# FAQ Bot – QDrant Vector Store

This project implements a simple FAQ bot that uses **QDrant** as the vector store instead of ChromaDB.  
The bot can ingest a text file containing FAQ content, embed the text using OpenAI embeddings, store the embeddings in QDrant, and answer user questions by retrieving the most relevant passages.

## Features

- **Vector Store** – QDrant (via `qdrant-client`)
- **Embeddings** – OpenAI `text-embedding-ada-002`
- **CLI** – Ingest data, query the bot, delete the collection
- **API** – `get_response(question: str, top_k: int = 5)` for integration with tools like MCP-tool

## Prerequisites

- Python 3.9+
- QDrant server running locally or accessible via network
- OpenAI API key

## Setup

1. **Clone the repository**

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

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. **Set environment variables**

   Create a `.env` file in the project root or export the variables directly:

   ```bash
   export OPENAI_API_KEY="your-openai-api-key"
   export QDRANT_URL="http://localhost:6333"   # Adjust if your QDrant instance is elsewhere
   export QDRANT_API_KEY=""   # Leave empty if no auth is required
   export QDRANT_COLLECTION="faq_collection"
   ```

   If you prefer not to use a `.env` file, you can set the variables in your shell session.

## Usage

### 1. Ingest Data

Prepare a plain text file (`faq.txt`) containing your FAQ content. Then run:

```bash
python src/index.py ingest faq.txt
```

The script will:

- Split the text into chunks (max 500 characters per chunk)
- Generate embeddings for each chunk
- Store the embeddings in QDrant under the collection name defined by `QDRANT_COLLECTION`

### 2. Query the Bot

```bash
python src/index.py query "What is the return policy?"
```

You can adjust the number of results returned with `--top_k`:

```bash
python src.index.py query "What is the return policy?" --top_k 3
```

### 3. Delete the Collection

> **Warning:** This will permanently delete all data in the collection.

```bash
python src/index.py delete
```

### 4. Integration via API

If you want to use the bot programmatically (e.g., from MCP-tool), import the `get_response` function:

```python
from src.index import get_response

answer = get_response("How do I reset my password?")
print(answer)
```

## Troubleshooting

- **QDrant Connection Errors**  
  Ensure the QDrant server is running and reachable at the URL specified by `QDRANT_URL`. Check firewall settings if accessing remotely.

- **OpenAI API Errors**  
  Verify that `OPENAI_API_KEY` is correct and has sufficient quota. Check the OpenAI dashboard for usage limits.

- **Large Documents**  
  The ingestion script splits documents into 500‑character chunks. Adjust `max_chunk_size` in `split_text_into_chunks` if you need larger or smaller chunks.

## License

This project is provided under the MIT License. Feel free to modify and extend it for your own use cases.

## Contact

For questions or support, contact Artur Kuzakhmetov at `artur@example.com`.