Databricks MCP Assistant (with React UI)

A full-stack, Databricks-themed conversational assistant for supply chain queries, powered by OpenAI Agents and Databricks MCP servers. Includes a React chat UI and a FastAPI backend that streams agent responses.

---

Features

• Conversational chat UI (React) with Databricks red palette
• FastAPI backend with streaming /chat endpoint
• Secure Databricks MCP integration
• Example agent logic and tool usage
• Modern UX, easy local development

---

Project Structure

/ (root)
├── main.py                # Example CLI agent runner
├── api_server.py          # FastAPI backend for chat UI
├── requirements.txt       # Python dependencies
├── ui/                    # React frontend (Vite)
│   ├── src/components/ChatUI.jsx, ChatUI.css, ...
│   └── ...
└── README.md              # (this file)

---

Quickstart

0. Databricks assets

You can kick start your project with Databricks’ Supply-Chain Optimization Solution Accelerator (or any other accelerator if working in a different industry). Clone this accelerator’s GitHub repo into your Databricks workspace and run the bundled notebooks by running notebook 1:

https://github.com/lararachidi/agent-supply-chain/blob/main/README.md

These notebooks stand up every asset the Agent will later reach via MCP, from raw enterprise tables and unstructured e-mails to classical ML models and graph workloads.

1. Prerequisites

• Python 3.10+
• Node.js 18+
• Databricks credentials in ~/.databrickscfg
• (Optional) Virtualenv/pyenv for Python isolation

2. Install Python Dependencies

pip install -r requirements.txt

3. Start the Backend (FastAPI)

To kick off the backend, run:

python -m uvicorn api_server:app --reload --port 8000

• The API will be available at http://localhost:8000
• FastAPI docs: http://localhost:8000/docs

4. Start the Frontend (React UI)

In a different terminal, run the following:

cd ui
npm install
npm run dev

• The app will be available at http://localhost:5173

---

Usage

1. Open http://localhost:5173 in your browser.
2. Type a supply chain question (e.g., "What are the delays with distribution center 5?") and hit Send.
3. The agent will stream back a response from the Databricks MCP server.

---

Troubleshooting

• Port already in use: Kill old processes with lsof -ti:8000 | xargs kill -9 (for backend) or change the port.
• Frontend not loading: Make sure you ran npm install and npm run dev in the ui/ folder.

---

Customization

• To change the agent's greeting, edit ui/src/components/ChatUI.jsx.
• To update backend agent logic, modify api_server.py.
• UI styling is in ui/src/components/ChatUI.css (Databricks red palette).

---

Credits & References

• Inspired by Databricks Supply Chain Solution Accelerator
• Uses openai-agents-python
• Databricks MCP integration via databricks.sdk
• Supply-chain scope enforced by a simple LLM guardrail (see supply_chain_guardrails.py)