ServiceNow Incident Analysis QnA Bot

An integrated Python Flask App for extracting ServiceNow incident data, exporting it to CSV, and utilizing Langchain with Amazon Bedrock's models for an AI-powered Q&A bot.

Prerequisites

• Python 3.9 or higher
• Docker (optional)
• AWS Access Key ID, AWS Secret Access Key, Region
• ServiceNow Client ID, Client Secret, Username, Password, Instance Name

Clone the repository

git clone https://github.com/sprider/servicenow-incident-analysis-qnabot.git

Setup virtual environment

Navigate to your project directory and create a virtual environment:

cd servicenow-incident-analysis-qnabot/app
python3 -m venv venv

This creates a new virtual environment named venv in your project directory.

Activate the virtual environment

Before you can start installing or using packages in your virtual environment you’ll need to activate it. Activating a virtual environment will put the virtual environment-specific python and pip executables into your shell’s PATH.

On macOS and Linux:

source venv/bin/activate

Install requirements

To install the Python packages that the application depends on, run the following command:

pip3 install -r requirements.txt

Set environment variables

The application uses several environment variables that you'll need to set. You can set them in your shell, or you can put them in a .env file in the app directory of the project. Here's what your .env file should look like:

AWS_ACCESS_KEY_ID=your_aws_access_key_id
AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key
AWS_REGION=your_aws_region
SNOW_CLIENT_ID=your_snow_client_id
SNOW_CLIENT_SECRET=your_snow_client_secret
SNOW_USER=your_snow_user
SNOW_PASSWORD=your_snow_password
SNOW_INSTANCE=your_snow_instance

"Replace your_aws_access_key_id, your_aws_region, your_aws_secret_access_key, your_snow_client_id, your_snow_client_secret, your_snow_user, your_snow_password, and your_snow_instance with your actual AWS Access Key ID, AWS Region, AWS Secret Access Key, ServiceNow Client ID, ServiceNow Client Secret, ServiceNow User, ServiceNow Password, and ServiceNow Instance."

Running the app locally

Run the Flask app:

python3 app.py

The app will start on http://localhost:5000.

Running the app in a Docker container

Build the Docker image:

Navigate to the directory where your Dockerfile is located.

docker build -t servicenow-incident-analysis-qnabot:latest .

Run the Docker container with the necessary environment variables:

docker run -d -p 5000:5000 \
    -e AWS_ACCESS_KEY_ID=your_aws_access_key_id \
    -e AWS_SECRET_ACCESS_KEY=your_aws_secret_access_key \
    -e AWS_REGION=your_aws_region \
    -e SNOW_CLIENT_ID=your_snow_client_id \
    -e SNOW_CLIENT_SECRET=your_snow_client_secret \
    -e SNOW_USER=your_snow_user \
    -e SNOW_PASSWORD=your_snow_password \
    -e SNOW_INSTANCE=your_snow_instance \
    --name myapp_container servicenow-incident-analysis-qnabot:latest

The app will be accessible at http://localhost:5000.

Running the App with Docker Compose

Use Docker Compose to build and start the application. This command also starts any other services defined in your docker-compose.yml file.

Make sure the .env file in the app directory has the correct environment variables set before starting the application.

Start the Application

docker-compose up -d

Stop the Application

If you want to stop the application, you can use the following command:

docker-compose down