Negative Keyword Cleaner

This is NOT an official Google product

Negative Keyword Cleaner is a solution that leverages Large Language Models to identify and help you remove Negative Keywords blocking traffic from your Google Ads account.

Prerequisites

1. A Google Ads Developer token
2. A Consent Screen on the GCP Console
3. Create OAuth2 Credentials of type Web

Deployment

To avoid potential conflicts, we recommend deploying this solution in a new Google Cloud Platform (GCP) project. Existing GCP projects may already be linked to a specific Google Ads Manager Account (MCC) through prior use of the Google Ads API.

If you're certain that your chosen GCP project has not been used with the Google Ads API, or that it has only been used with the same MCC you intend to use now, you may proceed with the steps below.

1. If not already done, create a Consent Screen on the GCP Console and publish it.

2. Create a new OAuth 2.0 Client ID with Web Application as type (leave the rest blank).

   • Take note of the Client ID and Client Secret, you are going to need them later.

3. In APIs & Service, enable Artifact Registry API, Cloud Run API, and IAM API.

4. In Cloud Shell

   git clone https://github.com/google-marketing-solutions/negative_keyword_cleaner
   cd negative_keyword_cleaner
   export TF_VAR_project_id=$(gcloud config list --format 'value(core.project)')
   

5. Create a service account for the Cloud Run service to use.

   gcloud iam service-accounts create neg-keywords-cleaner \
     --display-name "Negative Keywords Cleaner"
   

6. Grant the necessary permissions to the service account.

   gcloud projects add-iam-policy-binding $TF_VAR_project_id \
     --member="serviceAccount:neg-keywords-cleaner@$TF_VAR_project_id.iam.gserviceaccount.com" \
     --role="roles/run.invoker"
   gcloud projects add-iam-policy-binding $TF_VAR_project_id \
     --member="serviceAccount:neg-keywords-cleaner@$TF_VAR_project_id.iam.gserviceaccount.com" \
     --role="roles/storage.admin"
   

7. Create an Artifact Registry repository.

   gcloud artifacts repositories create negatives \
     --repository-format=docker \
     --location=us-central1
   

8. Replace the ... with the Client ID and Client Secret obtained in Step 2

   echo 'google_oauth_client_id = "..."' > terraform/secrets.tfvars
   echo 'google_oauth_client_secret = "..."' >> terraform/secrets.tfvars
   echo 'mcc_id = "XXXXXXXXXX"' >> terraform/secrets.tfvars
   echo 'google_ads_api_token = "..."' >> terraform/secrets.tfvars
   echo 'open_ai_key = "..."' >> terraform/secrets.tfvars
   

   You can edit the terraform/main.tf file under the google_iam_policy.noauth section, to restrict access to specific users (the app will be visible to all users with the url per default).

9. Build and deploy container

   docker build -t us-central1-docker.pkg.dev/$TF_VAR_project_id/negatives/negatives:v1 . --no-cache
   docker push us-central1-docker.pkg.dev/$TF_VAR_project_id/negatives/negatives:v1
   

   If it doesn't deploy, you might have to run gcloud auth configure-docker us-central1-docker.pkg.dev and try again.

10. Deploy the solution

    cd terraform/
    terraform init -upgrade
    terraform apply -var-file secrets.tfvars
    

    Take note of the App URL. Example: https://myapp.run.app

11. Go back to Credentials > OAuth 2.0 Client ID and select your Client ID.

    • Add the App URL into the list of Authorized Redirect URL.

Uninstall

You can remove all GCP resources with one command:

terraform destroy

Run the app on a local machine

Create a test client for local development:

# Create the OAuth Client (from the Google Cloud Platform UI, cannot be done programmatically)
open https://console.cloud.google.com

# Copy the OAuth2.0 Client Id and Client Secret in `.env`
echo "OAUTH_CLIENT_ID=xxx" > .env
echo "OAUTH_CLIENT_SECRET=yyy" >> .env
echo "OAUTH_REDIRECT_URI=http://localhost:8080" >> .env

Using a standard virtualenv:

# (Optional) if you have pyenv
pyenv update
pyenv install 3.11.3
pyenv local 3.11.3

# Installs dependencies
python -m venv .venv
source .venv/bin/activate
(.venv) pip install -r requirements.txt
(.venv) pip install python-dotenv

# Runs the streamlit server
(.venv) streamlit run --server.headless=false src/AI_student.py

Disclaimer

This is not an officially supported Google product.

Copyright 2024 Google LLC. Supported by Google LLC and/or its affiliate(s). This solution, including any related sample code or data, is made available on an “as is,” “as available,” and “with all faults” basis, solely for illustrative purposes, and without warranty or representation of any kind. This solution is experimental, unsupported and provided solely for your convenience. Your use of it is subject to your agreements with Google, as applicable, and may constitute a beta feature as defined under those agreements. To the extent that you make any data available to Google in connection with your use of the solution, you represent and warrant that you have all necessary and appropriate rights, consents and permissions to permit Google to use and process that data. By using any portion of this solution, you acknowledge, assume and accept all risks, known and unknown, associated with its usage and any processing of data by Google, including with respect to your deployment of any portion of this solution in your systems, or usage in connection with your business, if at all. With respect to the entrustment of personal information to Google, you will verify that the established system is sufficient by checking Google's privacy policy and other public information, and you agree that no further information will be provided by Google.