https://github.com/natgluons/hiringhelp-chatbot
A chatbot that helps with hiring-related questions using RAG (Retrieval-Augmented Generation) with LangChain
https://github.com/natgluons/hiringhelp-chatbot
chatbot gradio hiring-platform huggingface-spaces langchain python rag rag-chatbot vercel-deployment
Last synced: 6 days ago
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A chatbot that helps with hiring-related questions using RAG (Retrieval-Augmented Generation) with LangChain
- Host: GitHub
- URL: https://github.com/natgluons/hiringhelp-chatbot
- Owner: natgluons
- Created: 2025-03-27T04:02:24.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-12T17:21:41.000Z (about 1 year ago)
- Last Synced: 2025-07-19T01:03:23.315Z (12 months ago)
- Topics: chatbot, gradio, hiring-platform, huggingface-spaces, langchain, python, rag, rag-chatbot, vercel-deployment
- Language: Python
- Homepage: https://huggingface.co/spaces/natgluons/HiringHelp-Chatbot
- Size: 1.69 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
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](https://hiring-help-chatbot.vercel.app/) - inactive API [Preview Only]
2. **Hugging Face Spaces**: [Interactive Demo](https://huggingface.co/spaces/natgluons/HiringHelp-Chatbot) - 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:
```bash
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:
```bash
# Build and start the container
docker-compose up --build
# Access the web interface at http://localhost:5000
```
## Docker Commands
```bash
# 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
```