https://github.com/rvats20/python-ollama

Python-llama Agents, LLM-Rag-Application, Aenerative-AI, Machine-Learning. Model Training, Implementing various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. Model Evaluation: Assessing model performance
https://github.com/rvats20/python-ollama

deep-learning deep-reinforcement-learning generative-adversarial-network generative-ai generative-ai-projects gpt langchain-python llama3 llm machine-learning python3 rag transformer

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Python-llama Agents, LLM-Rag-Application, Aenerative-AI, Machine-Learning. Model Training, Implementing various machine learning algorithms such as Logistic Regression, Decision Trees, Random Forests, and Gradient Boosting. Model Evaluation: Assessing model performance

• Host: GitHub
• URL: https://github.com/rvats20/python-ollama
• Owner: rvats20
• Created: 2024-10-22T07:43:09.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-11-13T16:42:39.000Z (3 months ago)
• Last Synced: 2024-12-13T13:16:52.281Z (about 2 months ago)
• Topics: deep-learning, deep-reinforcement-learning, generative-adversarial-network, generative-ai, generative-ai-projects, gpt, langchain-python, llama3, llm, machine-learning, python3, rag, transformer
• Language: Jupyter Notebook
• Homepage: https://rvats20.github.io/rahulvts/
• Size: 16.6 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# 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:

```bash

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:

```python

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 [email protected].