abstractllm 0.1.0

A Python library for abstracting LLM interactions

AbstractLLM

A lightweight, unified interface for interacting with multiple Large Language Model providers.

[THIS IS A WORK IN PROGRESS, STAY TUNED !]

Features

• 🔄 Unified API: Consistent interface for OpenAI, Anthropic, Ollama, and Hugging Face models
• 🔌 Provider Agnostic: Switch between providers with minimal code changes
• 🎛️ Configurable: Flexible configuration at initialization or per-request
• 📝 System Prompts: Standardized handling of system prompts across providers
• 📊 Capabilities Inspection: Query models for their capabilities
• 📝 Logging: Built-in request and response logging

Installation

# Basic installation
pip install abstractllm

# With provider-specific dependencies
pip install abstractllm[openai]
pip install abstractllm[anthropic]
pip install abstractllm[huggingface]

# All dependencies
pip install abstractllm[all]

Quick Start

from abstractllm import create_llm

# Create an LLM instance
llm = create_llm("openai", api_key="your-api-key")

# Generate a response
response = llm.generate("Explain quantum computing in simple terms.")
print(response)

Supported Providers

OpenAI

llm = create_llm("openai", 
                api_key="your-api-key",
                model="gpt-4")

Anthropic

llm = create_llm("anthropic", 
                api_key="your-api-key",
                model="claude-3-opus-20240229")

Ollama

llm = create_llm("ollama", 
                base_url="http://localhost:11434",
                model="llama2")

Hugging Face

llm = create_llm("huggingface", 
                model="google/gemma-7b")

Configuration

You can configure the LLM's behavior in several ways:

# At initialization
llm = create_llm("openai", temperature=0.7, system_prompt="You are a helpful assistant.")

# Update later
llm.set_config({"temperature": 0.5})

# Per-request
response = llm.generate("Hello", temperature=0.9)

System Prompts

System prompts help shape the model's personality and behavior:

llm = create_llm("openai", system_prompt="You are a helpful scientific assistant.")

# Or for a specific request
response = llm.generate("What is quantum entanglement?", 
                     system_prompt="You are a physics professor explaining to a high school student.")

Capabilities

Check what capabilities a provider supports:

capabilities = llm.get_capabilities()
print(capabilities)
# Example: {'streaming': True, 'max_tokens': 4096, 'supports_system_prompt': True}

Logging

AbstractLLM includes built-in logging:

import logging
from abstractllm.utils.logging import setup_logging

# Set up logging with desired level
setup_logging(level=logging.DEBUG)

Advanced Usage

See the Usage Guide for advanced usage patterns, including:

• Using multiple providers
• Implementing fallback chains
• Error handling
• And more

Contributing

Contributions are welcome! Read more about how to contribute in the CONTRIBUTING file. Please feel free to submit a Pull Request.

License

This project is licensed under the MIT License - see the LICENSE file for details.