{"id":20469239,"url":"https://github.com/joshmusira/rag_application","last_synced_at":"2026-04-08T21:32:14.554Z","repository":{"id":245342869,"uuid":"817906997","full_name":"JoshMusira/RAG_Application","owner":"JoshMusira","description":"A state-of-the-art Retrieval-Augmented Generation (RAG) application using OpenAI, Qdrant vector store, embeddings, FastAPI, React for the UI, NewsAPI, Word Cloud, and Langchain. 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It provides a web interface and backend API to interact with a language model, perform keyword extraction, and generate word clouds based on retrieved information.\n\n## Features\n\n- **Frontend**: Built with React to provide an interactive web interface.\n- **Backend**: Developed using FastAPI to handle API requests.\n- **Vector Store**: Utilizes Qdrant for vector storage and retrieval.\n- **Language Model**: Powered by OpenAI's GPT-3.5-turbo for generating answers.\n- **Keyword Extraction**: Uses TF-IDF vectorization to extract keywords from text responses.\n- **Word Cloud Generation**: Creates visual representations of word frequency using extracted keywords.\n\n## Requirements\n\n- Python 3.12\n- Node.js and npm\n- Qdrant\n- OpenAI API Key (set as environment variable `OPENAI_API_KEY`)\n\n\n### Contribute\n\n1. Clone the repository:\n\n   ```bash\n   git clone https://github.com/\u003cusername\u003e/RAG-Fullstack-Backend.git\n   cd RAG-Fullstack-Backend\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshmusira%2Frag_application","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjoshmusira%2Frag_application","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjoshmusira%2Frag_application/lists"}