■ load_tools 함수를 사용해 serpapi와 llm-math 도구를 로드하는 방법을 보여준다.
▶ main.py
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import os from langchain_openai import ChatOpenAI from langchain_community.agent_toolkits.load_tools import load_tools os.environ["OPENAI_API_KEY" ] = "<OPENAI_API_KEY>" os.environ["SERPAPI_API_KEY"] = "<SERPAPI_API_KEY>" chatOpenAI = ChatOpenAI(model = "gpt-4") toolList = load_tools(tool_names = ["serpapi", "llm-math"], llm = chatOpenAI) # 도구를 로드한다. print(toolList) """ [ Tool(name='Search', description='A search engine. Useful for when you need to answer questions about current events. Input should be a search query.', func=<bound method SerpAPIWrapper.run of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>, coroutine=<bound method SerpAPIWrapper.arun of SerpAPIWrapper(search_engine=<class 'serpapi.google_search.GoogleSearch'>, params={'engine': 'google', 'google_domain': 'google.com', 'gl': 'us', 'hl': 'en'}, serpapi_api_key='83dffd65104b51e264e6f063394f66a3d22a492631a4f438c9b1a71b89cbbd24', aiosession=None)>), Tool(name='Calculator', description='Useful for when you need to answer questions about math.', func=<bound method Chain.run of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>, coroutine=<bound method Chain.arun of LLMMathChain(llm_chain=LLMChain(prompt=PromptTemplate(input_variables=['question'], template='Translate a math problem into a expression that can be executed using Python\'s numexpr library. Use the output of running this code to answer the question.\n\nQuestion: ${{Question with math problem.}}\n```text\n${{single line mathematical expression that solves the problem}}\n```\n...numexpr.evaluate(text)...\n```output\n${{Output of running the code}}\n```\nAnswer: ${{Answer}}\n\nBegin.\n\nQuestion: What is 37593 * 67?\n```text\n37593 * 67\n```\n...numexpr.evaluate("37593 * 67")...\n```output\n2518731\n```\nAnswer: 2518731\n\nQuestion: 37593^(1/5)\n```text\n37593**(1/5)\n```\n...numexpr.evaluate("37593**(1/5)")...\n```output\n8.222831614237718\n```\nAnswer: 8.222831614237718\n\nQuestion: {question}\n'), llm=ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7fc2c5782170>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7fc2c5783880>, model_name='gpt-4', openai_api_key=SecretStr('**********'), openai_proxy='')))>) ] """ |
▶ requirements.txt
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aiohttp==3.9.5 aiosignal==1.3.1 annotated-types==0.7.0 anyio==4.4.0 async-timeout==4.0.3 attrs==23.2.0 certifi==2024.6.2 charset-normalizer==3.3.2 dataclasses-json==0.6.7 distro==1.9.0 exceptiongroup==1.2.1 frozenlist==1.4.1 google-search-results==2.4.2 greenlet==3.0.3 h11==0.14.0 httpcore==1.0.5 httpx==0.27.0 idna==3.7 jsonpatch==1.33 jsonpointer==2.4 langchain==0.2.3 langchain-community==0.2.4 langchain-core==0.2.5 langchain-openai==0.1.8 langchain-text-splitters==0.2.1 langsmith==0.1.75 marshmallow==3.21.3 multidict==6.0.5 mypy-extensions==1.0.0 numexpr==2.10.0 numpy==1.26.4 openai==1.33.0 orjson==3.10.3 packaging==23.2 pydantic==2.7.3 pydantic_core==2.18.4 PyYAML==6.0.1 regex==2024.5.15 requests==2.32.3 sniffio==1.3.1 SQLAlchemy==2.0.30 tenacity==8.3.0 tiktoken==0.7.0 tqdm==4.66.4 typing-inspect==0.9.0 typing_extensions==4.12.2 urllib3==2.2.1 yarl==1.9.4 |
※ pip install numexpr google-search-results langchain langchain-community langchain-openai 명령을 실행했다.