rag-tutorial-v2

This repository is a starter guide for learning how to use Retrieval-Augmented Generation (RAG) with Ollama. Follow the steps below to set up and run the project.

Prerequisites

Ensure you have the following installed:

• Python 3.8 or higher
• pip (Python package installer)

Installation

1. Clone the repository:

   git clone <repository-url>
   cd rag-tutorial-v2

2. Install the required Python packages:

   pip install -r requirements.txt

Setting Up the Database

1. Place your PDF documents in the data directory.

2. Populate the database with the documents:

   python populate_database.py --reset

Querying the Database

To query the database, run the query_data.py script with your query text:

python query_data.py "Your query text here"

Project Structure

• __pycache__/: Directory containing Python bytecode files.
• .gitignore: Git ignore file to specify untracked files to ignore.
• chroma/: Directory containing the Chroma database files.
  • 77943a61-1aef-45fe-af55-a29fb829e25b/: Subdirectory for Chroma database.
  • chroma.sqlite3: SQLite database file for Chroma.
• data/: Directory to store PDF documents.
• get_embedding_function.py: Script to get the embedding function.
• populate_database.py: Script to populate the Chroma database with documents.
• query_data.py: Script to query the Chroma database.
• README.md: This README file.
• requirements.txt: List of required Python packages.
• test_rag.py: Script to run tests on the RAG setup.