This application automates the analysis of purchase data by integrating various machine learning models and simulating analyst roles to process and categorize purchase information efficiently. It leverages a combination of advanced language models and custom logic to analyze, categorize, and review purchase data, streamlining the decision-making process for businesses.
- Model Integration: Utilizes several large language models, including OpenAI, Gemini, Mixtral, Sonnet, and Opus, each configured with specific parameters to best suit the analysis needs.
- Role-Based Analysis: Simulates two types of analyst roles, senior and junior, each with specific goals, backstories, and tools, to mimic a real-world analysis team.
- Automated Tasks: Automates the categorization and review of purchase data, allowing for scalable and efficient data processing.
- Sequential Task Execution: Configures tasks to be executed in a sequential manner, ensuring a logical flow of data processing and analysis.
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Clone the Repository: Clone this repository to your local machine using
git clone. -
Install Dependencies: Navigate to the cloned directory and install the required Python packages using
pip install -r requirements.txt. -
Configuration: Configure the
.envfile with necessary API keys and model parameters.
To run the application, navigate to the application directory and execute the main script:
python main.py
Ensure that you have configured the application properly, including setting up the .env file and adjusting any model parameters as needed.
Contributions are welcome! Please feel free to submit pull requests or open issues to suggest improvements or add new features.
This project is licensed under the MIT License - see the LICENSE file for details.