# FAQ Bot – ChromaDB + Ollama

This project implements a simple FAQ bot that uses **ChromaDB** as the vector store and **Ollama** for embeddings and language generation.  
The bot is built with **LangChain** and relies on a single **MCPTool** to retrieve relevant documents and generate answers.

## Features

- **Embeddings**: Uses the `nomic-embed-text` model from Ollama.
- **Vector Store**: Stores embeddings in a persistent ChromaDB collection.
- **LLM**: Generates answers with the `llama3` model from Ollama.
- **MCPTool**: A single tool that handles retrieval and generation in one step.
- **CLI**: Interactive command‑line interface for quick testing.

## Setup

```bash
# Clone the repository
git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-chromadb-odin.git
cd povtornyy-ekzamen-faq-bot-chromadb-odin

# Create a virtual environment (optional but recommended)
python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\\Scripts\\activate

# Install dependencies
pip install -r requirements.txt
```

### Data

Place your FAQ documents as plain text files (`*.txt`) in the `data/` directory.  
Each file will be loaded, embedded, and stored in ChromaDB.

## Running the Bot

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

You will see a prompt:

```
FAQ Bot powered by ChromaDB and Ollama.
Type 'exit' to quit.

Your question:
```

Type a question and press Enter. The bot will return an answer.

## Testing

Run the unit tests to verify that the bot uses the correct components:

```bash
python -m unittest discover -s tests
```

All tests should pass, confirming that:

- The embeddings are from `OllamaEmbeddings`.
- The vector store is a `Chroma` instance.
- No OpenAI modules are imported.
- Answers are returned as strings.

## Project Structure

```
├── data/                # FAQ documents (plain text)
├── chroma_db/           # Persisted ChromaDB collection
├── src/
│   └── main.py          # Bot implementation
├── tests/
│   └── test_main.py     # Unit tests
├── requirements.txt
└── README.md
```

## Notes

- The bot requires an Ollama server running locally.  
  Ensure that the `nomic-embed-text` and `llama3` models are available:

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

- The vector store is persisted in the `chroma_db/` directory.  
  If you add new documents, delete this folder and rerun the bot to rebuild the index.

Enjoy building your FAQ bot!