https://github.com/igopalakrishna/ai_knowledge_worker

A RAG-based expert Q&A agent for Insurance Tech, built with LangChain, ChromaDB, and OpenAI GPT-4o. Features vector search, Gradio UI, and Docker-ready deployment.
https://github.com/igopalakrishna/ai_knowledge_worker

chatgpt chatgpt-api language llm nlp nlp-machine-learning openai plotly rag

Last synced: 10 months ago
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A RAG-based expert Q&A agent for Insurance Tech, built with LangChain, ChromaDB, and OpenAI GPT-4o. Features vector search, Gradio UI, and Docker-ready deployment.

• Host: GitHub
• URL: https://github.com/igopalakrishna/ai_knowledge_worker
• Owner: igopalakrishna
• Created: 2025-02-25T02:54:13.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-25T04:32:24.000Z (over 1 year ago)
• Last Synced: 2025-04-05T05:31:20.593Z (about 1 year ago)
• Topics: chatgpt, chatgpt-api, language, llm, nlp, nlp-machine-learning, openai, plotly, rag
• Language: Jupyter Notebook
• Homepage:
• Size: 1.34 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

          
# AI Knowledge Worker  

### An Expert Question-Answering Agent for Insurance Tech  

This project is an AI-powered question-answering agent designed to assist employees of Insurellm, an Insurance Tech company. Built using Retrieval-Augmented Generation (RAG), the system ensures accurate and cost-effective responses by leveraging vector-based document retrieval and OpenAI's LLMs.

---

### Table of Contents  

1. Features  

2. Technologies Used  

3. Folder Structure  

4. Installation  

5. Usage  

6. Deployment  

7. Contributing  

8. License  

---

### Features  

- Retrieval-Augmented Generation using LangChain  

- Vector embedding storage using ChromaDB  

- Interactive chatbot interface powered by Gradio  

- Secure integration with OpenAI API  

- Supports text documents from the knowledge base  

- Visualizes vector embeddings using t-SNE  

---

### Technologies Used  

- Python 3.9  

- LangChain (Document loading, vector retrieval, and LLM integration)  

- OpenAI GPT-4o-mini (LLM for question answering)  

- ChromaDB (Vector database for document embeddings)  

- Gradio (Chatbot UI)  

- Matplotlib and Plotly (Vector visualization)  

---

### Folder Structure  

```plaintext  

AI_worker  

├── app.py               # Main application script  

├── knowledge-base       # Folder containing knowledge documents  

├── vector_db            # Folder for vector database storage  

├── requirements.txt     # Required dependencies  

└── README.md            # Project documentation  

Installation

To run this project locally, follow these steps:

    Clone the repository:

git clone https://github.com/yourusername/AI_knowledge_Worker.git  

cd AI_knowledge_Worker  

    Set up a virtual environment (recommended):

python -m venv venv  

source venv/bin/activate   # On Windows use: venv\Scripts\activate  

    Install dependencies:

pip install -r requirements.txt  

    Set up environment variables:

    Create a .env file in the root directory and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key_here  

Usage

    Build the vector database (if not already created):

python app.py  

    Launch the chatbot interface using Gradio:

python app.py  

The interface will be available at:

http://localhost:7860

Deployment

Deploy on Hugging Face Spaces (Recommended)

    Upload the entire AI_worker folder to Hugging Face Spaces.

    Set the OPENAI_API_KEY under Settings > Secrets in Hugging Face.

    The app will automatically build and deploy, providing a public URL for access.

Deploy Using Docker

    Build the Docker image:

docker build -t ai-knowledge-worker .  

    Run the container:

docker run -p 7860:7860 -e OPENAI_API_KEY=your_openai_api_key_here ai-knowledge-worker  

Contributing

Contributions are welcome. Feel free to submit issues and pull requests to improve the project.

    Fork the repository

    Create a new branch:

git checkout -b feature-branch  

    Commit your changes:

git commit -m 'Add new feature'  

    Push to the branch:

git push origin feature-branch  

    Submit a pull request

License

This project is licensed under the MIT License.

Contact

For inquiries or collaboration opportunities, please reach out via LinkedIn or email.