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Made using **RAG (Retrieval-Augmented Generation)** with **LangChain**.\n\n![image](https://github.com/user-attachments/assets/afbcd76a-d26f-40af-9081-3fe4e7e041ca)\n\n## How It Works\nHiringHelp uses LangChain's RAG implementation to provide accurate, document-grounded responses. The process involves:\n1. **Document Processing**: Candidate documents are split into chunks and embedded\n2. **Retrieval**: When a query is received, relevant document chunks are retrieved using FAISS vector similarity\n3. **Generation**: Retrieved context is combined with the query to generate accurate responses\n\n## Technology\n- **API \u0026 Model**: Using Qwen-2-7B-Chat via OpenRouter API for its balance of performance and cost-effectiveness in RAG applications, with custom embedding generation for document retrieval.\n- **Stack**: LangChain, FAISS, Gradio, Flask-Limiter\n\n## Features\n- Interactive chat interface\n- Support for text document formats\n- Example questions for easy interaction\n- Source attribution for responses\n- Rate limiting (10 requests/minute, 100 requests/day)\n- Vector similarity search for accurate retrieval\n- Environment variable configuration\n\n## Usage Examples\n```\n\"List all the available candidates\"\n\"Tell me about a candidate named [Name]\"\n\"Which candidate is best for [Role] role?\"\n```\n\n## Demo Snapshots\nA demo version is available with sample candidate data for testing purposes.\n\n### ❔ Ask about a specific candidate\n\u003cimg src=\"https://github.com/user-attachments/assets/7fa55a0b-8e70-4b6f-bdc0-8b07bbc0a9bc\" width=\"800px\"\u003e\n\n### 🏆 Or ask who's best for the role\n\u003cimg src=\"https://github.com/user-attachments/assets/9bba78b9-a027-4d8c-b553-688a5850d680\" width=\"800px\"\u003e\n\n## 👀 Preview all candidate's resume!\n\u003cimg src=\"https://github.com/user-attachments/assets/5762a3f8-3df3-4ca1-8151-7fe8b97206cd\" width=\"800px\"\u003e\n\n## Rate Limits\n- 10 requests per minute\n- 100 requests per day\n\n## Deployment \n1. **Vercel Deployment**: [Live Demo](https://hiring-help-chatbot.vercel.app/) - inactive API [Preview Only]\n\n\u003cimg src=\"https://github.com/user-attachments/assets/c1a43285-bbec-457a-ab24-ba6cdb0a0bde\" width=\"800px\"\u003e\n\n2. **Hugging Face Spaces**: [Interactive Demo](https://huggingface.co/spaces/natgluons/HiringHelp-Chatbot) - active\n\n\u003cimg src=\"https://github.com/user-attachments/assets/25a08529-317d-491b-a376-fec7f224c365\" width=\"800px\"\u003e\n\n\n3. **Local Development**: See below for setup instructions \u0026 local-docs branch for complete script.\n\n---\n*❝\nInterested in building your own chatbot? Follow this setup instructions below! (◕‿◕)\n❞*\n---\n\n# Local Development Setup\n\n### Requirements\n```\nFlask==2.0.3\nWerkzeug==2.0.3\nopenai\u003e=1.0.0\nsqlalchemy==1.4.25\npython-dotenv==1.0.1\npandas==2.2.0\nscikit-learn==1.5.0\nlangchain-core\u003e=0.1.17\nlangchain-community\u003e=0.0.13\nlangchain\u003e=0.1.0\ntiktoken\nlangchain-openai\nfaiss-cpu==1.7.4\nFlask-Limiter\u003e=3.5.0\nrequests\u003e=2.32.3\naiohttp==3.9.1\nbeautifulsoup4==4.12.2\n```\n\n1. Clone the repository:\n```bash\ngit clone https://github.com/natgluons/HiringHelp-Chatbot.git\ncd HiringHelp-Chatbot\n```\n\n2. Set up environment variables:\nCreate a `.env` file in the root directory:\n```\nOPENROUTER_API_KEY=your_api_key_here\n```\n\n3. Add candidate documents:\nPlace candidate documents in the `knowledge_sources` directory.\n\n4. Run with Docker:\n```bash\n# Build and start the container\ndocker-compose up --build\n\n# Access the web interface at http://localhost:5000\n```\n\n## Docker Commands\n```bash\n# Start the application\ndocker-compose up -d\n\n# Stop the application\ndocker-compose down\n\n# View logs\ndocker-compose logs -f\n\n# Rebuild and restart\ndocker-compose up --build -d\n```\n\n## Project Structure\n```\nHiringHelp-Chatbot/\n├── api/                    # Main application code\n│   ├── index.py           # Flask application and API endpoints\n│   └── __init__.py\n├── knowledge_sources/      # Directory for candidate documents\n├── lib/                    # Helper libraries\n├── public/                 # Static files\n├── database/              # Database related files\n├── docker-compose.yml     # Docker compose configuration\n├── Dockerfile             # Docker build instructions\n├── requirements.txt       # Python dependencies\n└── .env                   # Environment variables\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnatgluons%2Fhiringhelp-chatbot","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnatgluons%2Fhiringhelp-chatbot","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnatgluons%2Fhiringhelp-chatbot/lists"}