## How to Run This Project

### 1️⃣ Create and Activate a Virtual Environment

Run the following command to create a virtual environment:

```sh
python -m venv venv
```
### Activate the Virtual Environment

**On macOS/Linux:**
  ```sh
  source venv/bin/activate
```
### 2️⃣ Install Dependencies

After activating the virtual environment, install the required packages:

```sh
pip install -r requirements.txt
```


### 3️⃣ Run the Flask Application

Once dependencies are installed, start the Flask app by running:

```sh
cd src
python app.py
```

## Environment Variables


1. Copy `.env.example` to `.env`
2. Edit `.env` and fill in the required values.


### **File Placement of .env in Development**
- Place the `.env` file inside `src` directory.

### **Example `.env` File**
```ini
QDRANT_HOST=localhost
QDRANT_PORT=6333
QDRANT_API_KEY=
QDRANT_HTTPS='true'
QDRANT_SSL_VERIFY= 'false'
QDRANT_CLUSTER=''
QDRANT_COLLECTION_NAME='mocktestdata'
MODEL_FOLDER=
TOKENIZER_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:
```ini
import nltk
nltk.download('punkt_tab')
```

This will download the necessary model and store it on your local machine under:

```
~/nltk_data
```