[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


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

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)

responseDictionary1 = agentExecutor.invoke({"input" : "hi!"})

print(responseDictionary1["output"])
print("-" * 100)

responseDictionary2 = agentExecutor.invoke({"input" : "how can langsmith help with testing?"})

print(responseDictionary["output"])
print("-" * 100)

responseDictionary2 = agentExecutor.invoke({"input" : "whats the weather in sf?"})

print(responseDictionary["output"])
print("-" * 100)

"""
Hello! 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

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)

responseDictionary1 = agentExecutor.invoke({"input" : "hi!"})

print(responseDictionary1["output"])

print("-" * 100)

responseDictionary2 = agentExecutor.invoke({"input" : "how can langsmith help with testing?"})

print(responseDictionary["output"])

print("-" * 100)

responseDictionary2 = agentExecutor.invoke({"input" : "whats the weather in sf?"})

print(responseDictionary["output"])

print("-" * 100)

"""

Hello! How can I assist you today?

LangSmith is a platform designed to assist in building production-grade LLM (Large Language Model) applications. It helps with testing by offering features to closely monitor and evaluate your application. This enables you to ship applications quickly and with confidence. LangSmith provides tools for logging traces and running evaluations, making it easier to test and refine your application's performance. Additionally, it integrates seamlessly with LangChain, although it can also function independently.
—————————————————————————————————-
The current weather in San Francisco is partly cloudy with a temperature of 13.3°C (55.9°F). The wind is coming from the west-southwest at 8.7 mph (14.0 kph). The humidity is at 93%, and the visibility is 16.0 km (9.0 miles).
—————————————————————————————————-
"""
—————————————————————————————————-

▶ 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
beautifulsoup4==4.12.3
bs4==0.0.2
certifi==2024.8.30
charset-normalizer==3.4.0
dataclasses-json==0.6.7
distro==1.9.0
exceptiongroup==1.2.2
faiss-gpu==1.7.2
frozenlist==1.4.1
greenlet==3.1.1
h11==0.14.0
httpcore==1.0.6
httpx==0.27.2
idna==3.10
jiter==0.6.1
jsonpatch==1.33
jsonpointer==3.0.0
langchain==0.3.3
langchain-community==0.3.2
langchain-core==0.3.10
langchain-openai==0.2.2
langchain-text-splitters==0.3.0
langsmith==0.1.132
marshmallow==3.22.0
multidict==6.1.0
mypy-extensions==1.0.0
numpy==1.26.4
openai==1.51.2
orjson==3.10.7
packaging==24.1
propcache==0.2.0
pydantic==2.9.2
pydantic-settings==2.5.2
pydantic_core==2.23.4
python-dotenv==1.0.1
PyYAML==6.0.2
regex==2024.9.11
requests==2.32.3
requests-toolbelt==1.0.0
sniffio==1.3.1
soupsieve==2.6
SQLAlchemy==2.0.35
tenacity==8.5.0
tiktoken==0.8.0
tqdm==4.66.5
typing-inspect==0.9.0
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

beautifulsoup4==4.12.3

bs4==0.0.2

certifi==2024.8.30

charset-normalizer==3.4.0

dataclasses-json==0.6.7

distro==1.9.0

exceptiongroup==1.2.2

faiss-gpu==1.7.2

frozenlist==1.4.1

greenlet==3.1.1

h11==0.14.0

httpcore==1.0.6

httpx==0.27.2

idna==3.10

jiter==0.6.1

jsonpatch==1.33

jsonpointer==3.0.0

langchain==0.3.3

langchain-community==0.3.2

langchain-core==0.3.10

langchain-openai==0.2.2

langchain-text-splitters==0.3.0

langsmith==0.1.132

marshmallow==3.22.0

multidict==6.1.0

mypy-extensions==1.0.0

numpy==1.26.4

openai==1.51.2

orjson==3.10.7

packaging==24.1

propcache==0.2.0

pydantic==2.9.2

pydantic-settings==2.5.2

pydantic_core==2.23.4

python-dotenv==1.0.1

PyYAML==6.0.2

regex==2024.9.11

requests==2.32.3

requests-toolbelt==1.0.0

sniffio==1.3.1

soupsieve==2.6

SQLAlchemy==2.0.35

tenacity==8.5.0

tiktoken==0.8.0

tqdm==4.66.5

typing-inspect==0.9.0

typing_extensions==4.12.2

urllib3==2.2.3

yarl==1.14.0

※ pip install python-dotenv langchain-community langchain-openai faiss-gpu bs4 명령을 실행했다.