템 메시지를 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                                 import load_dotenv
from langchain_openai                       import ChatOpenAI
from langchain_core.tools                   import tool
from langchain_core.prompts                 import ChatPromptTemplate
from langgraph.prebuilt.chat_agent_executor import AgentState
from langgraph.prebuilt                     import create_react_agent

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

chatPromptTemplate = ChatPromptTemplate.from_messages(
    [
        ("system"     , "You are a helpful assistant. Respond only in Spanish."),
        ("placeholder", "{messages}")
    ]
)

def modifyStateMessageList(agentState : AgentState):
    chatPromptValue  = chatPromptTemplate.invoke({"messages" : agentState["messages"]})
    messageList      = chatPromptValue.to_messages()
    finalMessageList = messageList + [("user", "Also say 'Pandamonium!' after the answer.")]
    return finalMessageList

compiledStateGraph = create_react_agent(chatOpenAI, toolList, state_modifier = modifyStateMessageList)

query = "what is the value of magic_function(3)?"

responseAddableValuesDict = compiledStateGraph.invoke({"messages": [("human", query)]})

print({"input" : query, "output" : responseAddableValuesDict["messages"][-1].content})

"""
{'input': 'what is the value of magic_function(3)?', 'output': 'El valor de magic_function(3) es 5. ¡Pandamonium!'}
"""

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langchain_core.prompts import ChatPromptTemplate

from langgraph.prebuilt.chat_agent_executor import AgentState

from langgraph.prebuilt import create_react_agent

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

chatPromptTemplate = ChatPromptTemplate.from_messages(

[

("system" , "You are a helpful assistant. Respond only in Spanish."),

("placeholder", "{messages}")

]

)

def modifyStateMessageList(agentState : AgentState):

chatPromptValue = chatPromptTemplate.invoke({"messages" : agentState["messages"]})

messageList = chatPromptValue.to_messages()

finalMessageList = messageList + [("user", "Also say 'Pandamonium!' after the answer.")]

return finalMessageList

compiledStateGraph = create_react_agent(chatOpenAI, toolList, state_modifier = modifyStateMessageList)

query = "what is the value of magic_function(3)?"

responseAddableValuesDict = compiledStateGraph.invoke({"messages": [("human", query)]})

print({"input" : query, "output" : responseAddableValuesDict["messages"][-1].content})

"""

{'input': 'what is the value of magic_function(3)?', 'output': 'El valor de magic_function(3) es 5. ¡Pandamonium!'}

"""

[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : invoke 메소드를 사용해 채팅하기 1

■ RunnableWithMessageHistory 클래스의 invoke 메소드를 사용해 채팅하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                                     import load_dotenv
from langchain_community.tools.tavily_search    import TavilySearchResults
from langchain_community.document_loaders       import WebBaseLoader
from langchain_text_splitters                   import RecursiveCharacterTextSplitter
from langchain_openai                           import OpenAIEmbeddings
from langchain_community.vectorstores           import FAISS
from langchain.tools.retriever                  import create_retriever_tool
from langchain_openai                           import ChatOpenAI
from langchain                                  import hub
from langchain.agents                           import create_tool_calling_agent
from langchain.agents                           import AgentExecutor
from langchain_core.chat_history                import BaseChatMessageHistory
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.runnables.history           import RunnableWithMessageHistory

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

tavilySearchResults = TavilySearchResults(max_results = 2)

webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview")

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

vectorStoreRetrieverTool = create_retriever_tool(
    vectorStoreRetriever,
    "langsmith_search",
    "Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)

toolList = [tavilySearchResults, vectorStoreRetrieverTool]

chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent")

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

sessionIDDictionary = {}

def getChatMessageHistory(sessionID : str) -> BaseChatMessageHistory:
    if sessionID not in sessionIDDictionary:
        sessionIDDictionary[sessionID] = ChatMessageHistory()
    return sessionIDDictionary[sessionID]

runnableWithMessageHistory = RunnableWithMessageHistory(
    agentExecutor,
    getChatMessageHistory,
    input_messages_key   = "input",
    history_messages_key = "chat_history",
)

responseDictionary1 = runnableWithMessageHistory.invoke(
    {"input" : "hi! I'm bob"},
    config = {"configurable" : {"session_id" : "<foo>"}}
)

print(responseDictionary1)
print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke(
    {"input" : "what's my name?"},
    config = {"configurable" : {"session_id" : "<foo>"}}
)

print(responseDictionary2)
print("-" * 100)

"""
{'input': "hi! I'm bob", 'chat_history': [], 'output': 'Hello Bob! How can I assist you today?'}

from dotenv import load_dotenv

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_community.document_loaders import WebBaseLoader

from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain.tools.retriever import create_retriever_tool

from langchain_openai import ChatOpenAI

from langchain import hub

from langchain.agents import create_tool_calling_agent

from langchain.agents import AgentExecutor

from langchain_core.chat_history import BaseChatMessageHistory

from langchain_community.chat_message_histories import ChatMessageHistory

from langchain_core.runnables.history import RunnableWithMessageHistory

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

tavilySearchResults = TavilySearchResults(max_results = 2)

webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview")

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

vectorStoreRetrieverTool = create_retriever_tool(

vectorStoreRetriever,

"langsmith_search",

"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",

)

toolList = [tavilySearchResults, vectorStoreRetrieverTool]

chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent")

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

sessionIDDictionary = {}

def getChatMessageHistory(sessionID : str) -> BaseChatMessageHistory:

if sessionID not in sessionIDDictionary:

sessionIDDictionary[sessionID] = ChatMessageHistory()

return sessionIDDictionary[sessionID]

runnableWithMessageHistory = RunnableWithMessageHistory(

agentExecutor,

getChatMessageHistory,

input_messages_key = "input",

history_messages_key = "chat_history",

)

responseDictionary1 = runnableWithMessageHistory.invoke(

{"input" : "hi! I'm bob"},

config = {"configurable" : {"session_id" : "<foo>"}}

)

print(responseDictionary1)

print("-" * 100)

responseDictionary2 = runnableWithMessageHistory.invoke(

{"input" : "what's my name?"},

config = {"configurable" : {"session_id" : "<foo>"}}

)

print(responseDictionary2)

print("-" * 100)

"""

{'input': "hi! I'm bob", 'chat_history': [], 'output': 'Hello Bob! How can I assist you today?'}

{'input': "what's

[PYTHON/LANGCHAIN] create_react_agent 함수 : state_modifier 인자를 사용해 시스템 메시지 설정하기 1

■ create_react_agent 함수에서 state_modifier 인자를 사용해 시스템 메시지를 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                  import load_dotenv
from langchain_openai        import ChatOpenAI
from langchain_core.tools    import tool
from langchain_core.messages import SystemMessage
from langgraph.prebuilt      import create_react_agent

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool
def magicFunction(input : int) -> int:
    """Applies a magic function to an input."""
    return input + 2

toolList = [magicFunction]

query = "what is the value of magic_function(3)?"

systemMessage = SystemMessage(content = "You are a helpful assistant. Respond only in Spanish.")

compiledStateGraph = create_react_agent(chatOpenAI, toolList, state_modifier = systemMessage)

addableValuesDict = compiledStateGraph.invoke({"messages" : [("user", query)]})

messageList = addableValuesDict["messages"]

for message in messageList:
    print(type(message))
    print(message.content)
    print("-" * 100)

from dotenv import load_dotenv

from langchain_openai import ChatOpenAI

from langchain_core.tools import tool

from langchain_core.messages import SystemMessage

from langgraph.prebuilt import create_react_agent

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

@tool

def magicFunction(input : int) -> int:

"""Applies a magic function to an input."""

return input + 2

toolList = [magicFunction]

query = "what is the value of magic_function(3)?"

systemMessage = SystemMessage(content = "You are a helpful assistant. Respond only in Spanish.")

compiledStateGraph = create_react_agent(chatOpenAI, toolList, state_modifier = systemMessage)

addableValuesDict = compiledStateGraph.invoke({"messages" : [("user", query)]})

messageList = addableValuesDict["messages"]

for message in messageList:

print(type(message))

print(message.content)

print("-" * 100)

[PYTHON/LANGCHAIN] create_react_agent 함수 : 도구 호출 ReAct 스타일 에이전트 만들기

■ create_react_agent 함수를 사용해 도구 호출 ReAct 스타일 에이전트를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] RunnableWithMessageHistory 클래스 : 생성자에서 input_messages_key/history_messages_key 인자를 사용해 RunnableWithMessageHistory 객체 만들기 1

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : invoke 메소드 사용시 채팅 히스토리 설정하기

■ AgentExecutor 클래스의 invoke 메소드 사용시 채팅 히스토리를 설정하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                                  import load_dotenv
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders    import WebBaseLoader
from langchain_text_splitters                import RecursiveCharacterTextSplitter
from langchain_openai                        import OpenAIEmbeddings
from langchain_community.vectorstores        import FAISS
from langchain.tools.retriever               import create_retriever_tool
from langchain_openai                        import ChatOpenAI
from langchain                               import hub
from langchain.agents                        import create_tool_calling_agent
from langchain.agents                        import AgentExecutor
from langchain_core.messages                 import HumanMessage
from langchain_core.messages                 import AIMessage

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

tavilySearchResults = TavilySearchResults(max_results = 2)

webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview")

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

vectorStoreRetrieverTool = create_retriever_tool(
    vectorStoreRetriever,
    "langsmith_search",
    "Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",
)

toolList = [tavilySearchResults, vectorStoreRetrieverTool]

chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent")

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

responseDictionary = agentExecutor.invoke(
    {
        "chat_history" : [
            HumanMessage(content = "hi! my name is bob"),
            AIMessage(content = "Hello Bob! How can I assist you today?")
        ],
        "input" : "what's my name?"
    }
)

print(responseDictionary)

"""
{
    'chat_history' : [
        HumanMessage(content = 'hi! my name is bob', additional_kwargs = {}, response_metadata = {}),
        AIMessage(content = 'Hello Bob! How can I assist you today?', additional_kwargs = {}, response_metadata = {})
    ],
    'input'  : "what's my name?",
    'output' : 'Your name is Bob.'
}
"""

from dotenv import load_dotenv

from langchain_community.tools.tavily_search import TavilySearchResults

from langchain_community.document_loaders import WebBaseLoader

from langchain_text_splitters import RecursiveCharacterTextSplitter

from langchain_openai import OpenAIEmbeddings

from langchain_community.vectorstores import FAISS

from langchain.tools.retriever import create_retriever_tool

from langchain_openai import ChatOpenAI

from langchain import hub

from langchain.agents import create_tool_calling_agent

from langchain.agents import AgentExecutor

from langchain_core.messages import HumanMessage

from langchain_core.messages import AIMessage

load_dotenv()

chatOpenAI = ChatOpenAI(model = "gpt-4o")

tavilySearchResults = TavilySearchResults(max_results = 2)

webBaseLoader = WebBaseLoader("https://docs.smith.langchain.com/overview")

documentList = webBaseLoader.load()

recursiveCharacterTextSplitter = RecursiveCharacterTextSplitter(chunk_size = 1000, chunk_overlap = 200)

splitDocumentList = recursiveCharacterTextSplitter.split_documents(documentList)

openAIEmbeddings = OpenAIEmbeddings()

faiss = FAISS.from_documents(splitDocumentList, openAIEmbeddings)

vectorStoreRetriever = faiss.as_retriever()

vectorStoreRetrieverTool = create_retriever_tool(

vectorStoreRetriever,

"langsmith_search",

"Search for information about LangSmith. For any questions about LangSmith, you must use this tool!",

)

toolList = [tavilySearchResults, vectorStoreRetrieverTool]

chatPromptTemplate = hub.pull("hwchase17/openai-functions-agent")

runnableSequence = create_tool_calling_agent(chatOpenAI, toolList, chatPromptTemplate)

agentExecutor = AgentExecutor(agent = runnableSequence, tools = toolList)

responseDictionary = agentExecutor.invoke(

{

"chat_history" : [

HumanMessage(content = "hi! my name is bob"),

AIMessage(content = "Hello Bob! How can I assist you today?")

"input" : "what's my name?"

}

)

print(responseDictionary)

"""

{

'chat_history' : [

HumanMessage(content = 'hi! my name is bob', additional_kwargs = {}, response_metadata = {}),

AIMessage(content = 'Hello Bob! How can I assist you today?', additional_kwargs = {}, response_metadata = {})

'input' : "what's my name?",

'output' : 'Your name is Bob.'

}

"""

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : OPENAI 모델과 TAVILY 도구를 사용해 질의 응답하기 2

■ AgentExecutor 클래스에서 OPENAI 모델과 TAVILY 도구를 사용해 질의 응답하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py