Universal LLM service supporting OpenAI, Anthropic, and local models for code analysis and summarization
Project description
Universal LLM Service
Universal LLM service supporting OpenAI, Anthropic, and local models for code analysis and summarization.
Features
- Multi-Provider Support: OpenAI, Anthropic, Azure OpenAI, Google, and local models
- Async Operations: Full async/await support for all LLM operations
- Smart Configuration: Automatic provider detection and configuration
- Response Validation: Built-in response quality validation
- Rate Limiting: Intelligent rate limiting and retry mechanisms
- Type Safety: Complete type annotations with mypy support
- Extensible Design: Easy to add new LLM providers
Installation
# Basic installation
pip install universal-llm-service
# With OpenAI support
pip install universal-llm-service[openai]
# With Anthropic support
pip install universal-llm-service[anthropic]
# With all providers
pip install universal-llm-service[all]
Quick Start
import asyncio
from universal_llm_service import LLMService, LLMInput, LLMProvider, ModelConfig
async def main():
# Initialize service
service = LLMService()
# Create input
input_data = LLMInput(
prompt="Analyze this code and provide insights",
llm_provider=LLMProvider.OPENAI,
model_config=ModelConfig(model="gpt-4", temperature=0.3)
)
# Generate summary
result = await service.generate_summary(input_data)
print(f"Summary: {result.summary}")
print(f"Confidence: {result.confidence_score}")
print(f"Tokens used: {result.metadata.total_tokens}")
asyncio.run(main())
Configuration
Set your API keys as environment variables:
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
Supported Providers
- OpenAI: GPT-3.5, GPT-4, and newer models
- Anthropic: Claude 3 family (Haiku, Sonnet, Opus)
- Azure OpenAI: Enterprise OpenAI deployment
- Google: Gemini and PaLM models
- Local: Custom local model integration
License
MIT License
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