🧠 Monk-RET

Monk-RET (Monk Retail Insights Engine) is an intelligent retail analytics platform powered by MonkDB, its MCP server, LangChain, Streamlit.
It helps businesses gain actionable insights from large-scale retail data by orchestrating data ingestion, processing, and visualization seamlessly.

---

✨ Features

• 📊 Retail Analytics Engine – Ingests, processes large-scale retail datasets and writes to MonkDB.
• 🧩 LangChain Orchestrator – Modular orchestration of tasks with LLMs
• ⚡ Batch Data Processing – Automated CSV ingestion & MonkDB syncing
• 📈 Interactive Dashboards – Streamlit-based UI for analytics & insights
• 🔄 Automation – Watchdog-powered auto-refresh for new datasets

---

🚀 Getting Started

1️⃣ Clone the repo

git clone https://github.com/monkdbofficial/demo.retailagent.git
cd demo.retailagent

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Run the Watchdong

python watchdog_.py

4️⃣ (Optional) Start the Streamlit dashboard locally

python orchestrator.py

---

🛠️ Tech Stack

• Languages: Python
• Frameworks: LangChain, Streamlit, FastAPI, Flask
• Data: Pandas, SQLAlchemy, MongoDB
• DevOps: Docker, Watchdog, Jenkins, Terraform
• Visualization: Plotly, Chart.js, Streamlit

---

📊 Example Workflow

1. Drop a new retail CSV into the /data folder
2. watchdog_.py detects and inserts data → DB
3. langchain_orch.py & gen_insights_force.py generate AI-powered insights
4. Open streamlit_app.py → interactive analytics dashboard

---