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| from langchain import hub | |
| from langchain.agents import AgentExecutor, create_openai_tools_agent | |
| from langchain.agents import load_tools | |
| from langchain_openai import ChatOpenAI | |
| def init_config(): | |
| search_tool = load_tools(['serpapi']) | |
| tools = [search_tool[0]] | |
| prompt = hub.pull("hwchase17/openai-tools-agent") | |
| llm = ChatOpenAI(model="gpt-4", temperature=0) | |
| # Construct the OpenAI Tools agent | |
| agent = create_openai_tools_agent(llm, tools, prompt) | |
| agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True) | |
| return agent_executor | |
| def answer_question(agent, question): | |
| full_prompt = f""" | |
| You are a master AI that can control other AI agents. You are specifically designed to automate plant maintenance. | |
| You will recieve a prompt from a user regarding plant maintenance, and will have to classify the prompt's purpose as one of the following categories: | |
| [0] Fertilizing the plant | |
| [1] Turning plant lights on | |
| [2] Turning plant lights off | |
| [3] Watering the plant | |
| Return the category number and name of the prompt in a JSON format with the following format: | |
| {{ | |
| "plant_category": 0, | |
| "plant_action": "Fertilizing the plant" | |
| }} | |
| User: {question} | |
| """ | |
| return agent.invoke({"input": full_prompt})['output'] | |