🎬 RAG-Based Movie Recommendation Q&A System

This project builds a Retrieval-Augmented Generation (RAG) system for movie recommendations, leveraging FAISS for efficient similarity search and deploying the solution via Hugging Face using Chainlit for an interactive experience.

🔗 Live Demo: MovieChatBot on Hugging Face

🚀 Features

• Question-Answering System: Provides movie recommendations based on natural language queries.
• Vector Search with FAISS: Uses Facebook AI Similarity Search (FAISS) for efficient retrieval of relevant movies.
• Hugging Face Deployment: Hosted and accessible via Hugging Face Spaces.
• Interactive UI with Chainlit: Enables an intuitive and conversational experience for users.
• OpenAI API Calls with LangChain Runnable: Uses LangChain's runnable framework to make API calls to OpenAI for enhanced responses.

🛠️ Tech Stack

• FAISS: Fast retrieval of semantically similar movie embeddings.
• Hugging Face: Model hosting and inference.
• Chainlit: Frontend for conversational interactions.
• Python: Backend implementation.
• LangChain Runnable: Enables structured OpenAI API calls for generating responses.

🔧 Installation

1. Clone the repository:

   git clone https://github.com/your-username/rag-movie-recommendation.git  
   cd rag-movie-recommendation  

2. Install dependencies:

   pip install -r requirements.txt  

3. Run the application locally:

   chainlit run app.py  

📌 Usage

• Ask questions like:
  • "Recommend me a sci-fi movie like Interstellar."
  • "What are some good thriller movies?"
• The system retrieves relevant movie recommendations using FAISS and generates responses using a language model.

📈 Future Enhancements

• Expand the dataset for better recommendations.
• Integrate multi-modal features (images, trailers).
• Fine-tune models for improved relevance.

🤝 Contributing

Feel free to fork the repository, create pull requests, or submit issues!

📜 License

This project is open-source and available under the MIT License.