Apertis Docs
Apertis Docs

SDKs & Libraries

Chat Completions

Generate text responses using chat-based models with the familiar OpenAI-compatible interface.

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Prerequisites

pip install apertis

Get your API Key from Apertis

Basic Usage

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1-mini",
        messages=[
            {"role": "user", "content": "Explain quantum computing in simple terms."}
        ]
    )

    print(response.choices[0].message.content)

if __name__ == "__main__":
    main()

Multi-Turn Conversations

from apertis import Apertis

def main():
    client = Apertis()

    messages = [
        {"role": "system", "content": "You are a helpful Python tutor."},
        {"role": "user", "content": "What is a list comprehension?"},
    ]

    response = client.chat.completions.create(
        model="claude-sonnet-4.5",
        messages=messages
    )

    print("Assistant:", response.choices[0].message.content)

    # Continue the conversation
    messages.append({"role": "assistant", "content": response.choices[0].message.content})
    messages.append({"role": "user", "content": "Can you show me an example?"})

    response = client.chat.completions.create(
        model="claude-sonnet-4.5",
        messages=messages
    )

    print("\nAssistant:", response.choices[0].message.content)

if __name__ == "__main__":
    main()

Configuration Options

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {"role": "user", "content": "Write a creative story opening."}
        ],
        temperature=0.9,      # Higher = more creative (0.0 - 2.0)
        max_tokens=500,       # Maximum response length
        top_p=0.95,           # Nucleus sampling
        frequency_penalty=0.5, # Reduce repetition
        presence_penalty=0.5,  # Encourage new topics
    )

    print(response.choices[0].message.content)

if __name__ == "__main__":
    main()

Response Format

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1-mini",
        messages=[
            {"role": "user", "content": "Hello!"}
        ]
    )

    # Access response data
    print(f"Model: {response.model}")
    print(f"Content: {response.choices[0].message.content}")
    print(f"Role: {response.choices[0].message.role}")
    print(f"Finish Reason: {response.choices[0].finish_reason}")

    # Token usage
    print(f"\nPrompt Tokens: {response.usage.prompt_tokens}")
    print(f"Completion Tokens: {response.usage.completion_tokens}")
    print(f"Total Tokens: {response.usage.total_tokens}")

if __name__ == "__main__":
    main()

Context Compression

Reduce token usage for long conversations by enabling context compression:

from apertis import Apertis

def main():
    client = Apertis()

    response = client.chat.completions.create(
        model="gpt-4.1",
        messages=[
            {"role": "user", "content": "Explain distributed systems"},
            {"role": "assistant", "content": "Distributed systems are..."},
            # ... many turns of conversation history ...
            {"role": "user", "content": "Summarize the key points"}
        ],
        extra_body={
            "compression": {
                "enabled": True,
                "strategy": "on",
                "model": "gpt-4.1-mini"
            }
        }
    )

    print(response.choices[0].message.content)

if __name__ == "__main__":
    main()

Supported Models

All chat-capable models are supported, including:

Provider Models
OpenAI gpt-4.1, gpt-4.1-mini, gpt-4.1, o1, o3-mini
Anthropic claude-sonnet-4.5, claude-opus-4-5-20251101, claude-haiku-4-5-20250501
Google gemini-3-pro-preview, gemini-2.5-flash
DeepSeek deepseek-chat, deepseek-reasoner
xAI grok-3, grok-3-fast

View all models →

API Reference

Parameter Type Description
model str Model identifier (required)
messages list Conversation messages (required)
temperature float Sampling temperature (0.0 - 2.0)
max_tokens int Maximum tokens to generate
top_p float Nucleus sampling parameter
frequency_penalty float Repetition penalty (-2.0 - 2.0)
presence_penalty float Topic diversity penalty (-2.0 - 2.0)
stop list[str] Stop sequences
n int Number of completions to generate