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| title: Rag Demo With Gradio | |
| emoji: 🏆 | |
| colorFrom: pink | |
| colorTo: blue | |
| sdk: gradio | |
| sdk_version: 4.19.2 | |
| app_file: app.py | |
| pinned: false | |
| license: mit | |
| # Advanced RAG System | |
| This repository contains the code for a Gradio web app that demoes a Retrieval-Augmented Generation (RAG) system. This app is designed to allow users to load multiple documents of their choice into a vector database, submit queries, and receive answers generated by a sophisticated RAG system that leverages the latest advancements in natural language processing and information retrieval technologies. | |
| ## Features | |
| #### 1. Dynamic Processing | |
| - Users can load multiple source documents of their choice into a vector store in real-time. | |
| - Users can submit queries which are processed in real-time for enhanced retrieval and generation. | |
| #### 2. PDF Integration | |
| - The system allows for the loading of multiple PDF documents into a vector store, enabling the RAG system to retrieve information from a vast corpus. | |
| #### 3. Advanced RAG System | |
| Integrates various components, including: | |
| - **UI**: Allows users to input URLs for documents and then input user queries; displays the LLM response. | |
| - **Document Loader**: Loads documents from URLs. | |
| - **Text Splitter**: Chunks loaded documents. | |
| - **Vector Store**: Embeds text chunks and adds them to a FAISS vector store; embeds user queries. | |
| - **Retrievers**: Uses an ensemble of BM25 and FAISS retrievers, along with a Cohere reranker, to retrieve relevant document chunks based on user queries. | |
| - **Language Model**: Utilizes a Llama 2 large language model for generating responses based on the user query and retrieved context. | |
| #### 4. PDF and Query Error Handling | |
| - Validates PDF URLs and queries to ensure that they are not empty and that they are valid. | |
| - Displays error messages for empty queries or issues with the RAG system. | |
| #### 5. Refresh Mechanism | |
| - Instructs users to refresh the page to clear / reset the RAG system. | |
| ## Installation | |
| To run this application, you need to have Python and Gradio installed. Follow these steps: | |
| 1. Clone this repository to your local machine. | |
| 2. Create and activate a virtual environment of your choice (venv, conda, etc.). | |
| 3. Install dependencies from the requirements.txt file by running `pip install -r requirements.txt`. | |
| 4. Set up environment variables REPLICATE_API_TOKEN (for a Llama 2 model hosted on replicate.com) and COHERE_API_KEY (for embeddings and reranking service on cohere.com) | |
| 4. Start the Gradio app by running `python rag_gradio_app.py`. | |
| ## Licence | |
| MIT license |