{"id":27044230,"url":"https://github.com/igopalakrishna/ai_knowledge_worker","last_synced_at":"2025-09-03T09:37:05.803Z","repository":{"id":279340652,"uuid":"938481073","full_name":"igopalakrishna/AI_knowledge_Worker","owner":"igopalakrishna","description":"A RAG-based expert Q\u0026A agent for Insurance Tech, built with LangChain, ChromaDB, and OpenAI GPT-4o. 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Built using Retrieval-Augmented Generation (RAG), the system ensures accurate and cost-effective responses by leveraging vector-based document retrieval and OpenAI's LLMs.\n\n---\n\n### Table of Contents  \n1. Features  \n2. Technologies Used  \n3. Folder Structure  \n4. Installation  \n5. Usage  \n6. Deployment  \n7. Contributing  \n8. License  \n\n---\n\n### Features  \n- Retrieval-Augmented Generation using LangChain  \n- Vector embedding storage using ChromaDB  \n- Interactive chatbot interface powered by Gradio  \n- Secure integration with OpenAI API  \n- Supports text documents from the knowledge base  \n- Visualizes vector embeddings using t-SNE  \n\n---\n\n### Technologies Used  \n- Python 3.9  \n- LangChain (Document loading, vector retrieval, and LLM integration)  \n- OpenAI GPT-4o-mini (LLM for question answering)  \n- ChromaDB (Vector database for document embeddings)  \n- Gradio (Chatbot UI)  \n- Matplotlib and Plotly (Vector visualization)  \n\n---\n\n### Folder Structure  \n```plaintext  \nAI_worker  \n├── app.py               # Main application script  \n├── knowledge-base       # Folder containing knowledge documents  \n├── vector_db            # Folder for vector database storage  \n├── requirements.txt     # Required dependencies  \n└── README.md            # Project documentation  \nInstallation\n\nTo run this project locally, follow these steps:\n\n    Clone the repository:\n\ngit clone https://github.com/yourusername/AI_knowledge_Worker.git  \ncd AI_knowledge_Worker  \n\n    Set up a virtual environment (recommended):\n\npython -m venv venv  \nsource venv/bin/activate   # On Windows use: venv\\Scripts\\activate  \n\n    Install dependencies:\n\npip install -r requirements.txt  \n\n    Set up environment variables:\n    Create a .env file in the root directory and add your OpenAI API key:\n\nOPENAI_API_KEY=your_openai_api_key_here  \n\nUsage\n\n    Build the vector database (if not already created):\n\npython app.py  \n\n    Launch the chatbot interface using Gradio:\n\npython app.py  \n\nThe interface will be available at:\nhttp://localhost:7860\nDeployment\nDeploy on Hugging Face Spaces (Recommended)\n\n    Upload the entire AI_worker folder to Hugging Face Spaces.\n    Set the OPENAI_API_KEY under Settings \u003e Secrets in Hugging Face.\n    The app will automatically build and deploy, providing a public URL for access.\n\nDeploy Using Docker\n\n    Build the Docker image:\n\ndocker build -t ai-knowledge-worker .  \n\n    Run the container:\n\ndocker run -p 7860:7860 -e OPENAI_API_KEY=your_openai_api_key_here ai-knowledge-worker  \n\nContributing\n\nContributions are welcome. Feel free to submit issues and pull requests to improve the project.\n\n    Fork the repository\n    Create a new branch:\n\ngit checkout -b feature-branch  \n\n    Commit your changes:\n\ngit commit -m 'Add new feature'  \n\n    Push to the branch:\n\ngit push origin feature-branch  \n\n    Submit a pull request\n\nLicense\n\nThis project is licensed under the MIT License.\nContact\n\nFor inquiries or collaboration opportunities, please reach out via LinkedIn or email.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figopalakrishna%2Fai_knowledge_worker","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Figopalakrishna%2Fai_knowledge_worker","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Figopalakrishna%2Fai_knowledge_worker/lists"}