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Project Description

Installation Instructions

To install this project, follow these steps:

  1. Clone the repository using https://github.com/chintanrabadiya/Learning_RAG.git
  2. Navigate into the project directory using cd project-name
  3. Install the required dependencies using pip install -r requirements.txt
  4. Create a virtual environment using python -m venv .venv (on Windows) or python3 -m venv .venv (on Linux/Mac)
  5. Activate the virtual environment using source .venv/bin/activate (on Linux/Mac) or . .venv\Scripts\activate (on Windows)

Usage Guidelines

To use this project, follow these steps:

  1. Run the application using python app.py
  2. Follow the prompts to complete the task
  3. Refer to the documentation for more information on usage guidelines

Adding a .env File

To add a .env file, create a new file named .env in the root directory of your project. Add your environment variables to this file, one per line, in the format VARIABLE_NAME=value.

Example:

API_KEY=your-api-key
SECRET_KEY=your-secret-key

Adding a .venv Virtual Environment

To add a .venv virtual environment, navigate into the project directory and run python -m venv .venv (on Windows) or python3 -m venv .venv (on Linux/Mac). Activate the virtual environment using source .venv/bin/activate (on Linux/Mac) or . .venv\Scripts\activate (on Windows).

I hope this helps! Let me know if you need any further assistance.

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It has basic data to make model of RAG using Ollama lllm.

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