Nestlé HR Assistant

A conversational chatbot that answers employee questions about Nestlé's HR policies using Retrieval Augmented Generation (RAG).

How it works

1. Load - The Nestlé HR Policy PDF is loaded and split into text chunks.
2. Embed - Each chunk is converted to a vector using OpenAI embeddings and stored in a FAISS index.
3. Retrieve - On each question, the most relevant chunks are retrieved by similarity search.
4. Generate - GPT-4o receives the retrieved context and produces a grounded, cited answer.
5. Chat - A Gradio web UI provides a conversational interface with memory across turns.

Stack

Layer	Technology
LLM	OpenAI GPT-4o
Embeddings	OpenAI text-embedding-3-small
Vector store	FAISS
RAG pipeline	LangChain (LCEL)
UI	Gradio

Setup

# 1. Install dependencies
pip install -r requirements.txt

# 2. Add your OpenAI API key
cp .env.example .env
# Open .env and set: OPENAI_API_KEY=sk-...

# 3. Run
python hr_assistant.py

The app opens at http://localhost:7860.

Project structure

project_hr_assistant/
├── hr_assistant.py   # Main application
├── requirements.txt
├── .env.example
└── dataset/
    └── the_nestle_hr_policy_pdf_2012.pdf