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This project is based on the Cocktail Recommendation System, which utilizes the Retrieval-Augmented Generation (RAG) approach to provide users with personalized cocktail recommendations based on their queries.
This repository contains a Streamlit-based Document Question Answering System implementing the Retrieve-and-Generate (RAG) architecture, utilizing Streamlit for the UI, LangChain for text processing, and Google Generative AI for embeddings.
This RAG Streamlit app lets users chat with PDF documents using Gemini and Google's generative AI. Upload PDFs, process text, and get intelligent answers to your questions.
Optimizing a Retrieval-Augmented Generation (RAG) system on the CNN/Daily Mail dataset using LangChain, with performance benchmarking and analysis via RAGAS.
RAG-nificent is a state-of-the-art framework leveraging Retrieval-Augmented Generation (RAG) to provide instant answers and references from a curated directory of PDFs containing information on any given topic. Supports Llama3.1 and OpenAI Models via the Groq API.
Open source implementation of Sova - RAG-based Web search engine using power of LLMs. Using Langchain, Ollama, HuggingFace Embeddings and scraping google search results.