title: HiringHelp-Chatbot app_file: app.py sdk: gradio sdk_version: 5.22.0

👔 HiringHelp Chatbot

A chatbot that helps you find the most fitting candidate for the role! Made using RAG (Retrieval-Augmented Generation) with LangChain.

How It Works

HiringHelp uses LangChain's RAG implementation to provide accurate, document-grounded responses. The process involves:

1. Document Processing: Candidate documents are split into chunks and embedded
2. Retrieval: When a query is received, relevant document chunks are retrieved using FAISS vector similarity
3. Generation: Retrieved context is combined with the query to generate accurate responses

Technology

• API & 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.
• Stack: LangChain, FAISS, Gradio, Flask-Limiter

Features

• Interactive chat interface
• Support for text document formats
• Example questions for easy interaction
• Source attribution for responses
• Rate limiting (10 requests/minute, 100 requests/day)
• Vector similarity search for accurate retrieval
• Environment variable configuration

Usage Examples

"List all the available candidates"
"Tell me about a candidate named [Name]"
"Which candidate is best for [Role] role?"

Demo Snapshots

A demo version is available with sample candidate data for testing purposes.

❔ Ask about a specific candidate

🏆 Or ask who's best for the role

👀 Preview all candidate's resume!

Rate Limits

• 10 requests per minute
• 100 requests per day

Deployment

1. Vercel Deployment: Live Demo - inactive API [Preview Only]

2. Hugging Face Spaces: Interactive Demo - active

3. Local Development: See below for setup instructions & local-docs branch for complete script.

---

❝ Interested in building your own chatbot? Follow this setup instructions below! (◕‿◕) ❞

Local Development Setup

Requirements

Flask==2.0.3
Werkzeug==2.0.3
openai>=1.0.0
sqlalchemy==1.4.25
python-dotenv==1.0.1
pandas==2.2.0
scikit-learn==1.5.0
langchain-core>=0.1.17
langchain-community>=0.0.13
langchain>=0.1.0
tiktoken
langchain-openai
faiss-cpu==1.7.4
Flask-Limiter>=3.5.0
requests>=2.32.3
aiohttp==3.9.1
beautifulsoup4==4.12.2

1. Clone the repository:

git clone https://github.com/natgluons/HiringHelp-Chatbot.git
cd HiringHelp-Chatbot

2. Set up environment variables: Create a .env file in the root directory:

OPENROUTER_API_KEY=your_api_key_here

3. Add candidate documents: Place candidate documents in the knowledge_sources directory.

4. Run with Docker:

# Build and start the container
docker-compose up --build

# Access the web interface at http://localhost:5000

Docker Commands

# Start the application
docker-compose up -d

# Stop the application
docker-compose down

# View logs
docker-compose logs -f

# Rebuild and restart
docker-compose up --build -d

Project Structure

HiringHelp-Chatbot/
├── api/                    # Main application code
│   ├── index.py           # Flask application and API endpoints
│   └── __init__.py
├── knowledge_sources/      # Directory for candidate documents
├── lib/                    # Helper libraries
├── public/                 # Static files
├── database/              # Database related files
├── docker-compose.yml     # Docker compose configuration
├── Dockerfile             # Docker build instructions
├── requirements.txt       # Python dependencies
└── .env                   # Environment variables