PROMPT TEMPLATE Archives

[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] 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] 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] 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] 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] 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

[PYTHON/LANGCHAIN] AgentExecutor 클래스 : 생성자에서 agent/tools 인자를 사용해 AgentExecutor 객체 만들기 2

■ AgentExecutor 클래스의 생성자에서 agent/tools 인자를 사용해 AgentExecutor 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] create_tool_calling_agent 함수 : 모델, 도구 및 프롬프트 템플리트를 결합한 RunnableSequence 객체 만들기 2

■ create_tool_calling_agent 함수를 사용해 모델, 도구 및 프롬프트 템플리트를 결합한 RunnableSequence 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에

[PYTHON/LANGCHAIN] pull 함수 : 랭체인 허브에서 OPEN-AI 함수 관련 ChatPromptTemplate 객체 만들기

■ pull 함수를 사용해 랭체인 허브에서 OPEN-AI 함수 관련 ChatPromptTemplate 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain import hub

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

from langchain import hub

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

▶ requirements.txt


aiohappyeyeballs==2.4.3
aiohttp==3.10.9
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==3.4.0
exceptiongroup==1.2.2
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==0.3.3
langchain-core==0.3.10
langchain-text-splitters==0.3.0
langsmith==0.1.132
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
propcache==0.2.0
pydantic==2.9.2
pydantic_core==2.23.4
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_extensions==4.12.2
urllib3==2.2.3
yarl==1.14.0

aiohappyeyeballs==2.4.3

aiohttp==3.10.9

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==3.4.0

exceptiongroup==1.2.2

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==0.3.3

langchain-core==0.3.10

langchain-text-splitters==0.3.0

langsmith==0.1.132

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

propcache==0.2.0

pydantic==2.9.2

pydantic_core==2.23.4

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_extensions==4.12.2

urllib3==2.2.3

yarl==1.14.0

※ pip

[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : from_messages 정적 메소드를 사용해 멀티모달 프롬프트 만들기 2

■ ChatPromptTemplate 클래스의 from_messages 정적 메소드를 사용해 멀티모달 프롬프트를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : from_messages 정적 메소드를 사용해 멀티모달 프롬프트 만들기

[PYTHON/LANGCHAIN] ChatOpenAI 클래스 : FewShotPromptTemplate 객체를 사용해 채팅하기

■ ChatOpenAI 클래스에서 FewShotPromptTemplate 객체를 사용해 채팅하는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py


from dotenv                          import load_dotenv
from langchain_core.prompts          import PromptTemplate
from langchain_core.prompts.few_shot import FewShotPromptTemplate
from langchain_openai                import ChatOpenAI

load_dotenv()

exampleList = [
    {
        "question" : "아이유로 삼행시 만들어주세요.",
        "answer"   : """
아 : 아이유는
이 : 이런 강의를 들을 이
유 : 유가 없다.
"""
    },
    {
        "question" : "김민수로 삼행시를 만들어주세요.",
        "answer"   : """
김 : 김치는 맛있다.
민 : 민달팽이도 좋아하는 김치!
수 : 수억을 줘도 김치는 내꺼!
"""
    }
]

examplePromptTemplate = PromptTemplate(input_variables = ["quesiton", "answer"], template = "Question : {question}\n{answer}")

fewShotPromptTemplate = FewShotPromptTemplate(
    examples        = exampleList,
    example_prompt  = examplePromptTemplate,
    suffix          = "Question : {input}",
    input_variables = ["input"]
)

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

runnableSequence = fewShotPromptTemplate | chatOpenAI

responseAIMessage = runnableSequence.invoke({"input" : "홍길동으로 삼행시를 만들어주세요."})

print(responseAIMessage.content)

from dotenv import load_dotenv

from langchain_core.prompts import PromptTemplate

from langchain_core.prompts.few_shot import FewShotPromptTemplate

from langchain_openai import ChatOpenAI

load_dotenv()

exampleList = [

{

"question" : "아이유로 삼행시 만들어주세요.",

"answer" : """

아 : 아이유는

이 : 이런 강의를 들을 이

유 : 유가 없다.

"""

{

"question" : "김민수로 삼행시를 만들어주세요.",

"answer" : """

김 : 김치는 맛있다.

민 : 민달팽이도 좋아하는 김치!

수 : 수억을 줘도 김치는 내꺼!

"""

}

]

examplePromptTemplate = PromptTemplate(input_variables = ["quesiton", "answer"], template = "Question : {question}\n{answer}")

fewShotPromptTemplate = FewShotPromptTemplate(

examples = exampleList,

example_prompt = examplePromptTemplate,

suffix = "Question : {input}",

input_variables = ["input"]

)

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

runnableSequence = fewShotPromptTemplate | chatOpenAI

responseAIMessage = runnableSequence.invoke({"input" : "홍길동으로 삼행시를 만들어주세요."})

print(responseAIMessage.content)

▶ requirements.txt

[PYTHON/LANGCHAIN] FewShotPromptTemplate 클래스 : 생성자에서 examples/example_prompt/suffix/input_variables 인자를 사용해 FewShotPromptTemplate 객체 만들기

■ FewShotPromptTemplate 클래스의 생성자에서 examples/example_prompt/suffix/input_variables 인자를 사용해 FewShotPromptTemplate 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts          import PromptTemplate
from langchain_core.prompts.few_shot import FewShotPromptTemplate

exampleList = [
    {
        "question" : "아이유로 삼행시 만들어주세요.",
        "answer"   : """
아 : 아이유는
이 : 이런 강의를 들을 이
유 : 유가 없다.
"""
    },
    {
        "question" : "김민수로 삼행시를 만들어주세요.",
        "answer"   : """
김 : 김치는 맛있다.
민 : 민달팽이도 좋아하는 김치!
수 : 수억을 줘도 김치는 내꺼!
"""
    }
]

examplePromptTemplate = PromptTemplate(input_variables = ["quesiton", "answer"], template = "Question : {question}\n{answer}")

fewShotPromptTemplate = FewShotPromptTemplate(
    examples        = exampleList,
    example_prompt  = examplePromptTemplate,
    suffix          = "Question : {input}",
    input_variables = ["input"]
)

from langchain_core.prompts import PromptTemplate

from langchain_core.prompts.few_shot import FewShotPromptTemplate

exampleList = [

{

"question" : "아이유로 삼행시 만들어주세요.",

"answer" : """

아 : 아이유는

이 : 이런 강의를 들을 이

유 : 유가 없다.

"""

{

"question" : "김민수로 삼행시를 만들어주세요.",

"answer" : """

김 : 김치는 맛있다.

민 : 민달팽이도 좋아하는 김치!

수 : 수억을 줘도 김치는 내꺼!

"""

}

]

examplePromptTemplate = PromptTemplate(input_variables = ["quesiton", "answer"], template = "Question : {question}\n{answer}")

fewShotPromptTemplate = FewShotPromptTemplate(

examples = exampleList,

example_prompt = examplePromptTemplate,

suffix = "Question : {input}",

input_variables = ["input"]

)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.8
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.16
langchain-core==0.2.39
langchain-text-splitters==0.2.4
langsmith==0.1.117
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.1
pydantic_core==2.23.3
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.34
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.8

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.16

langchain-core==0.2.39

langchain-text-splitters==0.2.4

langsmith==0.1.117

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.1

pydantic_core==2.23.3

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.34

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.11.1

※ pip install langchain

[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : from_messages 정적 메소드에서 SystemMessagePromptTemplate/HumanMessagePromptTemplate 객체 리스를 사용해 ChatPromptTemplate 객체 만들기

■ ChatPromptTemplate 클래스의 from_messages 정적 메소드에서 SystemMessagePromptTemplate/HumanMessagePromptTemplate 객체 리스를 사용해 ChatPromptTemplate 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에

[PYTHON/LANGCHAIN] HumanMessagePromptTemplate 클래스 : from_template 정적 메소드를 사용해 HumanMessagePromptTemplate 객체 만들기

■ HumanMessagePromptTemplate 클래스의 from_template 정적 메소드를 사용해 HumanMessagePromptTemplate 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import HumanMessagePromptTemplate

humanTemplateString = "{재료}"

humanMessagePromptTemplate = HumanMessagePromptTemplate.from_template(humanTemplateString)

from langchain_core.prompts import HumanMessagePromptTemplate

humanTemplateString = "{재료}"

humanMessagePromptTemplate = HumanMessagePromptTemplate.from_template(humanTemplateString)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.8
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.16
langchain-core==0.2.39
langchain-text-splitters==0.2.4
langsmith==0.1.117
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.1
pydantic_core==2.23.3
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.34
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.8

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.16

langchain-core==0.2.39

langchain-text-splitters==0.2.4

langsmith==0.1.117

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.1

pydantic_core==2.23.3

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.34

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.11.1

※ pip install langchain

[PYTHON/LANGCHAIN] SystemMessagePromptTemplate 클래스 : from_template 정적 메소드를 사용해 SystemMessagePromptTemplate 객체 만들기

■ SystemMessagePromptTemplate 클래스의 from_template 정적 메소드를 사용해 SystemMessagePromptTemplate 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import SystemMessagePromptTemplate

systemTemplateString = """
당신은 요리사입니다. 제가 가진 재료들을 갖고 만들 수 있는 요리를 추천하고, 그 요리의 레시피를 추천해주시기 바랍니다.
제가 가진 재료는 아래와 같습니다.

<재료>
{재료}

"""

systemMessagePromptTemplate = SystemMessagePromptTemplate.from_template(systemTemplateString)

from langchain_core.prompts import SystemMessagePromptTemplate

systemTemplateString = """

당신은 요리사입니다. 제가 가진 재료들을 갖고 만들 수 있는 요리를 추천하고, 그 요리의 레시피를 추천해주시기 바랍니다.

제가 가진 재료는 아래와 같습니다.

<재료>

{재료}

"""

systemMessagePromptTemplate = SystemMessagePromptTemplate.from_template(systemTemplateString)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.8
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.16
langchain-core==0.2.39
langchain-text-splitters==0.2.4
langsmith==0.1.117
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.1
pydantic_core==2.23.3
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.34
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.8

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.16

langchain-core==0.2.39

langchain-text-splitters==0.2.4

langsmith==0.1.117

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.1

pydantic_core==2.23.3

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.34

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.11.1

※ pip install langchain

[PYTHON/LANGCHAIN] PromptTemplate 클래스 : 생성자에서 input_variables/template 인자를 사용해 PromptTemplate 객체 만들기 2

■ PromptTemplate 클래스의 생성자에서 input_variables/template 인자를 사용해 PromptTemplate 객체를 만드는 방법을 보여준다. ※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다. ▶ main.py

[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : format_prompt 메소드를 사용해 ChatPromptValue 객체 만들기

■ ChatPromptTemplate 클래스의 format_prompt 메소드를 사용해 ChatPromptValue 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_template("tell me a joke about {subject}")

chatPromptValue = chatPromptTemplate.format_prompt(subject = "soccer")

humanMessage = chatPromptValue.messages[0]

print(humanMessage.content)

from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_template("tell me a joke about {subject}")

chatPromptValue = chatPromptTemplate.format_prompt(subject = "soccer")

humanMessage = chatPromptValue.messages[0]

print(humanMessage.content)

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.8
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.16
langchain-core==0.2.39
langchain-text-splitters==0.2.4
langsmith==0.1.117
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.1
pydantic_core==2.23.3
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.34
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.8

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.16

langchain-core==0.2.39

langchain-text-splitters==0.2.4

langsmith==0.1.117

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.1

pydantic_core==2.23.3

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.34

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.11.1

※ pip install langchain 명령을

[PYTHON/LANGCHAIN] ChatPromptTemplate 클래스 : from_template 정적 메소드를 사용해 ChatPromptTemplate 객체 만들기

■ ChatPromptTemplate 클래스의 from_template 정적 메소드를 사용해 ChatPromptTemplate 객체를 만드는 방법을 보여준다. ▶ main.py


from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_template("tell me a joke about {subject}")

from langchain_core.prompts import ChatPromptTemplate

chatPromptTemplate = ChatPromptTemplate.from_template("tell me a joke about {subject}")

▶ requirements.txt


aiohappyeyeballs==2.4.0
aiohttp==3.10.5
aiosignal==1.3.1
annotated-types==0.7.0
anyio==4.4.0
attrs==24.2.0
certifi==2024.8.30
charset-normalizer==3.3.2
frozenlist==1.4.1
greenlet==3.1.0
h11==0.14.0
httpcore==1.0.5
httpx==0.27.2
idna==3.8
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.2.16
langchain-core==0.2.39
langchain-text-splitters==0.2.4
langsmith==0.1.117
multidict==6.1.0
numpy==1.26.4
orjson==3.10.7
packaging==24.1
pydantic==2.9.1
pydantic_core==2.23.3
PyYAML==6.0.2
requests==2.32.3
sniffio==1.3.1
SQLAlchemy==2.0.34
tenacity==8.5.0
typing_extensions==4.12.2
urllib3==2.2.2
yarl==1.11.1

aiohappyeyeballs==2.4.0

aiohttp==3.10.5

aiosignal==1.3.1

annotated-types==0.7.0

anyio==4.4.0

attrs==24.2.0

certifi==2024.8.30

charset-normalizer==3.3.2

frozenlist==1.4.1

greenlet==3.1.0

h11==0.14.0

httpcore==1.0.5

httpx==0.27.2

idna==3.8

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.2.16

langchain-core==0.2.39

langchain-text-splitters==0.2.4

langsmith==0.1.117

multidict==6.1.0

numpy==1.26.4

orjson==3.10.7

packaging==24.1

pydantic==2.9.1

pydantic_core==2.23.3

PyYAML==6.0.2

requests==2.32.3

sniffio==1.3.1

SQLAlchemy==2.0.34

tenacity==8.5.0

typing_extensions==4.12.2

urllib3==2.2.2

yarl==1.11.1

※ pip install langchain