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By combining Pinecone's vector database with Google's Gemini LLM, it provides accurate, contextually relevant medical information grounded in verified literature.\n\n# ✨ Project Features\n\n- RAG Implementation\n  - Semantic Chunking: Intelligent document segmentation preserving medical context\n  - Multi-vector Retrieval: Top-K similarity search with relevance scoring\n  - Context-Aware Generation: Prompt engineering optimized for medical accuracy\n\n- Production-Ready Architecture\n   - Containerized Deployment: Docker support with multi-stage builds\n   - Environment Management: Secure API key handling and configuration\n\n# 📊 Project Workflow\n\n![alt text](flow.png)\n\n# 🖥️ Application Interface\n\n\u003cimg width=\"1918\" height=\"910\" alt=\"image\" src=\"https://github.com/user-attachments/assets/55bb0aa9-67c3-4e41-9491-7468ac5485f1\" /\u003e\n\n\n# 🚀 Tech Stack\n\n\u003cdiv align=\"left\"\u003e\n\n| Category | Technologies |\n|----------|--------------|\n| **🤖 AI Framework \u0026 Models** | `LangChain` `Gemini LLM` `Sentence Transformers` |\n| **🗄️ Vector Database** | `Pinecone` `HuggingFace` |\n| **🌐 Backend** | `Flask` `Flask-CORS` |\n| **🎨 Frontend** | `HTML5` `CSS3` `JavaScript` |\n| **📄 Document Processing** | `PyPDF` |\n| **🔧 Development \u0026 Deployment** | `Docker` `Python-Dotenv` `Python 3.10+` |\n\n\u003c/div\u003e\n\n\n\n# 🛠️ Installation \u0026 Setup\n\n### Prerequisites\n- Pinecone API account\n- Google Gemini API key\n\n## Steps: \n\nClone the repository \n\n```bash \ngit clone https://github.com/yyigitturan/Medical-Chatbot.git\n```\n\n### Step 01- Create a environment after opening the repository \n\n```bash \npython -m venv medicalbot\nsource medicalbot/bin/activate  \n``` \n### Step 02- Install Dependencies\n\n```bash \npip install -r requirements.txt\n``` \n### Step 03- Environment Configuration\n\nCreate a .env file with your API keys: \n\n```bash \nPINECONE_API_KEY=your_pinecone_api_key\nGEMINI_API_KEY=your_gemini_api_key\n``` \n\n### Step 04- Initialize Vector Database\n\n```bash \npython store_index.py\n``` \n### Step 05- Run the Application\n\n```bash \npython app.py\n``` \n## Docker Deployment\nBuild the Image\n\n```bash \ndocker build -t medical-chatbot .\n``` \n\nRun the Container\n\n```bash \ndocker run -p 5000:5000 --env-file .env medical-chatbot\n``` \n# 💡 Usage\nStart the application\n\nAccess the web interface at http://localhost:5000\n\nEnter medical questions like:\n\n\"How can acne be prevented?\"\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyyigitturan%2Fmedical-chatbot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyyigitturan%2Fmedical-chatbot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyyigitturan%2Fmedical-chatbot/lists"}