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| title: GamerRadar RAG Chatbot | |
| emoji: 📉 | |
| colorFrom: yellow | |
| colorTo: purple | |
| sdk: gradio | |
| sdk_version: 6.19.0 | |
| python_version: '3.13' | |
| app_file: app.py | |
| pinned: false | |
| Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference | |
| # GamerRadar: Video Game Recommendation System using RAG | |
| GamerRadar is an intelligent video game recommendation assistant built using a Retrieval-Augmented Generation (RAG) pipeline. The system allows users to input their gaming preferences or games they previously enjoyed, and it retrieves contextually relevant recommendations from a specialized dataset. | |
| This project was developed as part of the Generative AI Bootcamp to demonstrate practical implementation of document indexing, vector databases, LLM orchestration, and interactive user interfaces. | |
| ## Features | |
| - Custom Dataset Integration: Uses a curated dataset of video games containing titles, genres, and detailed descriptions. | |
| - Open-Source Embeddings: Leverages Hugging Face sentence-transformers for local text embedding generation without API costs. | |
| - Vector Storage: Implements ChromaDB as a local vector database to store and perform similarity searches on game documents. | |
| - LLM Pipeline: Utilizes LangChain Expression Language (LCEL) to create a robust RAG pipeline powered by Google Gemini 2.5 Flash. | |
| - Interactive UI: Features a web-based chat interface deployed via Gradio for real-time user interaction. | |
| ## Tech Stack | |
| - Framework: LangChain / LangChain Community Core | |
| - Embedding Model: Hugging Face (all-MiniLM-L6-v2) | |
| - Vector Store: ChromaDB | |
| - Large Language Model: Google Gemini 2.5 Flash (via Google AI Studio) | |
| - User Interface: Gradio | |
| - Data Management: Pandas | |
| ## Project Structure and Pipeline | |
| 1. Data Preparation: A dataset of video games is initialized and saved to a local CSV file. | |
| 2. Document Loading: The CSV data is extracted and parsed into structured LangChain Document objects. | |
| 3. Embedding and Indexing: The documents are processed through the Hugging Face embedding model and indexed inside ChromaDB. | |
| 4. Retrieval and Prompting: A custom system prompt is configured to instruct the assistant. A retriever fetches the top 2 most similar games based on the user's input. | |
| 5. Generation: The retrieved game contexts and the user query are passed through an LCEL pipeline to Gemini 2.5 Flash to generate accurate, context-aware recommendations. | |
| 6. Interface Deployment: The entire workflow is wrapped into a Gradio ChatInterface, launching a local widget and a shareable public URL. | |
| ## Setup and Installation | |
| To run this notebook in Google Colab or a local Python environment, install the required dependencies: | |
| ```bash | |
| pip install langchain langchain-community langchain-chroma langchain-google-genai sentence-transformers gradio pandas |