This repository implements a Retrieval-Augmented Generation (RAG) approach leveraging advanced Language Models (LLMs). RAG combines the power of pre-trained LLMs with efficient information retrieval, enabling context-aware and coherent content generation.
- RAG Model: Implement a RAG model that combines a language model for generation and a retriever for content retrieval.
- Language Models Integration: Incorporate state-of-the-art language models, such as BERT, GPT, or others, for powerful text generation.
- Efficient Retrieval: Utilize an efficient retriever to gather relevant context from large document collections.
- Customization: Easily adapt the RAG model and language models for specific use cases and domains.
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Clone the repository:
git clone https://github.com/rushizirpe/rag-with-llms.git cd rag-with-llms -
Install dependencies:
pip install -r requirements.txt
streamlit run main.py
Contributions are welcome! Feel free to open issues, submit pull requests, or suggest improvements.