# FAQ‑Bot – ChromaDB + MCP Tool

## Overview
This project implements an FAQ‑bot that answers questions about local course notes stored in Markdown files using **ChromaDB** for retrieval and a single **MCP‑style tool** for metadata queries.

- **Local knowledge** – 2–3 `.md` files in `data/`.
- **Vector store** – ChromaDB with Ollama embeddings.
- **Tool** – `fetch_course_meta` that performs a GET request to a local JSON file (mock MCP server).
- **Agent** – LangGraph agent that routes the query to the appropriate source and labels the answer with `source: chroma | mcp_meta`.
- **CLI** – 3 example questions (2 for Chroma, 1 for meta) and an interactive mode.

## Setup
```bash
# Pull Ollama model
ollama pull nomic-embed-text

# Install dependencies
pip install -r requirements.txt

# Start local mock MCP server (JSON file)
python -m http.server 8000 --directory mock
```

## Running the Bot
```bash
python main.py --question "What is the course schedule?"
```

## Example Questions
1. **Chroma** – *"What topics are covered in week 3?"*
2. **Chroma** – *"How many hours per week are required?"*
3. **Meta** – *"What is the course schedule?"*

## Project Structure
```
├── data/                 # Markdown FAQ files
├── mock/                 # JSON mock MCP server
├── src/
│   ├── agent.py          # LangGraph agent definition
│   ├── chunker.py        # Chroma loader
│   ├── cli.py            # CLI interface
│   ├── config.py         # Config & constants
│   ├── init_loader.py    # Bulk loader
│   ├── qdrant_store.py   # Vector store helper
│   ├── rag_tools.py      # Search & meta tools
│   ├── tools.py          # MCP wrapper
│   └── vector_store.py   # Chroma store
└── requirements.txt
```

## License
MIT