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LLM API Etkileşimi (OpenAI, Anthropic)

Resmî API'lerini kullanarak OpenAI ve Anthropic gibi önde gelen LLM sağlayıcılarıyla program aracılığıyla nasıl etkileşim kuracağınızı anlayın.

LLM API Etkileşimi (OpenAI, Anthropic), CoddyKit'te ücretsiz bir Prompt Engineering & LLM Optimization for Developers dersidir. Bu, 4 dersinin 1. dersidir. Aşağıdan dersin tamamını ücretsiz okuyabilir, sonra tarayıcıda yerleşik kod editörü ve 7/24 yapay zeka koçu ile uygulamalı olarak pratik yapabilirsin. Bu, Prompt Engineering & LLM Optimization for Developers öğrenme yolunun bir parçasıdır ve ilerlemeniz web ve CoddyKit uygulaması arasında senkronize olur. Prompt Engineering & LLM Optimization for Developers kursu toplamda 4 dersten oluşur.

Bu dersin bazı bölümleri henüz çevrilmemiş olup İngilizce olarak gösterilmektedir.

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 openai

Then, 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 anthropic

Then, 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, and max_tokens for both platforms.

This knowledge is foundational for integrating powerful LLMs into your developer workflows!

Sıkça Sorulan Sorular

“LLM API Etkileşimi (OpenAI, Anthropic)” dersi ücretsiz mi?

Evet — “LLM API Etkileşimi (OpenAI, Anthropic)” dersin tüm metni burada web'de ücretsiz olarak okunabilir. Etkileşimli olarak pratik yapmak (yerleşik kod editörü ve 7/24 yapay zeka koçu) ve Prompt Engineering & LLM Optimization for Developers kursunun geri kalanını açmak için CoddyKit PRO'ya yükselt. Prompt Engineering & LLM Optimization for Developers kursu toplamda 4 dersten oluşur.

“LLM API Etkileşimi (OpenAI, Anthropic)” dersinde ne öğreneceğim?

Resmî API'lerini kullanarak OpenAI ve Anthropic gibi önde gelen LLM sağlayıcılarıyla program aracılığıyla nasıl etkileşim kuracağınızı anlayın. Prompt Engineering & LLM Optimization for Developers ile uygulamalı kodu tarayıcıda doğrudan çalıştırarak pratik yaparsın ve 7/24 yapay zeka koçu dersi çalışırken sorularını yanıtlar.

Prompt Engineering & LLM Optimization for Developers öğrenmeye başlamak için deneyim gerekli mi?

Önceden deneyim gerekmez. CoddyKit'te Prompt Engineering & LLM Optimization for Developers, başlangıçtan ileri seviyeye kadar yapılandırıldığı için buradan başlayabilir veya başından başlayıp kendi hızında ilerleme yapabilirsin. Bu, 4 dersinin 1. dersidir.

“LLM API Etkileşimi (OpenAI, Anthropic)” dersi ne kadar sürer?

Çoğu CoddyKit dersi yaklaşık 5–10 dakika sürer. Her biri kısa ve etkileşimli olduğu için sabit ilerleme yaparsın ve web ile uygulama arasında tam olarak bıraktığın yerden devam edebilirsin.

Bu Prompt Engineering & LLM Optimization for Developers dersinde kod yazıp çalıştırabilir miyim?

Evet. Her Prompt Engineering & LLM Optimization for Developers dersi yerleşik bir kod editörü içerir, bu sayede tarayıcıda gerçek kod yazıp çalıştırabilir ve anlık yapay zeka geri bildirimi alırsın — yerel kurulum gerekli değildir.

Bu kursun tüm dersleri

  1. LLM API Etkileşimi (OpenAI, Anthropic)
  2. LangChain ve LlamaIndex Temelleri
  3. İstem Yönetimi ve Sürümleme
  4. Almayla Zenginleştirilmiş Üretime (RAG) Giriş
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