OpenAI API

OpenAI API#

The OpenAIAPI class provides an interface to interact with OpenAI-compatible LLM APIs. It supports both OpenAI’s official API and third-party services that expose OpenAI-compatible endpoints.

Overview#

The OpenAIAPI class extends the base LLM class and provides a unified interface for making chat completion requests to OpenAI-compatible APIs. It handles API authentication, request formatting, and response parsing.

Setup Requirements#

Before using the OpenAIAPI, ensure you have the following:

  1. Python Package: Install the OpenAI Python package:

    pip install openai

  2. API Access: You need one of the following: - OpenAI API key from platform.openai.com - Third-party service with OpenAI-compatible API (e.g., Azure OpenAI, local proxies)

API Reference#

Class Definition#

class OpenAIAPI#

A concrete implementation of the LLM base class that interfaces with OpenAI-compatible APIs.

Constructor#

__init__(self, base_url: str, api_key: str, model: str, timeout: int = 60, **kwargs)#

Initialize the OpenAI API client.

Parameters:

  • base_url (str) – The base URL of the API endpoint. For OpenAI’s official API, use "https://api.openai.com/v1". For third-party services, use their provided endpoint URL.

  • api_key (str) – Your API key for authentication. For OpenAI, this is your secret API key.

  • model (str) – The model identifier to use (e.g., "gpt-4", "gpt-3.5-turbo").

  • timeout (int, optional) – Request timeout in seconds. Defaults to 60.

  • kwargs (Any, optional) – Additional parameters passed to the openai.OpenAI client constructor. See OpenAI Python SDK documentation for available options.

Note

For Azure OpenAI, the base_url should follow the format: "https://<your-resource-name>.openai.azure.com/openai/deployments/<your-deployment-name>"

Methods#

draw_sample(self, prompt: str | Any, *args, **kwargs) → str#

Generate a response from the LLM based on the provided prompt.

Parameters:

  • prompt (str | Any) – The input prompt. Can be either: - A string containing the user message - A list of message dictionaries with ‘role’ and ‘content’ keys

  • args – Additional positional arguments (unused).

  • kwargs – Additional keyword arguments passed to the API call. See OpenAI’s Chat Completion API for available parameters.

Returns:

The generated text content from the LLM response.

Return type:

str

Raises:

  • openai.APIError – If the API returns an error.

  • openai.APIConnectionError – If there’s a connection issue.

Note

When passing a string prompt, it is automatically wrapped in a user message format: [{'role': 'user', 'content': prompt.strip()}]

Examples#

Example 1: Using OpenAI’s Official API#

from llm4ad.tools.llm.llm_api_openai import OpenAIAPI

# Initialize with OpenAI API credentials
sampler = OpenAIAPI(
    base_url="https://api.openai.com/v1",
    api_key="your-api-key-here",
    model="gpt-4",
    timeout=60
)

# Simple prompt
response = sampler.draw_sample("What is the capital of France?")
print(response)  # Output: Paris

# Using chat message format
messages = [
    {"role": "system", "content": "You are a helpful coding assistant."},
    {"role": "user", "content": "Write a function to calculate factorial."}
]
response = sampler.draw_sample(messages)

Example 2: Using Third-Party OpenAI-Compatible API#

from llm4ad.tools.llm.llm_api_openai import OpenAIAPI

# Using a third-party service (e.g., local proxy or alternative provider)
sampler = OpenAIAPI(
    base_url="https://api.example.com/v1",  # Third-party endpoint
    api_key="your-third-party-api-key",
    model="gpt-3.5-turbo",
    timeout=30
)

response = sampler.draw_sample("Hello, how are you?")

Example 3: Using with Additional API Parameters#

from llm4ad.tools.llm.llm_api_openai import OpenAIAPI

sampler = OpenAIAPI(
    base_url="https://api.openai.com/v1",
    api_key="your-api-key-here",
    model="gpt-4",
    timeout=60
)

# Passing additional parameters via kwargs
response = sampler.draw_sample(
    prompt="Explain quantum computing in simple terms.",
    temperature=0.7,
    max_tokens=500,
    top_p=0.9
)

Example 4: Integration with LLM4AD Platform#

from llm4ad.tools.llm.llm_api_openai import OpenAIAPI
import llm4ad

# Create LLM sampler
sampler = OpenAIAPI(
    base_url="https://api.openai.com/v1",
    api_key="your-api-key-here",
    model="gpt-4"
)

# Use with an LLM4AD method (example with EoH)
task = llm4ad.tasks.optimization.SymReg(  # Example task
    dimension=5,
    num_samples=100,
    eval_budget=1000
)

method = llm4ad.methods.eoh.EoH(
    task=task,
    sampler=sampler,
    num_iterations=50
)

result = method.run()
print(f"Best solution: {result.best_solution}")
print(f"Best fitness: {result.best_fitness}")

Common Issues and Troubleshooting#

  1. Authentication Error: Ensure your API key is correct and has sufficient credits.

  2. Rate Limiting: If you encounter rate limit errors, implement retry logic or reduce request frequency.

  3. Timeout Issues: Increase the timeout parameter if network latency is high.

  4. Model Not Found: Verify that the model name is correct and available for your API key.

See Also#