10월 2024 - 8 중 8 번째 페이지

[PYTHON/LANGCHAIN] create_react_agent 함수 : compiledStateGraph 객체 만들기

■ create_react_agent 함수를 사용해 compiledStateGraph 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                      import load_dotenv
from langchain_core.documents    import Document
from langchain_openai            import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai            import ChatOpenAI
from langgraph.prebuilt          import create_react_agent

load_dotenv()

documentList = [
    Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),
    Document(page_content = "Cats are independent pets that often enjoy their own space."         )
]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(
    documentList,
    embedding = openAIEmbeddings
)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(
    search_type   = "similarity",
    search_kwargs = {"k" : 1}
)

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

tool = vectorStoreRetriever.as_tool(
    name        = "pet_info_retriever",
    description = "Get information about pets.",
)

toolList = [tool]

compiledStateGraph = create_react_agent(chatOpenAI, toolList)

for addableUpdatesDict in compiledStateGraph.stream({"messages" : [("human", "What are dogs known for?")]}):
    print(addableUpdatesDict)
    print("-" * 100)

"""
{
    'agent' : {
        'messages' : [
            AIMessage(
                content           = 'Dogs are known for several characteristics and traits, including:\n\n1. **Companionship**: Dogs are often referred to as "man\'s best friend" due to their loyalty and companionship.\n\n2. **Intelligence**: Many dog breeds are highly intelligent and capable of learning a variety of commands and tricks.\n\n3. **Variety of Breeds**: There are hundreds of dog breeds, each with its own unique traits, sizes, and temperaments.\n\n4. **Working Abilities**: Dogs are used in various roles, such as service animals, search and rescue, therapy dogs, and police or military dogs.\n\n5. **Strong Sense of Smell**: Dogs have an exceptional sense of smell, which makes them excellent for tracking and detection purposes.\n\n6. **Social Behavior**: Dogs are social animals and often thrive in the company of humans and other pets.\n\n7. **Playfulness**: Many dogs enjoy playing and being active, which makes them great companions for outdoor activities.\n\n8. **Emotional Support**: Dogs are known to provide emotional support and comfort to their owners, often sensing when someone is feeling down.\n\n9. **Protectiveness**: Many dogs have a natural instinct to protect their home and family, making them good guard animals.\n\n10. **Communication**: Dogs communicate through a combination of vocalizations, body language, and facial expressions. \n\nOverall, dogs are appreciated for their loyalty, intelligence, and the deep bond they can form with humans.',
                additional_kwargs = {'refusal' : None},
                response_metadata = {
                    'token_usage' : {
                        'completion_tokens'         : 299,
                        'prompt_tokens'             : 58,
                        'total_tokens'              : 357,
                        'completion_tokens_details' : {'reasoning_tokens' : 0}
                    },
                    'model_name'         : 'gpt-4o-mini-2024-07-18',
                    'system_fingerprint' : 'fp_f85bea6784',
                    'finish_reason'      : 'stop',
                    'logprobs'           : None
                },
                id                = 'run-b2d78792-6c54-422e-8739-07662d2eb56b-0',
                usage_metadata    = {'input_tokens' : 58, 'output_tokens' : 299, 'total_tokens' : 357}
            )
        ]
    }
}

from dotenv import load_dotenv

from langchain_core.documents import Document

from langchain_openai import OpenAIEmbeddings

from langchain_core.vectorstores import InMemoryVectorStore

from langchain_openai import ChatOpenAI

from langgraph.prebuilt import create_react_agent

load_dotenv()

documentList = [

Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),

Document(page_content = "Cats are independent pets that often enjoy their own space." )

]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(

documentList,

embedding = openAIEmbeddings

)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(

search_type = "similarity",

search_kwargs = {"k" : 1}

)

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

tool = vectorStoreRetriever.as_tool(

name = "pet_info_retriever",

description = "Get information about pets.",

)

toolList = [tool]

compiledStateGraph = create_react_agent(chatOpenAI, toolList)

for addableUpdatesDict in compiledStateGraph.stream({"messages" : [("human", "What are dogs known for?")]}):

print(addableUpdatesDict)

print("-" * 100)

"""

{

'agent' : {

'messages' : [

AIMessage(

content = 'Dogs are known for several characteristics and traits, including:\n\n1. **Companionship**: Dogs are often referred to as "man\'s best friend" due to their loyalty and companionship.\n\n2. **Intelligence**: Many dog breeds are highly intelligent and capable of learning a variety of commands and tricks.\n\n3. **Variety of Breeds**: There are hundreds of dog breeds, each with its own unique traits, sizes, and temperaments.\n\n4. **Working Abilities**: Dogs are used in various roles, such as service animals, search and rescue, therapy dogs, and police or military dogs.\n\n5. **Strong Sense of Smell**: Dogs have an exceptional sense of smell, which makes them excellent for tracking and detection purposes.\n\n6. **Social Behavior**: Dogs are social animals and often thrive in the company of humans and other pets.\n\n7. **Playfulness**: Many dogs enjoy playing and being active, which makes them great companions for outdoor activities.\n\n8. **Emotional Support**: Dogs are known to provide emotional support and comfort to their owners, often sensing when someone is feeling down.\n\n9. **Protectiveness**: Many dogs have a natural instinct to protect their home and family, making them good guard animals.\n\n10. **Communication**: Dogs communicate through a combination of vocalizations, body language, and facial expressions. \n\nOverall, dogs are appreciated for their loyalty, intelligence, and the deep bond they can form with humans.',

additional_kwargs = {'refusal' : None},

response_metadata = {

'token_usage' : {

'completion_tokens' : 299,

'prompt_tokens' : 58,

'total_tokens' : 357,

'completion_tokens_details' : {'reasoning_tokens' : 0}

'model_name' : 'gpt-4o-mini-2024-07-18',

'system_fingerprint' : 'fp_f85bea6784',

'finish_reason' : 'stop',

'logprobs' : None

id = 'run-b2d78792-6c54-422e-8739-07662d2eb56b-0',

usage_metadata = {'input_tokens' : 58, 'output_tokens' : 299, 'total_tokens' : 357}

)

]

}

""" —————————————————————————————————-

[PYTHON/LANGCHAIN] VectorStoreRetriever 클래스 : as_tool 메소드를 사용해 Tool 객체 만들기

■ VectorStoreRetriever 클래스의 as_tool 메소드를 사용해 Tool 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.documents    import Document
from langchain_openai            import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore

documentList = [
    Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),
    Document(page_content = "Cats are independent pets that often enjoy their own space."         )
]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(
    documentList,
    embedding = openAIEmbeddings
)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(
    search_type   = "similarity",
    search_kwargs = {"k" : 1}
)

tool = vectorStoreRetriever.as_tool(
    name        = "pet_info_retriever",
    description = "Get information about pets.",
)

from langchain_core.documents import Document

from langchain_openai import OpenAIEmbeddings

from langchain_core.vectorstores import InMemoryVectorStore

documentList = [

Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),

Document(page_content = "Cats are independent pets that often enjoy their own space." )

]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(

documentList,

embedding = openAIEmbeddings

)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(

search_type = "similarity",

search_kwargs = {"k" : 1}

)

tool = vectorStoreRetriever.as_tool(

name = "pet_info_retriever",

description = "Get information about pets.",

)

※ pip install langchain-openai 명령을 실행했다.

[PYTHON/LANGCHAIN] InMemoryVectorStore 클래스 : as_retriever 메소드를 사용해 VectorStoreRetriever 객체 만들기

■ InMemoryVectorStore 클래스의 as_retriever 메소드를 사용해 VectorStoreRetriever 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.documents    import Document
from langchain_openai            import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore

documentList = [
    Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),
    Document(page_content = "Cats are independent pets that often enjoy their own space."         )
]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(
    documentList,
    embedding = openAIEmbeddings
)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(
    search_type   = "similarity",
    search_kwargs = {"k" : 1}
)

from langchain_core.documents import Document

from langchain_openai import OpenAIEmbeddings

from langchain_core.vectorstores import InMemoryVectorStore

documentList = [

Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),

Document(page_content = "Cats are independent pets that often enjoy their own space." )

]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(

documentList,

embedding = openAIEmbeddings

)

vectorStoreRetriever = inMemoryVectorStore.as_retriever(

search_type = "similarity",

search_kwargs = {"k" : 1}

)

※ pip install langchain-openai 명령을 실행했다.

[PYTHON/LANGCHAIN] InMemoryVectorStore 클래스 : from_documents 정적 메소드를 사용해 InMemoryVectorStore 객체 만들기

■ InMemoryVectorStore 클래스의 from_documents 정적 메소드를 사용해 InMemoryVectorStore 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.documents    import Document
from langchain_openai            import OpenAIEmbeddings
from langchain_core.vectorstores import InMemoryVectorStore

documentList = [
    Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),
    Document(page_content = "Cats are independent pets that often enjoy their own space."         )
]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(
    documentList,
    embedding = openAIEmbeddings
)

from langchain_core.documents import Document

from langchain_openai import OpenAIEmbeddings

from langchain_core.vectorstores import InMemoryVectorStore

documentList = [

Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),

Document(page_content = "Cats are independent pets that often enjoy their own space." )

]

openAIEmbeddings = OpenAIEmbeddings()

inMemoryVectorStore = InMemoryVectorStore.from_documents(

documentList,

embedding = openAIEmbeddings

)

※ pip install langchain-openai 명령을 실행했다.

[PYTHON/LANGCHAIN] Document 클래스 : page_content 속성을 사용해 Document 객체 만들기

■ Document 클래스의 page_content 속성을 사용해 Document 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.documents import Document

documentList = [
    Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),
    Document(page_content = "Cats are independent pets that often enjoy their own space."         )
]

from langchain_core.documents import Document

documentList = [

Document(page_content = "Dogs are great companions, known for their loyalty and friendliness."),

Document(page_content = "Cats are independent pets that often enjoy their own space." )

]

[PYTHON/LANGCHAIN] RunnableSequence 클래스 : as_tool 메소드를 사용해 StructuredTool 객체 만들기

■ RunnableSequence 클래스의 as_tool 메소드를 사용해 StructuredTool 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.runnables import RunnableLambda

def test1(x : str) -> str:
    return x + "a"

def test2(x : str) -> str:
    return x + "z"

runnableSequence = RunnableLambda(test1) | test2

structuredTool = runnableSequence.as_tool()

responseString = structuredTool.invoke("b")

print(responseString)

"""
baz
"""

from langchain_core.runnables import RunnableLambda

def test1(x : str) -> str:

return x + "a"

def test2(x : str) -> str:

return x + "z"

runnableSequence = RunnableLambda(test1) | test2

structuredTool = runnableSequence.as_tool()

responseString = structuredTool.invoke("b")

print(responseString)

"""

baz

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install langcahin 명령을

[PYTHON/LANGCHAIN] RunnableLambda 클래스 : as_tool 메소드에서 BaseModel 객체를 설정해 StructuredTool 객체 만들기

■ RunnableLambda 클래스의 as_tool 메소드에서 BaseModel 객체를 설정해 StructuredTool 객체를 만드는 방법을 보여준다. ▶ main.py


from typing                   import Dict
from typing                   import Any
from pydantic                 import BaseModel
from pydantic                 import Field
from typing                   import List
from langchain_core.runnables import RunnableLambda

def test(x : Dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

class TestArgument(BaseModel):
    """Apply a function to an integer and list of integers."""
    a : int       = Field(..., description = "Integer"     )
    b : List[int] = Field(..., description = "List of ints")

structuredTool = runnableLambda.as_tool(TestArgument)

responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]})

print(responseString)

"""
6
"""

from typing import Dict

from typing import Any

from pydantic import BaseModel

from pydantic import Field

from typing import List

from langchain_core.runnables import RunnableLambda

def test(x : Dict[str, Any]) -> str:

return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

class TestArgument(BaseModel):

"""Apply a function to an integer and list of integers."""

a : int = Field(..., description = "Integer" )

b : List[int] = Field(..., description = "List of ints")

structuredTool = runnableLambda.as_tool(TestArgument)

responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]})

print(responseString)

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install

[PYTHON/LANGCHAIN] RunnableLambda 클래스 : as_tool 메소드에서 arg_types 인자를 사용해 도구 인자 타입 설정하기

■ RunnableLambda 클래스의 as_tool 메소드에서 arg_types 인자를 사용해 도구 인자 타입을 설정하는 방법을 보여준다. ▶ main.py


from typing                   import Dict
from typing                   import Any
from typing                   import List
from langchain_core.runnables import RunnableLambda

def test(x : Dict[str, Any]) -> str:
    return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(
    name        = "My tool",
    description = "Explanation of when to use tool.",
    arg_types   = {"a" : int, "b" : List[int]}
)

responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]})

print(responseString)

"""
6
"""

from typing import Dict

from typing import Any

from typing import List

from langchain_core.runnables import RunnableLambda

def test(x : Dict[str, Any]) -> str:

return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(

name = "My tool",

description = "Explanation of when to use tool.",

arg_types = {"a" : int, "b" : List[int]}

)

responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]})

print(responseString)

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip

[PYTHON/LANGCHAIN] StructuredTool 클래스 : invoke 메소드를 사용해 도구 함수 호출하기

■ StructuredTool 클래스의 invoke 메소드를 사용해 도구 함수를 호출하는 방법을 보여준다. ▶ main.py


from typing_extensions        import TypedDict
from typing                   import List
from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):
    a : int
    b : List[int]

def test(x : Argument) -> str:
    return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(
    name        = "My tool",
    description = "Explanation of when to use tool.",
)

responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]})

print(responseString)

"""
6
"""

from typing_extensions import TypedDict

from typing import List

from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):

a : int

b : List[int]

def test(x : Argument) -> str:

return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(

name = "My tool",

description = "Explanation of when to use tool.",

)

responseString = structuredTool.invoke({"a" : 3, "b" : [1, 2]})

print(responseString)

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] StructuredTool 클래스 : description/args_schema 속성 사용하기

■ StructuredTool 클래스의 description/args_schema 속성을 사용하는 방법을 보여준다. ▶ main.py


from typing_extensions        import TypedDict
from typing                   import List
from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):
    a : int
    b : List[int]

def test(x : Argument) -> str:
    return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(
    name        = "My tool",
    description = "Explanation of when to use tool.",
)

print(structuredTool.description)
print(structuredTool.args_schema.schema())

"""
Explanation of when to use tool.

{
    'properties' : {
        'a' : {
            'title' : 'A',
            'type'  : 'integer'
        },
        'b' : {
            'items' : {'type' : 'integer'},
            'title' : 'B',
            'type'  : 'array'
        }
    },
    'required' : ['a', 'b'],
    'title'    : 'My tool',
    'type'     : 'object'
}
"""

from typing_extensions import TypedDict

from typing import List

from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):

a : int

b : List[int]

def test(x : Argument) -> str:

return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(

name = "My tool",

description = "Explanation of when to use tool.",

)

print(structuredTool.description)

print(structuredTool.args_schema.schema())

"""

Explanation of when to use tool.

{

'properties' : {

'a' : {

'title' : 'A',

'type' : 'integer'

'b' : {

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

'title' : 'B',

'type' : 'array'

}

'required' : ['a', 'b'],

'title' : 'My tool',

'type' : 'object'

}

"""

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install langchain 명령을 실행했다.

[PYTHON/LANGCHAIN] RunnableLambda 클래스 : as_tool 메소드를 사용해 StructuredTool 객체 만들기

■ RunnableLambda 클래스의 as_tool 메소드를 사용해 StructuredTool 객체를 만드는 방법을 보여준다. ▶ main.py


from typing_extensions        import TypedDict
from typing                   import List
from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):
    a : int
    b : List[int]

def test(x : Argument) -> str:
    return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(
    name        = "My tool",
    description = "Explanation of when to use tool.",
)

from typing_extensions import TypedDict

from typing import List

from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):

a : int

b : List[int]

def test(x : Argument) -> str:

return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

structuredTool = runnableLambda.as_tool(

name = "My tool",

description = "Explanation of when to use tool.",

)

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] RunnableLambda 클래스 : 생성자를 사용해 RunnableLambda 객체 만들기

■ RunnableLambda 클래스의 생성자를 사용해 RunnableLambda 객체를 만드는 방법을 보여준다. ▶ main.py


from typing_extensions        import TypedDict
from typing                   import List
from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):
    a : int
    b : List[int]

def test(x : Argument) -> str:
    return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

from typing_extensions import TypedDict

from typing import List

from langchain_core.runnables import RunnableLambda

class Argument(TypedDict):

a : int

b : List[int]

def test(x : Argument) -> str:

return str(x["a"] * max(x["b"]))

runnableLambda = RunnableLambda(test)

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.8
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.6.0
async-timeout==4.0.3
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
exceptiongroup==1.2.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.10
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.1
langchain-core==0.3.7
langchain-text-splitters==0.3.0
langsmith==0.1.129
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.2
pydantic_core==2.23.4
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.35
tenacity==8.5.0
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

async-timeout==4.0.3

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

exceptiongroup==1.2.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.10

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.1

langchain-core==0.3.7

langchain-text-splitters==0.3.0

langsmith==0.1.129

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.2

pydantic_core==2.23.4

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.35

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.13.1

※ pip install langchain 명령을 실행했다.