RAG Financial Risk Assessment

RAG-based Financial Risk Assessment Tool

Overview

The RAG-based Financial Risk Assessment Tool is designed to leverage Retrieval-Augmented Generation (RAG) techniques to assess financial risk using advanced AI models. This project aims to provide insights into financial data and assist in risk assessment through an automated pipeline.

Project Structure

• src/: Contains the main source code files.

  • __init__.py: Initialization file for the src module.
  • retriever.py: Implements data retrieval using a retriever model.
  • generator.py: Implements text generation using a generator model.
  • main.py: Main script to run the RAG pipeline.
  • config.py: Configuration settings for the project.
  • utils/: Utility functions and helpers.
    • data_processing.py: Data processing and cleaning functions.
    • model_utils.py: Helper functions for model operations.
    • logging_utils.py: Logging functions for debugging.
  • tests/: Contains unit and integration tests.
    • test_retriever.py: Unit tests for the retriever module.
    • test_generator.py: Unit tests for the generator module.
    • test_main.py: Tests for the main pipeline.
  • pipelines/: Custom pipelines for complex workflows.
    • risk_assessment_pipeline.py: Pipeline specific to financial risk assessment.

• data/: Data storage and management.

  • raw/: Raw datasets.
  • processed/: Processed data ready for analysis.

• config/: Configuration files.

  • default_config.yaml: General configuration for the project.
  • logging_config.yaml: Logging configuration.
  • pipeline_config.yaml: Pipeline-specific configurations.

• logs/: Logs related to the project.

• notebooks/: Jupyter Notebooks for experimentation and analysis.

  • RAG_pipeline_demo.ipynb: Demonstration of the RAG pipeline.
  • EDA.ipynb: Exploratory Data Analysis (EDA) notebook.

Installation

1. Clone the repository:

   git clone <repository_url>
   cd RAG-Financial-Risk-Assessment

2. Create a virtual environment and activate it:

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

3. Install the required dependencies:

   pip install -r requirements.txt

4. Set up environment variables (if needed):

   export OPENAI_API_KEY=<your_openai_api_key>

Usage

1. Run the RAG Pipeline:

   python src/main.py

   This will execute the RAG pipeline for financial risk assessment.

2. Demo Notebook:

   Open and run notebooks/RAG_pipeline_demo.ipynb in a Jupyter Notebook environment to see a demonstration of the RAG pipeline.

3. Exploratory Data Analysis (EDA):

   Explore the dataset and perform EDA using notebooks/EDA.ipynb.

Configuration

The configuration files are located in the config/ directory:

• default_config.yaml: General settings and model configurations.
• logging_config.yaml: Settings for logging and debugging.
• pipeline_config.yaml: Specific configurations for the RAG pipeline.

Testing

To run the unit and integration tests, use:

pytest src/tests/

Contribution

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

License

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

Contact

For any questions or support, please contact [email protected].