# FAQ Bot – ChromaDB + MCP-style Tool

This project implements a simple FAQ bot that answers questions about a machine learning course.  
The bot uses:

- **ChromaDB** to store and retrieve FAQ documents.
- **Ollama** embeddings (`nomic-embed-text`) for vectorization.
- **LangChain** to build an agent that routes queries to the appropriate tool.
- **MCP-style HTTP tool** (`fetch_course_meta`) that returns course metadata from a local JSON file.

## Project Structure

```
.
├── chroma_faq/          # Persisted Chroma vector store
├── data/
│   ├── faq1.md
│   ├── faq2.md
│   ├── faq3.md
│   └── course_meta.json
├── src/
│   ├── __init__.py
│   ├── agent.py
│   ├── cli.py
│   ├── main.py
│   └── tools.py
├── requirements.txt
└── README.md
```

## Setup

1. **Install Ollama**  
   Download and install Ollama from https://ollama.ai/.  
   Pull the required models:

   ```bash
   ollama pull nomic-embed-text
   ollama pull llama3
   ```

2. **Create a virtual environment** (optional but recommended):

   ```bash
   python -m venv venv
   source venv/bin/activate   # On Windows: venv\Scripts\activate
   ```

3. **Install Python dependencies**:

   ```bash
   pip install -r requirements.txt
   ```

## Running the Bot

### Preset Questions

Run the script without arguments to execute three preset questions (two for the FAQ tool, one for the metadata tool):

```bash
python -m src.main
```

You should see output similar to:

```
Running preset questions:

Q1: What is the deadline for Assignment 1?
A1: The deadline for Assignment 1 is August 31, 2026. source: chroma

Q2: How many lectures are there in the course?
A2: There are 12 lectures in the course. source: chroma

Q3: What is the course schedule for next week?
A3: The course schedule for next week is:
- 2026-09-01: Lecture 1 – Introduction to ML (Room 101)
- 2026-09-08: Lecture 2 – Data Preprocessing (Room 102)
- 2026-09-15: Lecture 3 – Linear Regression (Room 103)
source: mcp_meta
```

### Interactive Mode

Start an interactive session:

```bash
python -m src.main --interactive
```

You can type any question, and the bot will answer using the appropriate tool. Type `exit` or `Ctrl+C` to quit.

## How It Works

1. **Data Loading**  
   `src/tools.py` contains `load_faq_to_chroma()` which reads all `.md` files in `data/`, chunks them, embeds them with `nomic-embed-text`, and persists the vector store in `chroma_faq/`.

2. **Tools**  
   - `search_course_docs(query, k)` – searches the Chroma vector store for relevant FAQ snippets.  
   - `fetch_course_meta(query)` – reads `data/course_meta.json` and returns schedule or instructor information based on the query.

3. **Agent**  
   `src/agent.py` builds a LangChain agent that:
   - Uses a system prompt to decide which tool to call.  
   - Adds a `source:` tag to the final answer indicating whether the answer came from the FAQ (`chroma`) or the metadata tool (`mcp_meta`).

4. **CLI**  
   `src/cli.py` provides a simple command‑line interface to run preset questions or an interactive session.

## Extending the Bot

- **Add more FAQ documents** – Place additional `.md` files in `data/` and re‑run the script to rebuild the vector store.
- **Add more metadata** – Update `data/course_meta.json` or modify `fetch_course_meta` to call a real HTTP endpoint.
- **Change the LLM** – Replace `Ollama` with another LLM provider in `src/agent.py`.

## License

This project is provided as-is for educational purposes. Feel free to adapt and extend it for your own use cases.