| src | ||
| .gitignore | ||
| docker-compose.yml | ||
| Dockerfile | ||
| README.md | ||
| startup.sh | ||
How to Run This Project
1️⃣ Create and Activate a Virtual Environment
Run the following command to create a virtual environment:
python -m venv venv
Activate the Virtual Environment
On macOS/Linux:
source venv/bin/activate
2️⃣ Install Dependencies
After activating the virtual environment, install the required packages:
pip install -r requirements.txt
3️⃣ Run the Flask Application
Once dependencies are installed, start the Flask app by running:
cd src
python app.py
Environment Variables
This project requires a .env file for configuration.
.env File Placement
- Place the
.envfile insidesrcdirectory.
Example .env File
QDRANT_HOST = "localhost"
QDRANT_PORT = 6333
QDRANT_API_KEY=''
QDRANT_CLUSTER=''
QDRANT_COLLECTION_NAME='titan'
TOKENIZER_FOLDER=''
MODEL_FOLDER=''
ℹ️ Note for production/docker container:
TOKENIZER_FOLDER and MODEL_FOLDER are only required for local development.
When running the application in a Docker container, these variables are already set in the Dockerfile and do not need to be defined in your .env file.
📥 NLTK Setup for Development
If you are running the application in development mode for the first time, you need to manually download the NLTK resource required for sentence tokenization.
Run the following inside your virtual environment:
import nltk
nltk.download('punkt_tab')
This will download the necessary model and store it on your local machine under:
~/nltk_data