googlecloudplatform--generative-ai/gemini/sample-apps/quickbot/website-search-using-agent-builder/backend

QuickBotApp website-search-using-agent-builder

Setting up

1. Create virtualenv and install dependencies

Create a virtual environment on the root of the application, activate it and install the requirements

python3 -m venv .venv
source .venv/bin/activate
pip3 install -r requirements.txt

2. Setup gcloud credentials

gcloud auth list
gcloud config list

gcloud auth login
gcloud config set project <your project id> 
gcloud auth application-default set-quota-project <your project id>

gcloud auth list
gcloud config list

3. Running the set up script

Ensure the necessary APIs are enabled for your project. At a minimum, you will need: BigQuery API Discovery Engine API (IAM roles to interact with Discovery Engine e.g. "Discovery Engine Viewer" or "Discovery Engine Admin") Then run

python3 setup.py

4. Add environment variables

If you have Mac or Windows

. ./local.env

If you have Linux

Open the file .venv/bin/activate and paste the env variables from .local.env after the PATH export, like this:

...

_OLD_VIRTUAL_PATH="$PATH"
PATH="$VIRTUAL_ENV/bin:$PATH"
export PATH

# QUICKBOT ENV VARIABLES
export ENVIRONMENT=development
export FRONTEND_URL=http://localhost:4200
export BIG_QUERY_DATASET=bigquery_quickbot_website_search
...

Check that the env variables has been taken into account, running:

env

You should see the new env variables set there

5. Run the application

Finally run using uvicorn

uvicorn main:app --reload --port 8080

Code Styling & Commit Guidelines

To maintain code quality and consistency:

• TypeScript (Frontend): We follow Angular Coding Style Guide by leveraging the use of Google's TypeScript Style Guide using gts. This includes a formatter, linter, and automatic code fixer.
• Python (Backend): We adhere to the Google Python Style Guide, using tools like pylint and black for linting and formatting.
• Commit Messages: We suggest following Angular's Commit Message Guidelines to create clear and descriptive commit messages.

Frontend (TypeScript with gts)

1. Initialize gts (if not already done in the project): Navigate to the frontend/ directory and run:

   npx gts init
   

   This will set up gts and create necessary configuration files (like tsconfig.json). Ensure your tsconfig.json (or a related gts config file like .gtsrc) includes an extension for gts defaults, typically:

   {
     "extends": "./node_modules/gts/tsconfig-google.json",
     // ... other configurations
   }
   

2. Check for linting issues:

   npm run lint
   

   (This assumes a lint script is defined in package.json, e.g., "lint": "gts lint")
3. Fix linting issues automatically (where possible):

   npm run fix
   

   (This assumes a fix script is defined in package.json, e.g., "fix": "gts fix")

Backend (Python with pylint and black)

1. Ensure Dependencies are Installed: Add pylint and black to your backend/requirements.txt file:

   pylint
   black
   

   Then install them within your virtual environment:

   pip install pylint black
   # or pip install -r requirements.txt
   

2. Configure pylint: It's recommended to have a .pylintrc file in your backend/ directory to configure pylint rules. You might need to copy a standard one or generate one (pylint --generate-rcfile > .pylintrc).
3. Check for linting issues with pylint: Navigate to the backend/ directory and run:

   pylint .
   

   (Or specify modules/packages: pylint your_module_name)
4. Format code with black: To automatically format all Python files in the current directory and subdirectories:

   python -m black . --line-length=80