awesome-llm-wrapper 0.1.2

A unified wrapper for multiple LLM APIs including OpenAI, Gemini, and Anthropic

LLM Wrapper

A unified Python library for interacting with multiple Large Language Model APIs including OpenAI, Google Gemini, and Anthropic. This wrapper provides a consistent interface across different LLM providers, simplifying integration and allowing easy switching between models.

Features

• Single interface for multiple LLM providers (OpenAI, Google Gemini, Anthropic)
• Simple configuration with API keys or environment variables
• Standardized request and response formats across providers
• Comprehensive error handling with detailed contextual messages
• Automatic rate limit handling with exponential backoff
• Full streaming support for real-time responses
• Proper system message handling for all providers
• Input validation and type checking for robust operation
• Detailed usage metrics for token consumption
• Consistent default parameters across all providers
• Consistent parameter handling across providers
• Built-in error handling with retries for rate limiting
• Streaming support for efficient token generation
• Simplified message handling for both completions and chat

Installation

pip install awesome-llm-wrapper

Quick Start

from llm_wrapper import LLMClient

# Initialize with your API keys
client = LLMClient(
    openai_api_key="your-openai-key",
    gemini_api_key="your-gemini-key",
    anthropic_api_key="your-anthropic-key"
)

# Or load from environment variables
client = LLMClient.from_env()

# Make a simple completion request
response = client.complete(
    provider="openai",  # or "gemini" or "anthropic"
    prompt="Tell me a joke about programming",
    model="gpt-4o-mini",  # optional: specify a model (defaults to provider's DEFAULT_MODEL if not specified)
    max_tokens=100
)

# Access the generated text
print(response.text)

# Access additional information
print(f"Model used: {response.model}")
print(f"Provider: {response.provider}")
print(f"Token usage: {response.usage}")

Advanced Usage

Chat Completions

# Chat completion with conversation history
response = client.chat(
    provider="anthropic",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is the capital of France?"},
        {"role": "assistant", "content": "The capital of France is Paris."},
        {"role": "user", "content": "What is its population?"}
    ],
    model="claude-3-5-sonnet",  # optional: specify a model (defaults to provider's DEFAULT_MODEL if not specified)
    temperature=0.7,
    max_tokens=150
)

print(response.text)

Streaming Responses

# Streaming response for real-time output
for chunk in client.stream_chat(
    provider="openai",
    messages=[
        {"role": "user", "content": "Write a poem about AI"}
    ],
    model="gpt-4o-mini",  # optional: specify a model (defaults to provider's DEFAULT_MODEL if not specified)
    temperature=0.9
):
    print(chunk.text, end="")

Using Message Objects

from llm_wrapper import LLMClient, Message

# Using Message objects instead of dictionaries
response = client.chat(
    provider="gemini",
    messages=[
        Message(role="system", content="You are a helpful assistant."),
        Message(role="user", content="Explain quantum computing in simple terms.")
    ],
    model="gemini-2.0-flash"  # optional: specify a model
)

System Message Handling

Each provider handles system messages differently, but LLM Wrapper normalizes this for you:

# System messages are properly formatted for each provider
response = client.chat(
    provider="anthropic",  # Works the same for OpenAI and Gemini
    messages=[
        {"role": "system", "content": "You are a helpful assistant who always responds in rhyme."},
        {"role": "user", "content": "Tell me about machine learning."}
    ]
)

Behind the scenes:

• OpenAI: System messages are passed directly as "system" role messages
• Anthropic: System messages are formatted with <system>...</system> tags
• Gemini: System messages are prepended to the first user message

Model Selection

Each provider has a default model, but you can specify a different model using the model parameter:

# Using specific models for each provider

# OpenAI (default: gpt-4o-mini)
response = client.complete(
    provider="openai",
    prompt="Generate a story",
    model="gpt-4",  # override the default model
    max_tokens=100
)

# Gemini (default: gemini-2.0-flash)
response = client.complete(
    provider="gemini",
    prompt="Generate a story",
    model="gemini-2.0-pro",
    max_tokens=100
)

# Anthropic (default: claude-3-5-sonnet)
response = client.complete(
    provider="anthropic",
    prompt="Generate a story",
    model="claude-3-opus",
    max_tokens=100
)

Error Handling and Rate Limiting

The library provides comprehensive error handling, including automatic retries for rate limit errors:

from llm_wrapper import LLMClient

try:
    response = client.complete(
        provider="openai",
        prompt="Generate a story",
        max_tokens=100
    )
    print(response.text)
except ValueError as e:
    print(f"Configuration error: {e}")  # Invalid API keys, missing parameters, etc.
except requests.HTTPError as e:
    print(f"HTTP error: {e}")  # API errors with detailed context
except Exception as e:
    print(f"Other error: {e}")  # Any other errors that might occur

Rate limit handling is built-in:

• Automatic retries with exponential backoff
• Respects the Retry-After header when provided by the API
• Detailed error messages if all retries fail

Configuration

Environment Variables

You can configure the library using environment variables:

OPENAI_API_KEY=your-openai-key
GEMINI_API_KEY=your-gemini-key
ANTHROPIC_API_KEY=your-anthropic-key

If no valid API keys are found, the library will raise a clear error message.

Available Models

Each provider has a default model that will be used if no model is specified:

Provider	Default Model	Other Available Models
OpenAI	gpt-4o-mini	gpt-4, gpt-4-turbo, gpt-3.5-turbo, etc.
Gemini	gemini-2.0-flash	gemini-2.0-pro, gemini-1.5-pro, etc.
Anthropic	claude-3-5-sonnet	claude-3-opus, claude-3-haiku, etc.

You can specify any model supported by the provider's API using the model parameter.

Consistent Default Parameters

All providers use consistent default parameters:

• max_tokens: 1000 (for all providers)
• Other parameters are passed directly to the underlying API

Response Object

The Response object provides access to:

• text: The generated text content
• provider: The provider used (OpenAI, Gemini, or Anthropic)
• model: The specific model used
• raw_response: The full raw response from the provider
• usage: Token usage statistics
• finish_reason: The reason why the generation stopped

Parameter Handling

The library intelligently handles parameter conversion between different providers:

Common Parameters (automatically converted)

Parameter	OpenAI	Gemini	Anthropic
max_tokens	max_tokens	maxOutputTokens	max_tokens
temperature	temperature	temperature	temperature
top_p	top_p	topP	top_p
top_k	N/A	topK	N/A

You can use the snake_case (Python style) parameter names consistently across all providers, and the library will automatically convert them to the appropriate format for each API:

# This works for all providers
response = client.complete(
    provider="gemini",  # or "openai" or "anthropic" 
    prompt="Tell me a joke",
    max_tokens=100,
    temperature=0.7,
    top_p=0.9,
    top_k=40  # only used by Gemini, ignored by others
)

Provider-Specific Parameters

You can also pass provider-specific parameters directly:

# OpenAI-specific
response = client.complete(
    provider="openai",
    prompt="Tell me a joke",
    frequency_penalty=0.5,  # OpenAI-specific
    presence_penalty=0.2    # OpenAI-specific
)

# Gemini-specific
response = client.complete(
    provider="gemini",
    prompt="Tell me a joke",
    candidateCount=2,  # Gemini-specific
    stopSequences=["The end"]  # Gemini-specific
)

# Anthropic-specific
response = client.complete(
    provider="anthropic",
    prompt="Tell me a joke",
    stop_sequences=["The end"]  # Anthropic-specific
)

Development

Setting Up the Development Environment

1. Clone the repository:

   git clone https://github.com/yourusername/llm-wrapper.git
   cd llm-wrapper
   

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`
   

3. Install development dependencies:

   pip install -e .[dev]
   

Running Tests

Run the test suite with:

pytest tests/

Project Structure

• src/llm_wrapper/: Main package directory
  • client.py: Main client interface
  • models.py: Data models and response objects
  • providers/: Provider implementations
    • base.py: Base provider class
    • openai.py: OpenAI provider
    • gemini.py: Google Gemini provider
    • anthropic.py: Anthropic provider

Adding New Providers

1. Create a new provider file in src/llm_wrapper/providers/ (e.g., new_provider.py)
2. Implement the provider class inheriting from BaseProvider
3. Add the provider to the Provider enum in models.py
4. Update the LLMClient class in client.py to support the new provider
5. Add tests for the new provider in tests/

Contributing Guidelines

• Follow PEP 8 style guidelines
• Write comprehensive docstrings for all public methods
• Include unit tests for new features
• Update documentation (README.md) when adding new features
• Use descriptive commit messages

License

MIT