Construcción de su primer agente sencillo

Build Your First Agent!

Set Up Your Python Environment

Install LangChain & Dependencies

Connect to an LLM

import os
from langchain_openai import ChatOpenAI

def main():
    # In a real setup, ensure OPENAI_API_KEY is set
    # For demonstration, we'll assume it's available
    # os.environ["OPENAI_API_KEY"] = "sk-..." # DON'T hardcode!

    try:
        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
        print("LLM initialized successfully!")
        # You can test it:
        # response = llm.invoke("Hello, LLM!")
        # print(response.content)
    except Exception as e:
        print(f"Error initializing LLM: {e}")
        print("Please ensure OPENAI_API_KEY is set.")

if __name__ == "__main__":
    main()

Agents Need Tools

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain

def main():
    # Ensure OPENAI_API_KEY is set
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)

    # Create a basic math tool
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]
    print("Basic math tool created and ready!")

if __name__ == "__main__":
    main()

Choosing Your Agent Type

Combining Components

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import ChatPromptTemplate

def main():
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]

    # Define the prompt for the agent
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful AI assistant. Use tools when necessary."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    # Create the agent
    agent = create_react_agent(llm, tools, prompt)

    # Create the agent executor
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
    print("Agent assembled and ready for action!")

if __name__ == "__main__":
    main()

Asking Your Agent a Question

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import ChatPromptTemplate

def main():
    llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]

    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful AI assistant. Use tools when necessary."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    agent = create_react_agent(llm, tools, prompt)
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    print("Asking the agent: 'What is 123 multiplied by 456?'")
    response = agent_executor.invoke({"input": "What is 123 multiplied by 456?"})
    print("\nAgent's final answer:")
    print(response["output"])

if __name__ == "__main__":
    main()

Understanding the Agent's Response

Your Complete First Agent!

import os
from langchain_openai import ChatOpenAI
from langchain.tools import Tool
from langchain_community.utilities import LLMMathChain
from langchain.agents import AgentExecutor, create_react_agent
from langchain_core.prompts import ChatPromptTemplate

def main():
    # 1. Set up your LLM
    # Ensure OPENAI_API_KEY is set as an environment variable
    # os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE" # DO NOT hardcode!
    try:
        llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
    except Exception as e:
        print(f"Error: {e}. Please ensure OPENAI_API_KEY is set.")
        return

    # 2. Define your tools
    llm_math_chain = LLMMathChain.from_llm(llm)
    math_tool = Tool.from_function(
        func=llm_math_chain.run,
        name="Calculator",
        description="Useful for when you need to answer questions about math."
    )
    tools = [math_tool]

    # 3. Define the agent's prompt
    prompt = ChatPromptTemplate.from_messages([
        ("system", "You are a helpful AI assistant. Use tools when necessary."),
        ("human", "{input}"),
        ("placeholder", "{agent_scratchpad}")
    ])

    # 4. Create the agent
    agent = create_react_agent(llm, tools, prompt)

    # 5. Create the AgentExecutor
    agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

    # 6. Run the agent
    print("\n--- Running the Agent ---")
    question = "What is the square root of 144 plus 25?"
    print(f"Agent input: '{question}'")
    response = agent_executor.invoke({"input": question})
    print("\n--- Agent's Final Answer ---")
    print(response["output"])

if __name__ == "__main__":
    main()

Agent Components Check

Recap: Your First Agent!