■ AgentExecutor 클래스의 생성자에서 return_intermediate_steps 인자를 사용해 중단 단계를 반환하는 방법을 보여준다.
※ OPENAI_API_KEY 환경 변수 값은 .env 파일에 정의한다.
▶ main.py
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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, return_intermediate_steps = True) query = "what is the value of magic_function(3)?" responseDictionary = agentExecutor.invoke({"input" : query}) print(responseDictionary["intermediate_steps"]) """ [ ( 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_kzVYcLT5CYRpgjxSBjJ3sz6q', '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-ac471672-96e0-4e8d-9fdf-d1ae32c65655', tool_calls = [ { 'name' : 'magicFunction', 'args' : {'input' : 3}, 'id' : 'call_kzVYcLT5CYRpgjxSBjJ3sz6q', 'type' : 'tool_call' } ], tool_call_chunks = [ { 'name' : 'magicFunction', 'args' : '{"input" : 3}', 'id' : 'call_kzVYcLT5CYRpgjxSBjJ3sz6q', 'index' : 0, 'type' : 'tool_call_chunk' } ] ) ], tool_call_id = 'call_kzVYcLT5CYRpgjxSBjJ3sz6q' ), 5 ) ] """ |
▶ requirements.txt
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aiohappyeyeballs==2.4.3 aiohttp==3.10.10 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 distro==1.9.0 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 jiter==0.6.1 jsonpatch==1.33 jsonpointer==3.0.0 langchain==0.3.3 langchain-core==0.3.10 langchain-openai==0.2.2 langchain-text-splitters==0.3.0 langgraph==0.2.35 langgraph-checkpoint==2.0.1 langsmith==0.1.134 msgpack==1.1.0 multidict==6.1.0 numpy==1.26.4 openai==1.51.2 orjson==3.10.7 packaging==24.1 propcache==0.2.0 pydantic==2.9.2 pydantic_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 SQLAlchemy==2.0.35 tenacity==8.5.0 tiktoken==0.8.0 tqdm==4.66.5 typing_extensions==4.12.2 urllib3==2.2.3 yarl==1.15.1 |
※ pip install python-dotenv langchain langchain-openai langgraph 명령을 실행했다.