# FAQ Bot with Qdrant Vector Store

This project implements a simple FAQ chatbot that uses **Qdrant** as the vector store for embeddings.  
The bot loads a set of FAQ entries, generates embeddings with OpenAI’s `text-embedding-ada-002` model, stores them in Qdrant, and answers user queries by performing a similarity search.

## Prerequisites

- Python 3.9+
- A running Qdrant instance (local or remote)
- An OpenAI API key

## Setup

1. **Clone the repository**

   ```bash
   git clone https://git.brojs.ru/kuzakhmetovartur/povtornyy-ekzamen-faq-bot-qdrant.git
   cd povtornyy-ekzamen-faq-bot-qdrant
   ```

2. **Create a virtual environment and install dependencies**

   ```bash
   python -m venv venv
   source venv/bin/activate   # On Windows use `venv\Scripts\activate`
   pip install -r requirements.txt
   ```

3. **Configure environment variables**

   Create a `.env` file in the project root with the following content:

   ```dotenv
   # Qdrant configuration
   QDRANT_HOST=localhost
   QDRANT_PORT=6333
   QDRANT_API_KEY=   # leave empty if no API key is required

   # OpenAI configuration
   OPENAI_API_KEY=your_openai_api_key_here
   ```

   Replace `your_openai_api_key_here` with your actual OpenAI API key.

4. **Run the bot**

   ```bash
   python src/main.py
   ```

   The bot will ingest the FAQ data into Qdrant and then wait for user input. Type a question and press Enter to receive an answer. Type `exit` or `quit` to stop the bot.

## How It Works

1. **Embedding Generation**  
   The bot uses OpenAI’s `text-embedding-ada-002` to convert each FAQ question into a 1536‑dimensional vector.

2. **Vector Store**  
   Qdrant stores these vectors in a collection named `faq_collection`. Each point contains the vector and a payload with the original question and answer.

3. **Querying**  
   When a user asks a question, the bot generates an embedding for the query, performs a cosine similarity search in Qdrant, and returns the answer from the most similar FAQ entry.

## Customization

- **Adding More FAQs**  
  Edit the `FAQ_DATA` list in `src/main.py` to include additional question/answer pairs.

- **Changing the Embedding Model**  
  Replace `"text-embedding-ada-002"` in `get_embedding()` with another OpenAI embedding model if desired.

- **Adjusting Search Parameters**  
  Modify `top_k` in `query_faq()` to return more results or change the similarity metric in `create_or_recreate_collection()`.

## Troubleshooting

- **Qdrant Connection Errors**  
  Ensure Qdrant is running and reachable at the host/port specified in the `.env` file.

- **OpenAI Rate Limits**  
  If you hit rate limits, consider adding retry logic or using a different model.

- **Missing Dependencies**  
  Run `pip install -r requirements.txt` again to ensure all packages are installed.

## License

This project is provided for educational purposes and is not licensed for commercial use.