docs(samples): add retail search service samples (#133)

* feat: Retail Interactive Tutorials. Search service code samples

* use default project for testing purposes

* add pytest-xdist as a requirement for testing

* fail on missing environment variable

* for testing purposes

* for testing purposes

* for testing purposes

* updated according to the review comments

* 🦉 Updates from OwlBot

See https://github.com/googleapis/repo-automation-bots/blob/main/packages/owl-bot/README.md

* try out GOOGLE_CLOUD_PROJECT_NUMBER instead of PROJECT_NUMBER

* add BUCKET_NAME as an environment variable

* adjust filepath so tests can run

* tests are fixed

* Update samples/interactive-tutorials/README.md

Co-authored-by: Anthonios Partheniou <partheniou@google.com>

* README file is updated

* fix typo in pip install command

Co-authored-by: Karl Weinmeister <11586922+kweinmeister@users.noreply.github.com>
Co-authored-by: Anthonios Partheniou <partheniou@google.com>
Co-authored-by: t-karasova <tkarasyova@griddynamics.com>
Co-authored-by: Owl Bot <gcf-owl-bot[bot]@users.noreply.github.com>

generated_samples/interactive-tutorials/README.md

@@ -0,0 +1,151 @@
+# Google Retail API: Python Code Samples
+
+## Get started with the Google Cloud Retail API
+
+The Retail API provides you with the following possibilities to: 
+  - Create and maintaining the catalog data. 
+  - Fine-tune the search configuration.
+  - Import and maintain the user events data.
+
+You can find the information about the Retail services in the [documentation](https://cloud.google.com/retail/docs)
+
+<!--TODO(tkarasova) update the link to the tutorials when will be published-->
+If you would like to have a closer look at the Retail API features and try them yourself,
+the best option is to use the [Interactive Tutorials](https://cloud.google.com/retail/docs/overview). The tutorials will be launched in the CloudShell environment, and you will be able to request the Retail services and check the response with minimum time and effort.
+
+The code samples in the directory **python-retail/samples/interactive-tutorials** are explicitly created for use with the Retail Interactive Tutorials.
+
+If, for some reason, you have decided to proceed with these code samples without the tutorial, please go through the following steps and set up the required preconditions.
+
+### Select your project and enable the Retail API
+
+Google Cloud organizes resources into projects. This lets you
+collect all the related resources for a single application in one place.
+
+If you don't have a Google Cloud project yet or you're not the owner of an existing one, you can
+[create a new project](https://console.cloud.google.com/projectcreate).
+
+After the project is created, set your PROJECT_ID to a ```project``` variable.
+1. Run the following command in Terminal:
+    ```bash
+    gcloud config set project <YOUR_PROJECT_ID>
+    ```
+
+1. To check that the Retail API is enabled for your Project, go to the [Admin Console](https://console.cloud.google.com/ai/retail/).
+
+### Create service account
+
+To access the Retail API, you must create a service account.
+
+1. To create a service account, follow this [instruction](https://cloud.google.com/retail/docs/setting-up#service-account)
+
+1. Find your service account on the [IAM page](https://console.cloud.google.com/iam-admin/iam),
+   click `Edit` icon, add the 'Storage Admin' and 'BigQuery Admin' roles. It may take some time for changes to apply.
+
+1. Copy the service account email in the Principal field.
+
+### Set up authentication
+
+To run a code sample from the Cloud Shell, you need to be authenticated using the service account credentials.
+
+1. Login with your user credentials.
+    ```bash
+    gcloud auth login
+    ```
+
+1. Type `Y` and press **Enter**. Click the link in a Terminal. A browser window should appear asking you to log in using your Gmail account.
+
+1. Provide the Google Auth Library with access to your credentials and paste the code from the browser to the Terminal.
+
+1. Upload your service account key JSON file and use it to activate the service account:
+
+    ```bash
+    gcloud iam service-accounts keys create ~/key.json --iam-account <YOUR_SERVICE_ACCOUNT_EMAIL>
+    ```
+
+    ```bash
+    gcloud auth activate-service-account --key-file ~/key.json
+    ```
+
+1. To request the Retail API, set your service account key JSON file as the GOOGLE_APPLICATION_CREDENTIALS environment variable :
+    ```bash
+    export GOOGLE_APPLICATION_CREDENTIALS=~/key.json
+    ```
+
+### Set the PROJECT_NUMBER and PROJECT_ID environment variables
+
+You will run the code samples in your own Google Cloud project. To use the **project_number** and **project_id** in every request to the Retail API, you should first specify them as environment variables.
+
+1. Find the project number and project ID in the Project Info card displayed on **Home/Dashboard**.
+
+1. Set the **project_number** with the following command:
+    ```bash
+    export PROJECT_NUMBER=<YOUR_PROJECT_NUMBER>
+    ```
+1. Set the **project_id** with the following command:
+    ```bash
+    export PROJECT_ID=<YOUR_PROJECT_ID>
+    ```
+
+### Install Google Cloud Retail libraries
+
+To run Python code samples for the Retail API tutorial, you need to set up your virtual environment.
+
+1. Run the following commands in a Terminal to create an isolated Python environment:
+    ```bash
+    pip install virtualenv
+    virtualenv myenv
+    source myenv/bin/activate
+    ```
+1. Next, install Google packages:
+    ```bash
+    pip install google
+    pip install google-cloud-retail
+    pip install google-cloud-storage
+    pip install google-cloud-bigquery
+
+    ```
+
+## Import Catalog Data
+
+This step is required if this is the first Retail API Tutorial you run.
+Otherwise, you can skip it.
+
+### Upload catalog data to Cloud Storage
+
+There is a JSON file with valid products prepared in the `product` directory:
+`product/resources/products.json`.
+
+Another file, `product/resources/products_some_invalid.json`, contains both valid and invalid products, and you will use it to check the error handling.
+
+In your own project, create a Cloud Storage bucket and put the JSON file there.
+The bucket name must be unique. For convenience, you can name it `<YOUR_PROJECT_ID>_<TIMESTAMP>`.
+
+1. To create the bucket and upload the JSON file, run the following command in the Terminal:
+
+    ```bash
+    python product/setup/create_gcs_bucket.py
+    ```
+
+    Now you can see the bucket is created in the [Cloud Storage](https://console.cloud.google.com/storage/browser), and the files are uploaded.
+
+1. The name of the created Retail Search bucket is printed in the Terminal. Copy the name and set it as the environment variable `BUCKET_NAME`:
+
+    ```bash
+    export BUCKET_NAME=<YOUR_BUCKET_NAME>
+    ```
+
+### Import products to the Retail Catalog
+
+To import the prepared products to a catalog, run the following command in the Terminal:
+
+```bash
+python product/import_products_gcs.py
+```
+
+## Run your code sample
+
+Run the sample in a terminal with the following command:
+```bash
+python search/search_with_boost_spec.py
+```