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160 lines
5.1 KiB
Markdown
160 lines
5.1 KiB
Markdown
---
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slug: nlp-hr-onboarding
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title: Intelligent HR Onboarding Assistant
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type: Academic Project
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description: Intelligent HR onboarding assistant using RAG, LangChain agents, and MistralAI embeddings to help new employees navigate company policies, employee directory, and administrative tasks.
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shortDescription: An AI-powered assistant for streamlining HR onboarding processes and improving new hire experience.
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publishedAt: 2026-03-13
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readingTime: 3
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favorite: false
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status: Completed
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tags:
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- Python
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- NLP
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- LangChain
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- RAG
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icon: i-ph-robot-duotone
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---
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**NLP Project — Master M2**
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*Authors: Arthur DANJOU, Aksscel Meh-Rik, Moritz von SIEMENS*
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::BackgroundTitle{title="Project Overview"}
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::
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The **Intelligent HR Onboarding Assistant** is a conversational AI system designed to guide new employees during their first week at **TechCorp**. It combines retrieval-augmented generation, tool-using agents, and conversational memory to provide accurate and actionable HR support.
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The assistant can answer policy questions, retrieve employee information, schedule internal meetings, and prepare leave requests from natural-language prompts.
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::BackgroundTitle{title="Key Features"}
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::
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- **Semantic HR policy search** powered by a RAG pipeline.
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- **Employee directory lookup** from structured JSON records.
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- **Meeting scheduling tools** integrated through LangChain.
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- **Automated leave request workflow** from chat instructions.
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- **Sliding-window memory** to keep multi-turn context coherent.
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- **Interactive Gradio UI** with visible agent actions and tool calls.
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::BackgroundTitle{title="Architecture"}
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::
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```
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┌──────────────────────────────────────────────────────────┐
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│ HR Onboarding Assistant — TechCorp │
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│ │
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│ 📝 System prompts (LangChain LCEL) │
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│ 🧠 Sliding window conversational memory │
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│ 🔧 Tools: │
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│ ├── 🔍 Knowledge base search (RAG) │
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│ ├── 👤 Employee directory │
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│ ├── 📅 Meeting scheduling │
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│ ├── 🏖️ Leave request submission │
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│ └── 🕐 Current date and time │
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│ 🔄 ReAct loop: reason → act → observe │
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│ 📊 MistralAI Embeddings + Qdrant Vector Store │
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└──────────────────────────────────────────────────────────┘
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```
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::BackgroundTitle{title="Prerequisites"}
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::
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- Python ≥ 3.13
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- MistralAI API key
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::BackgroundTitle{title="Installation"}
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::
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1. **Clone the repository**
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```bash
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git clone <repository-url>
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cd NLP-Intelligent-HR-Onboarding-Assistant-with-RAG-and-LangChain
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```
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2. **Install dependencies**
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```bash
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uv sync
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```
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3. **Configure MistralAI API key**
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Set the environment variable:
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```bash
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export MISTRAL_API_KEY="your_api_key"
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```
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::BackgroundTitle{title="Usage"}
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::
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### Run the Jupyter notebook
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```bash
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jupyter notebook projet.ipynb
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```
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Execute cells sequentially to:
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1. Analyze tokenization of HR documents
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2. Create the Qdrant vector database
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3. Initialize the ReAct agent
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4. Run demonstrations
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5. Launch the Gradio interface (runs on `http://127.0.0.1:7860`)
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### Data structure
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```
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data/
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├── entreprise.md # HR knowledge base (leave policy, remote work, etc.)
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└── employés.json # TechCorp employee directory
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```
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::BackgroundTitle{title="Learning Modules"}
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::
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| TP | Concept | Usage |
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|:---|:--------|:------|
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| **TP1** | Embeddings | Document vectorization and cosine similarity retrieval |
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| **TP2** | BPE Tokenization | Token-cost analysis with FR/EN comparison |
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| **TP3** | LLM + LangChain | ChatMistralAI setup, prompts, and LCEL chains |
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| **TP4** | Agents + Memory | `@tool` usage, ReAct orchestration, sliding-window memory |
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| **TP5** | RAG + Gradio | Qdrant indexing, semantic retrieval, interactive UI |
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::BackgroundTitle{title="Technologies"}
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::
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- **LangChain**: LLM orchestration framework
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- **MistralAI**: LLM inference and embeddings (`mistral-embed`)
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- **Qdrant**: In-memory vector database
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- **Gradio**: Interactive web interface
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- **tiktoken**: BPE tokenization analysis
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- **pandas**: Employee data manipulation
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::BackgroundTitle{title="Main Dependencies"}
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::
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```
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langchain>=1.2.11
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langchain-mistralai>=1.1.1
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langchain-qdrant>=1.1.0
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gradio>=6.9.0
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tiktoken>=0.12.0
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pandas>=3.0.1
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```
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::BackgroundTitle{title="Example Prompts"}
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::
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- "How many days of annual leave do I have?"
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- "What is the remote work policy?"
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- "Give me Claire Petit's contact information"
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- "Schedule a meeting with the Data Science team tomorrow at 2pm"
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- "I want to request leave from January 15th to 20th"
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::BackgroundTitle{title="Authors"}
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::
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- **Arthur DANJOU**
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- **Axelle MERIC**
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- **Moritz von SIEMENS**
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*Project completed as part of the Natural Language Processing course — Master M2*
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