https://github.com/machinelearningzuu/advanced-rag-experiments
Explore the cutting-edge world of advanced retriever and generator (RAG) models through the "Advanced-RAG-Experiments" repository. This collection of Jupyter notebooks and experiments is designed to provide a deep dive into the capabilities and nuances of advanced RAG models in the field of natural language processing.
https://github.com/machinelearningzuu/advanced-rag-experiments
langchain large-language-models llama-index natural-language-processing python
Last synced: about 1 year ago
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Explore the cutting-edge world of advanced retriever and generator (RAG) models through the "Advanced-RAG-Experiments" repository. This collection of Jupyter notebooks and experiments is designed to provide a deep dive into the capabilities and nuances of advanced RAG models in the field of natural language processing.
- Host: GitHub
- URL: https://github.com/machinelearningzuu/advanced-rag-experiments
- Owner: machinelearningzuu
- Created: 2024-01-17T12:54:11.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2024-01-30T03:30:17.000Z (over 2 years ago)
- Last Synced: 2025-04-12T06:05:17.563Z (about 1 year ago)
- Topics: langchain, large-language-models, llama-index, natural-language-processing, python
- Language: Jupyter Notebook
- Homepage:
- Size: 197 KB
- Stars: 3
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Advanced-RAG-Experiments
Explore the cutting-edge world of advanced retriever and generator (RAG) models through the "Advanced-RAG-Experiments" repository. This collection of Jupyter notebooks and experiments is designed to provide a deep dive into the capabilities and nuances of advanced RAG models in the field of natural language processing.
## What's Inside?
- **Experiments:** Engage in hands-on experiments that push the boundaries of retriever and generator architectures. Explore novel approaches to information retrieval, document selection, and context-aware text generation.
- **Comparisons:** Evaluate and compare different RAG variants, fine-tuning strategies, and pre-training techniques to understand their impact on performance and versatility.
- **Use Cases:** Discover practical use cases where advanced RAG models excel, from general question-answering systems to E2E automation systems and beyond.
## How to Use?
1. **Clone the Repository:**
```bash
git clone https://github.com/machinelearningzuu/Advanced-RAG-Experiments.git
cd Advanced-RAG-Experiments
```
2. **Set Up Environment:**
```bash
# Create and activate a virtual environment (optional but recommended)
python -m venv venv
source venv/bin/activate # On Windows, use `venv\Scripts\activate`
# Install dependencies
pip install -r requirements.txt
```
3. **Explore Experiments:**
Open the Jupyter notebooks using your preferred environment (Jupyter Notebook, JupyterLab, Google Colab, etc.) and start experimenting with advanced RAG models.
## Contribute
Contributions are encouraged! Whether you're interested in refining existing experiments, adding new insights, or enhancing documentation, your contributions play a crucial role in advancing the understanding of RAG models. Refer to [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.
## License
This repository is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
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Feel free to adapt and modify the description to better suit the specific goals and focus of your "Advanced-RAG-Experiments" repository. Good luck with your exploration of advanced RAG models!