RAG for personal Google Drive data

This repository demonstrates Retrieval Augmented Generation (RAG) using data retrieved from Google Drive.

Getting Started

Follow these steps to set up and run the demonstration:

1. Environment Setup:

• Create a Conda environment (recommended):

  conda create -n rag-gdrive python=3.9  # Or your preferred Python version
  conda activate rag-gdrive

• Install dependencies:

  pip install -r requirements.txt

2. Google API Configuration:

• Enable the Google Drive API:
  • Go to the Google Cloud Console: console.cloud.google.com
  • Create or select a project.
  • Search for and enable the "Google Drive API".
• Create Service Account Credentials:
  • In the Google Cloud Console, navigate to "IAM & Admin" -> "Service Accounts".
  • Create a new service account.
  • Grant the service account appropriate roles (e.g., "Viewer" for read-only access to Google Drive).
  • Create a JSON key file (credentials.json) for the service account and download it.
  • Place credentials.json:
  • Place the credentials.json file in the same directory as main.py (or adjust the code to point to the correct path).

3. Running the Application:

• Execute the main script:

  python main.py

Repository Contents:

• main.py: The main Python script that implements the RAG pipeline.
• requirements.txt: A list of Python dependencies.
• credentials.json: (You will create this) The Google Drive API credentials.
• (Potentially) other supporting files for data processing or model handling.

Notes:

• Replace env_name with your desired Conda environment name.
• Ensure that the Google Drive files you intend to use are accessible by the service account.
• Adjust the python version in the conda create command if needed.
• Consider adding a .gitignore file to prevent commiting the credentials.json file.
• Add clear instructions on how to structure the google drive files that are to be used.