This repository demonstrates the deployment of a Large Language Model (LLM) with a Retrieval Augmented Generation (RAG) approach. It explains how RAG enhances traditional LLM capabilities by integrating external data for more accurate and contextually relevant responses.
RAG combines the strengths of retrieval systems and generative models to provide accurate and dynamic answers. Instead of relying solely on pre-trained knowledge, RAG retrieves relevant external information during inference to enrich its responses. This approach is particularly useful for applications requiring up-to-date or domain-specific knowledge.
- Enhanced Text Generation: Combines database retrieval with advanced language modeling.
- Practical Examples: Includes examples to demonstrate the workflow.
- Scalability: Designed to handle large datasets for retrieval tasks.
To use this repository, ensure the following are installed:
- Python (>=3.8)
- Notebook runtime environment (e.g., Jupyter Notebook, Google Collab...)
- HuggingFace Access Token (Can be acquired here )
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Clone this repository:
git clone https://github.com/barkiayoub/RAG-Enhanced-Text-Generation.git cd RAG-Enhanced-Text-Generation -
Launch Your Notebook Runtime Environment ( Jupyter Notebook in this case ):
jupyter notebook
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Open
RAG_Application_using_MistralAI.ipynbto get started.
- Load the notebook and follow the step-by-step instructions provided.
- Customize the retrieval and generation pipeline according to your dataset or use case.
- Run the cells to execute the RAG workflow.
- Domain-specific knowledge retrieval
- Research and analysis tools
Contributions are welcome! Please fork the repository and submit a pull request with your improvements.
This project is licensed under the MIT License. See the LICENSE file for details.
- Mistral AI: For the powerful language modeling framework.
- Community: For their continued support and contributions.