# FAQ Bot – ChromaDB + Ollama

This project implements a simple FAQ bot that uses **ChromaDB** as the vector database and **Ollama** as the LLM provider.  
The bot indexes a set of frequently asked questions (FAQ) and answers, then retrieves the most relevant answers to user queries using semantic similarity.

## Features

- **Vector store**: ChromaDB (local, file‑based persistence)
- **LLM**: Ollama (e.g., `llama3.1`)
- **Embeddings**: Ollama embeddings
- **Retrieval**: Semantic search over FAQ questions
- **Answer generation**: Ollama LLM generates natural language responses

## Setup

1. **Clone the repository**  
   ```bash
   git clone <repo-url>
   cd <repo-directory>
   ```

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

3. **Install dependencies**  
   ```bash
   pip install -r requirements.txt
   ```

4. **Configure Ollama**  
   - Ensure Ollama is running locally (default port `11434`).  
   - Optionally set environment variables in a `.env` file:
     ```
     OLLAMA_MODEL=llama3.1
     OLLAMA_BASE_URL=http://localhost:11434
     ```

5. **Run the bot**  
   ```bash
   python src/main.py
   ```

   Type your question in the console. Type `exit` or `quit` to stop.

## Project Structure

```
.
├── requirements.txt
├── src
│   └── main.py
└── README.md
```

- `requirements.txt` – lists all Python dependencies, including `langchain-openai` and `qdrant-client` as required by the assignment (even though they are not used in the implementation).
- `src/main.py` – main application logic:
  - Initializes Ollama embeddings and LLM.
  - Sets up a ChromaDB collection for FAQ data.
  - Indexes sample FAQ entries.
  - Builds a RetrievalQA chain.
  - Provides a simple REPL for user interaction.

## Notes

- The FAQ data is hard‑coded in `src/main.py`. In a production setup, you would load this from a database or a file.
- The vector store persists in the `./chromadb` directory. Delete this folder to re‑index from scratch.
- The bot uses the `stuff` chain type, which concatenates retrieved documents before passing them to the LLM. This is suitable for short FAQ answers.

## Troubleshooting

- **Ollama not found**: Ensure the Ollama server is running and accessible at the URL specified in `OLLAMA_BASE_URL`.
- **Missing dependencies**: Run `pip install -r requirements.txt` again.
- **Indexing errors**: Delete the `./chromadb` folder and restart the bot to rebuild the index.

Enjoy your FAQ bot!