LLM Interaction Client

Project Description

This project provides a flexible client for interacting with Large Language Models (LLM), designed to facilitate information extraction, response generation, and comprehensive testing.

Key Features

• 🚀 Configurable LLM client
• 🧪 Complete unit test suite
• 🔒 Secure credential management
• 📊 Model performance analysis
• 🌐 Multilingual support

Prerequisites

• Python 3.8+
• Account with an LLM API provider (OpenAI, Anthropic, etc.)

Installation

1. Clone the repository:

git clone https://github.com/seseWho/llm-interaction-client.git
cd llm-interaction-client

2. Create a virtual environment (optional but recommended):

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

3. Install dependencies:

pip install -r requirements.txt

Configuration

1. Create a .env file in the root directory:

LLM_API_KEY=your_api_key_here
LLM_BASE_URL=https://api.example.com/v1
LLM_MODEL=model_name

Basic Usage

Initialize Client

from llm_client import LLMClient
from config import API_KEY, BASE_URL, MODEL_NAME

# Create LLM client
client = LLMClient(API_KEY, BASE_URL, MODEL_NAME)

# Generate response
response = client.generate_response("Hello, how are you?")
print(response)

Run Tests

python test_suite.py

Test Suite Features

The test suite includes:

• Initial connection test
• Response generation
• Response time measurement
• Context length limit
• Multilingual support
• Temperature variation
• JSON generation
• Code generation
• Logical reasoning tests

Project Structure

llm_interaction/
│
├── src/
│   ├── llm_client.py       # Main LLM client
│   ├── test_suite.py       # Test battery
│   └── config.py           # Configurations and constants
│
├── tests/                  # Additional tests
│   └── test_llm_client.py  
│
├── main.py                 # Program entry point
└── requirements.txt        # Project dependencies

Customization

• Modify config.py to change configurations
• Adjust llm_client.py to adapt to different APIs
• Extend test_suite.py with specific use case tests

Security Considerations

• Never commit your API credentials
• Use environment variables
• Limit access to configuration files

Contributions

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

License

Distributed under the MIT License. See LICENSE for more information.

Contact

Your Name - sergio.sesena@gmail.com

Project Link: https://github.com/seseWho/llm-interaction-client

Acknowledgments

• Requests
• python-dotenv

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Note: This project is under continuous development. Contributions and suggestions are welcome.