Python RAG Applications using Ollama

Welcome to the Python RAG Apps using Ollama repository! This project showcases various applications of Retrieval-Augmented Generation (RAG) using the Ollama framework.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Examples
• Contributing
• License
• Contact

Introduction

This repository contains a collection of Python applications that leverage the power of Retrieval-Augmented Generation (RAG) using the Ollama framework. RAG combines the strengths of retrieval-based and generation-based models to provide more accurate and contextually relevant responses.

Features

• High Accuracy: Combines retrieval and generation for precise results.
• Scalability: Easily scalable to handle large datasets.
• Flexibility: Supports various use cases including chatbots, Q&A systems, and more.
• Integration: Seamlessly integrates with existing Python projects.

Installation

To get started, clone the repository and install the required dependencies:

git clone https://github.com/yourusername/python-rag-apps-using-ollama.git
cd python-rag-apps-using-ollama
pip install -r requirements.txt

Usage

Here's a basic example of how to use the RAG model in your application:

from ollama import RAGModel

# Initialize the model
model = RAGModel()

# Example query
query = "What is the capital of France?"

# Get the response
response = model.generate(query)
print(response)

Examples

Check out the examples directory for more detailed use cases and applications.

Contributing

We welcome contributions! Please read our Contributing Guidelines for more details.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

For any questions or suggestions, feel free to open an issue or contact us at your-email@example.com.