■ AgentExecutor 클래스의 stream 메소드를 사용해 질의 응답하는 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) 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'}])]} |
{'steps': [AgentStep(action=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'), observation=5)], 'messages': [FunctionMessage(content='5', additional_kwargs={}, response_metadata={}, name='magicFunction')]}
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{'output': 'The value of magicFunction(3) is 5.', 'messages': [AIMessage(content='The value of magicFunction(3) is 5.', additional_kwargs={}, response_metadata={})]}
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"""
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▶ 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 langsmith==0.1.134 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==1.15.0 |
※ pip install python-dotenv langchain langchain-openai 명령을 실행했다.