Making API Requests with Python

Now that you understand the fundamentals of LLM APIs and how authentication works, let's get practical. Sending prompts and receiving completions programmatically involves making HTTP requests from your application to the LLM provider's API endpoint. Python, with its rich ecosystem of libraries, provides effective ways to handle these interactions.

There are two primary approaches for making API calls in Python:

1. Using a general-purpose HTTP library: Libraries like requests allow you to construct and send HTTP requests manually. This gives you fine-grained control over the request details.
2. Using provider-specific Software Development Kits (SDKs): Most major LLM providers (like OpenAI, Anthropic, Google) offer official Python SDKs. These libraries wrap the underlying API calls, providing convenient functions and abstractions tailored to their specific service.

Making Calls with the requests Library

The requests library is a standard choice for making HTTP requests in Python due to its simplicity and power. If you haven't installed it yet, you can do so using pip:

pip install requests

To interact with an LLM API using requests, you typically need to send an HTTP POST request. This request includes:

1. URL: The specific API endpoint provided by the LLM service (e.g., https://api.openai.com/v1/chat/completions).
2. Headers: Information about the request, most importantly:
   • Authorization: Contains your API key for authentication (usually prefixed with Bearer).
   • Content-Type: Specifies the format of the data you are sending, almost always application/json.
3. Body (Payload): The actual data sent to the API, formatted as JSON. This includes the prompt, model selection, and generation parameters like temperature and max_tokens.

Let's illustrate with an example targeting an LLM API endpoint. Assume you have your API key stored in an environment variable LLM_API_KEY.

import requests
import os
import json

# Retrieve the API key from environment variables
api_key = os.getenv("LLM_API_KEY")
if not api_key:
    raise ValueError("API key not found. Set the LLM_API_KEY environment variable.")

# Define the API endpoint
api_url = "https://api.example-llm-provider.com/v1/generate" # Replace with actual endpoint

# Set up the request headers
headers = {
    "Authorization": f"Bearer {api_key}",
    "Content-Type": "application/json",
}

# Construct the request payload (body)
payload = {
    "model": "example-model-v2", # Specify the desired LLM
    "prompt": "Explain the difference between HTTP GET and POST requests.",
    "max_tokens": 150,
    "temperature": 0.7,
    "stop_sequences": ["\n"] # Optional: sequences where generation should stop
}

try:
    # Send the POST request
    response = requests.post(api_url, headers=headers, data=json.dumps(payload))

    # Check if the request was successful (status code 200 OK)
    response.raise_for_status() # Raises an HTTPError for bad responses (4xx or 5xx)

    # Parse the JSON response
    result = response.json()
    print("API Response:")
    print(result)

    # Extract the generated text (structure depends on the API)
    if "choices" in result and len(result["choices"]) > 0:
        generated_text = result["choices"][0].get("text", "No text found")
        print("\nGenerated Text:")
        print(generated_text.strip())
    else:
        print("\nCould not extract generated text from response.")

except requests.exceptions.RequestException as e:
    print(f"An error occurred during the API request: {e}")
except json.JSONDecodeError:
    print(f"Failed to decode JSON response: {response.text}")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

In this example:

• We import the necessary libraries (requests, os, json).
• We securely fetch the API key from an environment variable. Never hardcode API keys directly in your source code.
• We define the headers dictionary, including the Authorization token and Content-Type.
• We create the payload dictionary containing the prompt and model parameters. The exact structure and parameter names will vary depending on the specific LLM API provider. Consult their documentation.
• requests.post() sends the request. We pass the URL, headers, and the JSON-serialized payload (json.dumps(payload)).
• response.raise_for_status() is important for basic error checking. It will raise an exception if the API returns an error status code (like 401 Unauthorized, 429 Rate Limit Exceeded, or 500 Internal Server Error).
• If successful, response.json() parses the JSON response body into a Python dictionary.
• We then attempt to extract the relevant generated text, accounting for the specific structure returned by the API.
• Error handling (try...except) is included to catch potential network issues, JSON parsing problems, or other exceptions.

Using requests provides transparency and works with any HTTP-based API, but requires you to handle details like JSON serialization and header construction manually.

Making Calls with Provider SDKs

LLM providers often supply Python SDKs to simplify interaction with their APIs. These libraries abstract away the lower-level HTTP request details, offering a more Pythonic interface.

For example, using the OpenAI Python library (install with pip install openai), making a chat completion request looks like this:

import os
from openai import OpenAI, OpenAIError

# SDKs often automatically look for the API key in environment variables
# (e.g., OPENAI_API_KEY) or you can pass it during client initialization.
# Ensure OPENAI_API_KEY is set in your environment.
try:
    client = OpenAI() # API key is implicitly read from OPENAI_API_KEY env var

    # Make the API call using the SDK's methods
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": "Explain the concept of recursion in programming.",
            }
        ],
        model="gpt-3.5-turbo", # Specify the model
        max_tokens=100,
        temperature=0.8,
    )

    # Access the response content through SDK objects
    print("API Response Object:")
    # print(chat_completion) # The raw SDK object can be verbose

    if chat_completion.choices:
        response_content = chat_completion.choices[0].message.content
        print("\nGenerated Text:")
        print(response_content.strip())
    else:
        print("No response choices found.")

    # Print usage information if available
    if chat_completion.usage:
        print("\nToken Usage:")
        print(f"Prompt Tokens: {chat_completion.usage.prompt_tokens}")
        print(f"Completion Tokens: {chat_completion.usage.completion_tokens}")
        print(f"Total Tokens: {chat_completion.usage.total_tokens}")


except OpenAIError as e:
    print(f"An OpenAI API error occurred: {e}")
except Exception as e:
    print(f"An unexpected error occurred: {e}")

Notice the differences compared to using requests:

• We instantiate a client object from the openai library. Authentication is often handled implicitly by the SDK (e.g., reading OPENAI_API_KEY from environment variables).
• We call a specific method (client.chat.completions.create) designed for the chat completion endpoint.
• Parameters like model, messages, max_tokens, and temperature are passed as function arguments.
• The SDK handles forming the HTTP request, setting headers, and serializing the data.
• The response is returned as a Python object (often a Pydantic model or similar structure), making it easier to access specific fields like choices[0].message.content or usage statistics.
• SDKs typically define specific exception types (like OpenAIError) for better error handling related to the API.

Which Approach Should You Use?

• Use requests when:
  • You need maximum control over the HTTP request details.
  • You are interacting with an API that doesn't have a dedicated Python SDK.
  • You prefer fewer dependencies or want to understand the underlying protocol better.
• Use a provider SDK when:
  • One is available for your target LLM provider.
  • You prefer a higher-level, more convenient abstraction.
  • You want built-in handling for authentication, response parsing, and provider-specific features.
  • You are integrating with frameworks (like LangChain) that often build upon these SDKs.

For most application development, using the official provider SDK is recommended as it simplifies development and maintenance. However, understanding how to use requests is valuable for debugging, working with less common APIs, or when you need direct control over the network interaction. Regardless of the method, secure handling of API keys and error handling are essential components of reliable application development.