jpcadena/autochain-bot

This project is dedicated to building an intelligent data processing pipeline using AutoChain LLM and BERT ML.

autochain-bot

Autochain bot

Autochain bot
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Table of Contents

1. About The Project
   • Built With
2. Getting Started
   • Prerequisites
   • Installation
3. Usage
4. Testing
5. Contributing
6. Security
7. Code of Conduct
8. License
9. Contact

About The Project

This project is dedicated to building an intelligent data processing pipeline, integrating state-of-the-art machine learning models like BERT and AutoChain. The pipeline encompasses various stages including data preparation, engineering, analysis, and configuration of AutoChain, forming a comprehensive and robust data analytics solution.

The project involves several key phases:

Data Preparation: Extracting and loading the raw data, followed by cleaning, and preprocessing. Data Engineering: Features are extracted and transformed for better insights and model compatibility. Data Analysis: Including both numerical analysis and visual analytics for in-depth data understanding. Feature Engineering: Selecting and crafting the best attributes that will enhance the modeling. Model Integration: Utilizing the BERT model for sequence processing and AutoChain for automating the machine learning pipeline. The implemented system facilitates both exploratory data analysis (EDA) and predictive modeling, aligning with clean code principles, design patterns, and SOLID principles. Its modular architecture enables easy scalability and maintainability.

Designed for efficiency and reliability, this project combines cutting-edge technologies and best practices to provide a powerful tool for data scientists, analysts, and organizations seeking to derive actionable insights from complex data sets. Its multifaceted nature makes it adaptable to various domains and data types, showcasing the versatility and innovation at the heart of the solution

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Built with

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Getting started

Prerequisites

• Python 3.11

Installation

1. Clone the repository

   git clone https://github.com/jpcadena/autochain-bot.git
   

2. Change the directory to root project

   cd autochain-bot
   

3. Create a virtual environment venv

   python3 -m venv venv
   

4. Activate environment in Windows

   .\venv\Scripts\activate
   

5. Install requirements with PIP

   pip install -r requirements.txt
   pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
   

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Usage

1. Setting up environment variables:

   If you find a .env.sample in the project directory, make a copy of it and rename to .env.

   cp .env.sample .env
   

   This .env file will be used to manage your application's environment variables.

2. Configuring your credentials:

   Open the .env file in a text editor and replace the placeholder values with your actual credentials.

   # .env file
   POSTGRES_USER=your_database_user
   SECRET_KEY=your_api_key
   

   Be sure to save the file after making these changes.

3. Executing the main script:

   To start the local project on your machine, run the following command in your terminal:

   python main.py
   

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Testing

1. Running tests:

   To run all tests, you can simply run the following command in the root directory of the project:

   pytest
   

2. Running a specific test:

   If you want to run a specific test, you can do so by specifying the file and test name. For example, the following command will only run the test_get_users test in the test_main.py file:

   pytest tests/test_main.py::test_get_users
   

3. Understanding test results:

   Pytest will provide a summary of the test results in the console. It will tell you how many tests passed and how many failed. For each failed test, Pytest will provide a detailed error message that can help you identify what went wrong.

4. Writing new tests:

   When you add new features to the application, you should also write corresponding test cases. Each test case should be a function that starts with the word 'test'. Inside the function, you can use assert statements to check that your code is working as expected. For example:

   def test_add_user():
       user = add_user("testuser", "testpass")
       assert user.name == "testuser"
       assert user.password == "testpass"

   This function tests that the add_user function correctly creates a new user with the given name and password.

Remember to update your tests whenever you update your code. Maintaining a comprehensive test suite will help ensure the reliability and robustness of your application.

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Contributing

Please read our contributing guide for details on our code of conduct, and the process for submitting pull requests to us.

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Security

For security considerations and best practices, please refer to our Security Guide for a detailed guide.

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Code of Conduct

We enforce a code of conduct for all maintainers and contributors. Please read our Code of Conduct to understand the expectations before making any contributions.

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License

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

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Contact

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