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

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
```

Set environment variables

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

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:

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

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

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

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.

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:

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.

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