rag-knowledge-base\n\n \n\n# rag-knowledge-base\n\n \n\n# rag-knowledge-base\n\nRAG semantic search framework featuring hybrid dense-sparse search, auto-chunking, and LLM synthesis.\n\nA premium, high-quality repository maintained by Novalabs-in.\n\n## 🚀 Key Features\n- Stateful, robust, and clean Python implementation.\n- Standard configuration settings with modular codebase patterns.\n- Extensively optimized for performance, scalability, and ease of deployment.\n\n## 🛠️ Tech Stack & Dependencies\n- Core Language: Python\n- Dependencies (defined in requirements.txt):\ntext\nsentence-transformers>=2.2.0\nqdrant-client>=1.3.0\nopenai>=1.0.0\n\n\n## ⚙️ Setup and Installation\n1. Clone this repository under your workspace:\n bash\n git clone https://github.com/Novalabs-in/rag-knowledge-base.git\n cd rag-knowledge-base\n \n2. Set up and activate a local virtual environment:\n bash\n python3 -m venv .venv\n source .venv/bin/activate\n \n3. Install all dependencies:\n bash\n pip install -r requirements.txt\n \n4. Execute the application:\n bash\n python main.py\n \n\n## 📄 License\nThis repository is licensed under the MIT License.