Interacción con APIs de LLM (OpenAI, Anthropic)
Comprenda cómo interactuar mediante programación con proveedores líderes de LLM como OpenAI y Anthropic usando sus APIs oficiales.
Interacción con APIs de LLM (OpenAI, Anthropic) es una lección gratuita de Prompt Engineering & LLM Optimization for Developers en CoddyKit. Esta es la lección 1 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 Prompt Engineering & LLM Optimization for Developers, y tu progreso se sincroniza en la web y la app de CoddyKit. El curso de Prompt Engineering & LLM Optimization for Developers incluye 4 lecciones en total.
Partes de esta lección aún no han sido traducidas y se muestran en inglés.
Why Use LLM APIs?
Large Language Models (LLMs) like those from OpenAI and Anthropic are incredibly powerful. To use them in your own apps, you need to interact with their Application Programming Interfaces (APIs).
An API acts like a messenger, allowing your code to send requests to the LLM and receive its responses. This enables you to build dynamic, AI-powered features into your applications.
API Keys: Your Access Pass
To use an LLM API, you need an API key. Think of it as a password that authenticates your requests and links them to your account for billing.
- Get your key: Sign up on OpenAI or Anthropic's platform and generate an API key.
- Keep it secret: Never hardcode your API key directly in your code. Store it securely, ideally as an environment variable.
- Environment variables: These are system-wide variables that your program can access without the key being visible in the code itself.
OpenAI API: Initial Setup
Let's start with OpenAI. First, you'll need to install their official Python client library. Then, set up your API key for authentication.
Run this in your terminal:
pip install openaiThen, in your Python script, you'll typically set the API key like this (before making calls):
import os
# It's best practice to load from an environment variable
# e.g., export OPENAI_API_KEY='sk-your-key-here'
# This line is usually enough if OPENAI_API_KEY is set
# The client automatically picks it up.
# If you need to set it manually in code (NOT recommended for production):
# from openai import OpenAI
# client = OpenAI(api_key="YOUR_ACTUAL_API_KEY")
print("OpenAI client library installed and ready!")
print("Ensure OPENAI_API_KEY is set as an environment variable.")OpenAI API: First Chat Completion
The core of OpenAI's API for conversation is the Chat Completions endpoint. You send a list of 'messages' and the model responds with the next message in the conversation.
Try running this simple example:
from openai import OpenAI
import os
# Initialize the client. It will automatically pick up OPENAI_API_KEY
# from your environment variables if it's set.
client = OpenAI()
def get_completion(prompt_text):
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=[
{"role": "user", "content": prompt_text}
],
temperature=0.7,
max_tokens=50
)
return response.choices[0].message.content
if __name__ == "__main__":
# Make sure you have your OPENAI_API_KEY set as an environment variable
# before running this code.
if "OPENAI_API_KEY" not in os.environ:
print("Error: OPENAI_API_KEY environment variable not set.")
print("Please set it before running this script.")
else:
user_prompt = "What is the capital of France?"
print(f"User: {user_prompt}")
llm_response = get_completion(user_prompt)
print(f"LLM: {llm_response}")OpenAI: Key Parameters
When making an OpenAI API call, these parameters are crucial:
model: Specifies which LLM to use (e.g.,"gpt-3.5-turbo","gpt-4"). Different models have different capabilities and costs.messages: A list of message objects defining the conversation history. Each object has a"role"and"content".temperature: Controls the randomness of the output. Higher values (e.g., 0.8) make the output more creative; lower values (e.g., 0.2) make it more focused and deterministic.max_tokens: The maximum number of tokens (words/pieces of words) the model should generate in its response.
OpenAI: Understanding Message Roles
The messages parameter in OpenAI's API uses specific roles to guide the conversation flow:
system: Sets the overall behavior or persona of the assistant.user: Represents the user's input to the assistant.assistant: Represents the assistant's previous responses.
Including past assistant messages helps the model maintain context.
from openai import OpenAI
import os
client = OpenAI()
def get_contextual_completion(messages_list):
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages=messages_list,
temperature=0.7,
max_tokens=70
)
return response.choices[0].message.content
if __name__ == "__main__":
if "OPENAI_API_KEY" not in os.environ:
print("Error: OPENAI_API_KEY environment variable not set.")
print("Please set it before running this script.")
else:
conversation_history = [
{"role": "system", "content": "You are a helpful assistant that provides short, factual answers."},
{"role": "user", "content": "What is the capital of Japan?"},
{"role": "assistant", "content": "The capital of Japan is Tokyo."},
{"role": "user", "content": "And of Germany?"}
]
print("Current conversation:")
for msg in conversation_history:
print(f"{msg['role'].capitalize()}: {msg['content']}")
llm_response = get_contextual_completion(conversation_history)
print(f"Assistant: {llm_response}")Anthropic API: Initial Setup
Now let's look at Anthropic's Claude models. Similar to OpenAI, you'll install their client library and set your API key.
Run this in your terminal:
pip install anthropicThen, prepare your Python script:
import os
# It's best practice to load from an environment variable
# e.g., export ANTHROPIC_API_KEY='sk-ant-your-key-here'
# The client automatically picks it up if ANTHROPIC_API_KEY is set.
# If you need to set it manually in code (NOT recommended for production):
# from anthropic import Anthropic
# client = Anthropic(api_key="YOUR_ACTUAL_ANTHROPIC_API_KEY")
print("Anthropic client library installed and ready!")
print("Ensure ANTHROPIC_API_KEY is set as an environment variable.")Anthropic API: First Messages Call
Anthropic's main API for conversational models is called the Messages API. It also uses a list of messages, but with slightly different role names and structure compared to OpenAI.
Run this example to see it in action:
from anthropic import Anthropic
import os
# Initialize the client. It will automatically pick up ANTHROPIC_API_KEY
# from your environment variables if it's set.
client = Anthropic()
def get_claude_completion(prompt_text):
response = client.messages.create(
model="claude-3-haiku-20240307", # A fast, cheaper Claude model
max_tokens=50,
temperature=0.7,
messages=[
{"role": "user", "content": prompt_text}
]
)
return response.content[0].text
if __name__ == "__main__":
# Make sure you have your ANTHROPIC_API_KEY set as an environment variable
# before running this code.
if "ANTHROPIC_API_KEY" not in os.environ:
print("Error: ANTHROPIC_API_KEY environment variable not set.")
print("Please set it before running this script.")
else:
user_prompt = "Tell me a very short fun fact about space."
print(f"User: {user_prompt}")
claude_response = get_claude_completion(user_prompt)
print(f"Claude: {claude_response}")Anthropic: Key Parameters & Roles
Anthropic's Messages API shares similarities with OpenAI but has some distinctions:
model: Specifies the Claude model (e.g.,"claude-3-haiku-20240307","claude-3-opus-20240229").messages: A list of message objects. Each message must alternate between"user"and"assistant"roles. Unlike OpenAI, Anthropic does not have a distinct"system"role; system instructions are included in the first"user"message or a dedicated"system"parameter.max_tokens: The maximum number of tokens Claude should generate.temperature: Controls creativity, similar to OpenAI.
API Interaction Check
You've learned the basics of interacting with both OpenAI and Anthropic APIs. Let's test your understanding!
Recap: Connecting to LLMs
In this lesson, you've taken your first steps into programmatic interaction with LLMs!
- We understood the importance of LLM APIs for building AI-powered applications.
- You learned how to securely handle API keys using environment variables.
- We explored setting up and making basic chat completion calls with both OpenAI's and Anthropic's Python client libraries.
- You now understand key parameters like
model,messages,temperature, andmax_tokensfor both platforms.
This knowledge is foundational for integrating powerful LLMs into your developer workflows!
Preguntas frecuentes
¿La lección «Interacción con APIs de LLM (OpenAI, Anthropic)» es gratis?
Sí — el texto completo de «Interacción con APIs de LLM (OpenAI, Anthropic)» 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 Prompt Engineering & LLM Optimization for Developers, actualiza a CoddyKit PRO. El curso de Prompt Engineering & LLM Optimization for Developers incluye 4 lecciones en total.
¿Qué aprenderé en «Interacción con APIs de LLM (OpenAI, Anthropic)»?
Comprenda cómo interactuar mediante programación con proveedores líderes de LLM como OpenAI y Anthropic usando sus APIs oficiales. Practicas Prompt Engineering & LLM Optimization for Developers 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 Prompt Engineering & LLM Optimization for Developers?
No se requiere experiencia previa. Prompt Engineering & LLM Optimization for Developers 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 1 de 4.
¿Cuánto tiempo toma la lección «Interacción con APIs de LLM (OpenAI, Anthropic)»?
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 Prompt Engineering & LLM Optimization for Developers?
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Todas las lecciones de este curso
- Interacción con APIs de LLM (OpenAI, Anthropic)
- Conceptos básicos de LangChain y LlamaIndex
- Gestión y control de versiones de prompts
- Fundamentos de Retrieval-Augmented Generation (RAG)