🤖 Raggedy: Multi-Agent HR Chatbots w/RAG

📚 Overview

RAG HR agents is a Retrieval-Augmented Generation (RAG) based AI chat platform that enables users to create intelligent agents trained on custom documentation sources. These agents will be trained on a job description that you link in the form field for URL. Currently, Raggedy 🪆 only supports documents from greenhouse.

Example of the Chatbot working

✨ Features

• 🧠 Multi-Agent Support – Create multiple chat agents for different documentation sources
• 💬 Contextual Chat – Agents remember past interactions for coherent conversations
• 🔍 Source-Aware RAG Responses – Answers are based on relevant documents and cite sources when possible
• 📂 Agent Management – Create, select, and delete agents as needed
• 🌐 Interactive UI – Streamlit-powered interface for intuitive usage

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🛠️ Technical Components

• Document Ingestion: Parses and chunks documents into vector embeddings
• Vector Storage: Stores embeddings for efficient similarity-based retrieval
• Retriever: Finds the most relevant document chunks for any query
• LLM Integration: Uses OpenAI to generate responses based on retrieved context
• Web Interface: Streamlit app for managing and interacting with agents

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🚀 Getting Started

✅ Prerequisites

• Python 3.12
• OpenAI API Key (set as an environment variable)

📦 Installation

# Clone the repository
git clone <repository-url>
cd privateRAG

# Install dependencies
pip install -r requirements.txt

# Set your OpenAI API key
export OPENAI_API_KEY=your-api-key

▶️ Running the App

streamlit run app.py

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🧑‍💻 Usage Guide

1. 🆕 Create a new agent by entering a document URL and naming your agent
2. 🔄 Select an existing agent from the sidebar
3. 💬 Chat with the agent by asking domain-specific questions
4. 🗑️ Delete agents when they are no longer needed

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🧪 Testing

The project includes test scripts to verify core functionality:

Script	Description
test_invoke.py	Test direct chain invocation logic

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Run Tests

python test_invoke.py

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📁 Project Structure

rag-chat-system/
├── app.py             # Main Streamlit application
├── chat_agent.py      # Agent creation and management
├── ingest.py          # Document ingestion and embedding
├── retriever.py       # Vector store retrieval logic
├── test_invoke.py     # Direct invocation test
└── requirements.txt   # Python dependencies
└── greenhouse_jobs_scraper.py   # Python dependencies

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🔮 Future Improvements

• 📱 Custom Resume (as a markdown) relevant to the position

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👥 Contributors

• [krystal_mess323]

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