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

   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

   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:

   # 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

   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.