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

Install Ollama
Download and install Ollama from https://ollama.ai/.
Pull the required models:
```
ollama pull nomic-embed-text
ollama pull llama3
```

Create a virtual environment (optional but recommended):

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

Install Python dependencies:
```
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):

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:

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

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/.
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.
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).
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.

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