Integración con API externas
Conecte sus agentes a servicios de terceros y API propietarias para aprovechar un amplio ecosistema de datos y funcionalidades.
Integración con API externas es una lección gratuita de AI Agents with LangChain & Autonomous Workflows en CoddyKit. Esta es la lección 2 de 4. Puedes leer la lección completa abajo gratuitamente — luego la practicas en el navegador con un editor de código integrado y un tutor de IA 24/7. Forma parte de la ruta de aprendizaje de AI Agents with LangChain & Autonomous Workflows, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de AI Agents with LangChain & Autonomous Workflows incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
Agents & External APIs
AI agents are powerful, but their knowledge is often limited to their training data. To interact with the real world, they need to fetch live information or perform actions.
This is where External APIs come in! They are the bridge for agents to access a vast ecosystem of services and data.
Tools for External Access
In LangChain, Tools are how agents interact with external systems. Think of them as specialized functions an agent can "call" when needed.
- A tool could search the web.
- Another might fetch weather data.
- Or even send an email!
We'll build tools to wrap API calls.
API: Your Agent's Interface
An API (Application Programming Interface) is a set of rules allowing different software applications to communicate with each other.
When your agent uses an API, it sends a request (like asking a question) and receives a response (the answer or data).
- Endpoint: A specific URL for an API function.
- Request: What your agent sends (e.g., "get me the current weather for London").
- Response: What the API sends back (e.g., weather data in JSON).
Securing API Access
Many external APIs require authentication to ensure only authorized users access their services. Common methods include:
- API Keys: A unique string passed with each request.
- OAuth: A more complex protocol for secure delegated access.
Always keep your API keys secret and never hardcode them directly in your public code!
Python & HTTP Requests
To interact with APIs, Python needs to send HTTP requests. The requests library is the standard way to do this.
Here's how to make a simple GET request to a public API:
import requests
def fetch_joke():
url = "https://official-joke-api.appspot.com/random_joke"
response = requests.get(url)
if response.status_code == 200:
return response.json()
else:
return {"error": "Could not fetch joke."}
if __name__ == "__main__":
joke_data = fetch_joke()
if "error" not in joke_data:
print(f"Setup: {joke_data['setup']}")
print(f"Punchline: {joke_data['punchline']}")
else:
print(joke_data["error"])
From Function to LangChain Tool
Now, let's turn our API-calling function into a LangChain Tool. This involves defining a Pydantic model for the tool's input and wrapping the function.
The agent will use this schema to understand how to call your tool.
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
import requests
# Define the input schema for the tool
class JokeInput(BaseModel):
# Our joke API doesn't need specific input,
# but tools usually define what they expect.
query: str = Field(description="A placeholder query, not used by this API.")
class JokeTool(BaseTool):
name = "get_random_joke"
description = (
"Useful for when you need a random joke. "
"Returns a setup and a punchline."
)
args_schema: type[BaseModel] = JokeInput
def _run(self, query: str):
url = "https://official-joke-api.appspot.com/random_joke"
response = requests.get(url)
if response.status_code == 200:
joke = response.json()
return f"Setup: {joke['setup']}\nPunchline: {joke['punchline']}"
return "Failed to fetch a joke."
async def _arun(self, query: str):
# Asynchronous version (optional, but good practice)
raise NotImplementedError("Asynchronous call not implemented for this tool yet.")
if __name__ == "__main__":
joke_tool = JokeTool()
print(joke_tool._run("tell me a joke"))
Agent with API Tool
With our JokeTool ready, we can now provide it to a LangChain agent. The agent will then decide when and how to use this tool based on the user's prompt.
Remember to initialize your LLM first!
# Assuming you have an OpenAI API key set as an environment variable
# export OPENAI_API_KEY="..."
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
import requests
import os
# Re-define JokeTool for completeness in runnable snippet
class JokeInput(BaseModel):
query: str = Field(description="A placeholder query, not used by this API.")
class JokeTool(BaseTool):
name = "get_random_joke"
description = "Useful for when you need a random joke. Returns a setup and a punchline."
args_schema: type[BaseModel] = JokeInput
def _run(self, query: str):
url = "https://official-joke-api.appspot.com/random_joke"
response = requests.get(url)
if response.status_code == 200:
joke = response.json()
return f"Setup: {joke['setup']}\nPunchline: {joke['punchline']}"
return "Failed to fetch a joke."
async def _arun(self, query: str):
raise NotImplementedError("Async not implemented.")
if __name__ == "__main__":
# Ensure OPENAI_API_KEY is set in environment
if not os.getenv("OPENAI_API_KEY"):
print("Please set your OPENAI_API_KEY environment variable.")
exit()
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
tools = [JokeTool()]
# Get the prompt for the ReAct agent
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
print("Agent is ready!")
# Example usage will be in the next scene.
Agent Using the API Tool
Now, let's ask our agent to tell us a joke. Observe how it uses the get_random_joke tool we provided.
The verbose=True setting helps us see the agent's "thought process" and tool calls.
# This code block assumes the setup from the previous scene
# has been executed and the agent_executor is available.
# In a real interactive environment, you'd run this after Scene 7.
# For demonstration, we re-initialize parts needed for execution
import os
from langchain_openai import ChatOpenAI
from langchain.agents import AgentExecutor, create_react_agent
from langchain import hub
from langchain.tools import BaseTool
from pydantic import BaseModel, Field
import requests
# Re-define JokeTool for completeness
class JokeInput(BaseModel):
query: str = Field(description="A placeholder query, not used by this API.")
class JokeTool(BaseTool):
name = "get_random_joke"
description = "Useful for when you need a random joke. Returns a setup and a punchline."
args_schema: type[BaseModel] = JokeInput
def _run(self, query: str):
url = "https://official-joke-api.appspot.com/random_joke"
response = requests.get(url)
if response.status_code == 200:
joke = response.json()
return f"Setup: {joke['setup']}\nPunchline: {joke['punchline']}"
return "Failed to fetch a joke."
async def _arun(self, query: str):
raise NotImplementedError("Async not implemented.")
if __name__ == "__main__":
if not os.getenv("OPENAI_API_KEY"):
print("Please set your OPENAI_API_KEY environment variable.")
exit()
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
tools = [JokeTool()]
prompt = hub.pull("hwchase17/react")
agent = create_react_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
print("--- Asking the agent for a joke ---")
response = agent_executor.invoke({"input": "Tell me a random joke."})
print("\nAgent's final answer:")
print(response["output"])
Robust API Integration
External APIs can fail due to network issues, rate limits, invalid requests, or server errors. It's crucial to build robust tools that gracefully handle these situations.
- Try-except blocks: Catch network errors.
- Status codes: Check HTTP status codes (e.g., 200 for success, 4xx for client errors, 5xx for server errors).
- Informative messages: Return clear error messages to the agent.
Beyond Simple APIs
The principles learned here extend to more complex APIs. You might encounter:
- APIs requiring specific headers or body data (POST requests).
- Pagination for large datasets.
- Asynchronous calls for long-running operations.
Always consult the API's official documentation!
API Integration Check
You've learned how to empower your agents by connecting them to external APIs. Let's test your understanding.
Recap & Next Steps
Great job! You've learned how to empower your AI agents by connecting them to external APIs.
- APIs expand an agent's capabilities beyond its internal knowledge.
- Custom Tools wrap API calls, providing a structured interface for the agent.
- Authentication and error handling are key for robust integrations.
This skill is fundamental for building truly dynamic and useful AI agents. Keep exploring different APIs!
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- Cursos
- 12
- Lecciones
- 50
Preguntas frecuentes
¿La lección «Integración con API externas» es gratis?
Sí — el texto completo de «Integración con API externas» es gratis para leer aquí en la web. Para practicarla de forma interactiva (editor de código integrado y tutor de IA 24/7) y desbloquear el resto del curso de AI Agents with LangChain & Autonomous Workflows, actualiza a CoddyKit PRO. El curso de AI Agents with LangChain & Autonomous Workflows incluye 4 lecciones en total.
¿Qué aprenderé en «Integración con API externas»?
Conecte sus agentes a servicios de terceros y API propietarias para aprovechar un amplio ecosistema de datos y funcionalidades. Practicas AI Agents with LangChain & Autonomous Workflows con código real que ejecutas directamente en el navegador, y un tutor de IA 24/7 responde tus preguntas mientras trabajas en la lección.
¿Necesito experiencia previa para empezar AI Agents with LangChain & Autonomous Workflows?
No se requiere experiencia previa. AI Agents with LangChain & Autonomous Workflows en CoddyKit está estructurado para principiantes hasta estudiantes avanzados, así que puedes empezar aquí o desde el inicio y avanzar a tu ritmo. Esta es la lección 2 de 4.
¿Cuánto tiempo toma la lección «Integración con API externas»?
La mayoría de las lecciones de CoddyKit toman alrededor de 5–10 minutos. Cada una es compacta e interactiva, así que avanzas constantemente y retomas exactamente por donde dejaste en la web y la app.
¿Puedo escribir y ejecutar código en esta lección de AI Agents with LangChain & Autonomous Workflows?
Sí. Cada lección de AI Agents with LangChain & Autonomous Workflows incluye un editor de código integrado, así que escribes y ejecutas código real directamente en tu navegador y obtienes retroalimentación instantánea de IA — sin configuración local necesaria.
Todas las lecciones de este curso
- Creación de herramientas personalizadas para LangChain
- Integración con API externas
- Web scraping y ampliación de datos
- Toolkits y entradas estructuradas de herramientas