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‎README.md‎

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# BreakYourLLM
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[![Python Version](https://img.shields.io/badge/python-3.8%2B-blue.svg)](https://www.python.org/downloads/release/python-380/)
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[![Waitlist Form](https://img.shields.io/badge/Form-Click_here-blue)](https://example.com/form)
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[![Website](https://img.shields.io/badge/Website-Click_here-green)](https://example.com/website)
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## Test & Simulate your Production LLMs
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**Break Your LLM before your users do! With our framework, you can build a comprehensive suite of tests that rigorously test your LLM against your guidelines.**
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## Overview
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This is a comprehensive framework designed for testing and evaluating language models, particularly in the field of Natural Language Processing (NLP). This toolset provides functionalities for running tests, calculating performance metrics, and visualizing results to assess the quality of language models.
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![Overview Image](docs/images/overview.png)
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## Table of Contents
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- [Features](#features)
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- [Technologies](#technologies)
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- [Installation](#installation)
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- [Usage](#usage)
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- [Codebase Organization](#codebase-organization)
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- [Contributing](#contributing)
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- [License](#license)
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## Features
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- **Model Testing**: Execute tests on various language models.
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- **Metrics Calculation**: Calculate metrics such as accuracy, hallucination rate, and more.
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- **Result Visualization**: Visualize test results for better analysis.
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- **Configurable**: Easily configure settings through YAML and environment files.
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![Features Image](docs/images/features.png)
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## Technologies
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- **Languages**: Python
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- **Frameworks/Libraries**:
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- OpenAI
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- Pandas
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- NumPy
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- PyYAML
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- Requests
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- **Tools**:
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- Pydantic
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- Python-dotenv
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## Installation
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To get started with this project, follow these steps:
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1. **Clone the repository**:
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```bash
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git clone https://github.com/yourusername/BreakYourLLM.git
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cd BreakYourLLM
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2. **Install the required dependencies**:
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```bash
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pip install -r requirements.txt
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```
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3. **Configure your environment**:
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```bash
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Create a .env file based on the provided .env.example and fill in the necessary variables.
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```
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## Usage
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To execute tests and evaluate models, run the following command:
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```bash
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python sources/execute_tests.py
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```
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Refer to the documentation in the sources/ directory for more detailed usage instructions.
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## Codebase Organization
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The project is organized as follows:
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```bash
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BreakYourLLM/
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│
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├── config/ # Configuration files
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│ ├── .env # Environment variables
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│ └── config.yaml # Configuration settings
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│
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├── sources/ # Source code
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│ ├── execute_tests.py # Script for executing tests
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│ ├── full_pipeline.py # Full testing pipeline script
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│ ├── helpers/ # Helper scripts
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│ ├── metrics/ # Metrics calculation modules
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│ ├── models/ # Model-related modules
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│ └── views/ # Result visualization modules
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│
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└── requirements.txt # Project dependencies
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```
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## Contributing
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We welcome contributions! Please follow these steps:
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1. Fork the repository.
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2. Create a new branch (git checkout -b feature/YourFeature).
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3. Make your changes and commit them (git commit -m 'Add some feature').
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4. Push to the branch (git push origin feature/YourFeature).
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5. Open a pull request.

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‎readme_1.md‎

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Based on the directory structure and the contents of the workspace, here is an analysis of what this project does:
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1. **Project Type**:
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- This project appears to be a tool or a set of scripts used for testing and evaluating models, possibly related to Natural Language Processing (NLP) tasks.
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2. **Purpose**:
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- The project seems to be focused on testing and evaluating the performance of language models (LLMs) or similar models. It likely provides functionalities to run tests, evaluate metrics, and analyze results to assess the quality and performance of these models.
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3. **Main Technologies**:# Project Analysis and Documentation
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## Project Overview
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This project is a toolset for testing and evaluating language models, with a focus on metrics calculation and result visualization.
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### Project Type
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* The project appears to be a tool or a set of scripts used for testing and evaluating models, possibly related to Natural Language Processing (NLP) tasks.
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### Purpose
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* The project seems to be focused on testing and evaluating the performance of language models (LLMs) or similar models.
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* It likely provides functionalities to run tests, evaluate metrics, and analyze results to assess the quality and performance of these models.
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### Main Technologies
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* **Languages**: Python
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* **Frameworks/Libraries**: OpenAI, Pandas, NumPy, PyYAML, Requests, among others
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* **Tools**: Pydantic, Python-dotenv
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### Codebase Organization
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* **config/**: Contains configuration files like `.env` and `config.yaml`.
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* **sources/**:
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* **execute_tests.py**: Script for executing tests.
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* **full_pipeline.py**: Possibly a script for running a full testing pipeline.
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* **helpers/**: Contains helper scripts for interacting with OpenAI, paraphrasing, and evaluating test cases.
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* **metrics/**: Contains modules for calculating various metrics like accuracy, hallucination rate, etc.
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* **models/**: Contains modules related to model metadata, unit tests, and results.
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* **views/**: Contains modules related to displaying views of test results.
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### Dependencies
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* The `requirements.txt` file lists all the dependencies required by the project, including libraries for handling HTTP requests, data manipulation, and model evaluation.
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### Conclusion
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This workspace seems to be a toolset for testing and evaluating language models, with a focus on metrics calculation and result visualization. It likely serves as a comprehensive solution for assessing the performance of models in NLP tasks.
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- **Languages**: Python
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- **Frameworks/Libraries**: OpenAI, Pandas, NumPy, PyYAML, Requests, among others
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- **Tools**: Pydantic, Python-dotenv
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4. **Codebase Organization**:
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- **config/**: Contains configuration files like `.env` and `config.yaml`.
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- **sources/**:
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- **execute_tests.py**: Script for executing tests.
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- **full_pipeline.py**: Possibly a script for running a full testing pipeline.
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- **helpers/**: Contains helper scripts for interacting with OpenAI, paraphrasing, and evaluating test cases.
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- **metrics/**: Contains modules for calculating various metrics like accuracy, hallucination rate, etc.
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- **models/**: Contains modules related to model metadata, unit tests, and results.
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- **views/**: Contains modules related to displaying views of test results.
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5. **Dependencies**:
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- The `requirements.txt` file lists all the dependencies required by the project, including libraries for handling HTTP requests, data manipulation, and model evaluation.
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In conclusion, this workspace seems to be a toolset for testing and evaluating language models, with a focus on metrics calculation and result visualization. It likely serves as a comprehensive solution for assessing the performance of models in NLP tasks.
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# The user is viewing line 1 of the Untitled-1 file, which is in the markdown language.
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```
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```
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# The user is on a macOS machine.
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# The last command and its output in the terminal is: `
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(base) ashrya@Mr-AA-MacBook BreakYourLLM
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(base) ashrya@Mr-AA-MacBook BreakYourLLM %
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`
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# The current project is a git repository on branch: main
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