feat: Testing the newly added link checker

.github/workflows/link-checker.yaml

@@ -19,12 +19,14 @@ on:
     branches:
       - main
   pull_request:
-    branches:
-      - main
 
 jobs:
-  mkdocs-link-check:
+  markdown-link-check:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@master
-    - uses: byrnereese/github-action-mkdocs-link-check@v1
+    - uses: actions/checkout@v4
+    - uses: tcort/github-action-markdown-link-check@v1
+      with:
+        use-quiet-mode: 'yes'
+        use-verbose-mode: 'yes'
+        config-file: '.link-checker-config.json'

.link-checker-config.json

@@ -0,0 +1,16 @@
+{
+  "ignorePatterns": [
+    {
+      "pattern": "^https?://(localhost|127\\.0\\.0\\.1)"
+    },
+    {
+      "pattern": "^((?!http).)*$"
+    }
+  ],
+  "aliveStatusCodes": [
+    200,
+    206,
+    302,
+    403
+  ]
+}

docs/get-started/streaming/quickstart-streaming.md

@@ -45,7 +45,7 @@ adk-streaming/  # Project folder
 
 ### agent.py
 
-Copy-paste the following code block to the [`agent.py`](http://agent.py).
+Copy-paste the following code block into the `agent.py` file.
 
 For `model`, please double check the model ID as described earlier in the [Models section](#supported-models).
 

docs/tools/google-cloud-tools.md

@@ -102,8 +102,8 @@ you only need to follow a subset of these steps.
     from the API, use
     `` `projects/my-project-id/locations/us-west1/apis/my-api-id` ``
 
-4. Create your agent file [Agent.py](http://Agent.py) and add the created tools
-   to your agent definition:
+4. Create your agent file Agent.py and add the created tools to your agent
+   definition:
 
     ```py
     from google.adk.agents.llm_agent import LlmAgent
@@ -188,20 +188,20 @@ Connect your agent to enterprise applications using
 
 
    ![Google Cloud Tools](../assets/application-integration-overview.png)
-   
+
 2. Go to [Connection Tool](https://console.cloud.google.com/integrations/templates/connection-tool/locations/us-central1)
    template from the template library and click on "USE TEMPLATE" button.
 
 
     ![Google Cloud Tools](../assets/use-connection-tool-template.png)
-   
+
 3. Fill the Integration Name as **ExecuteConnection** (It is mandatory to use this integration name only) and
    select the region same as the connection region. Click on "CREATE".
 
 4. Publish the integration by using the "PUBLISH" button on the Application Integration Editor.
 
 
-    ![Google Cloud Tools](../assets/publish-integration.png)  
+    ![Google Cloud Tools](../assets/publish-integration.png)
 
 **Steps:**
 
@@ -223,7 +223,7 @@ Connect your agent to enterprise applications using
     ```
 
     **Note:**
-    
+
     * You can provide service account to be used instead of using default credentials by generating [Service Account Key](https://cloud.google.com/iam/docs/keys-create-delete#creating) and providing right Application Integration and Integration Connector IAM roles to the service account.
     * To find the list of supported entities and actions for a connection, use the connectors apis: [listActions](https://cloud.google.com/integration-connectors/docs/reference/rest/v1/projects.locations.connections.connectionSchemaMetadata/listActions) or [listEntityTypes](https://cloud.google.com/integration-connectors/docs/reference/rest/v1/projects.locations.connections.connectionSchemaMetadata/listEntityTypes)
 
@@ -255,7 +255,7 @@ Connect your agent to enterprise applications using
     }
 
     oauth_scheme = dict_to_auth_scheme(oauth2_data_google_cloud)
-    
+
     auth_credential = AuthCredential(
       auth_type=AuthCredentialTypes.OAUTH2,
       oauth2=OAuth2Auth(
@@ -324,7 +324,7 @@ workflow as a tool for your agent or create a new one.
         project="test-project", # TODO: replace with GCP project of the connection
         location="us-central1", #TODO: replace with location of the connection
         integration="test-integration", #TODO: replace with integration name
-        triggers=["api_trigger/test_trigger"],#TODO: replace with trigger id(s). Empty list would mean all api triggers in the integration to be considered. 
+        triggers=["api_trigger/test_trigger"],#TODO: replace with trigger id(s). Empty list would mean all api triggers in the integration to be considered.
         service_account_credentials='{...}', #optional. Stringified json for service account key
         tool_name_prefix="tool_prefix1",
         tool_instructions="..."

examples/java/demos/patent-search-agent/src/main/java/tools/CloudRunFunction/README.md

@@ -1,7 +1,7 @@
 ### Create Database Objects
 
-In order to run this Cloud Run Function, you should have created the AlloyDB side of things prior, for the database objects. 
-Let’s create an AlloyDB cluster, instance and table where the ecommerce dataset will be loaded. 
+In order to run this Cloud Run Function, you should have created the AlloyDB side of things prior, for the database objects.
+Let’s create an AlloyDB cluster, instance and table where the ecommerce dataset will be loaded.
 
 #### Create a cluster and instance
 1. Navigate the AlloyDB page in the Cloud Console.  An easy way to find most pages in Cloud Console is to search for them using the search bar of the console.
@@ -15,13 +15,13 @@ PostgreSQL 15 / latest recommended
 Region: “us-central1”
 Networking: “default”
 
-4. When you select the default network, you'll see a screen like the one below.  
-Select SET UP CONNECTION.  
+4. When you select the default network, you'll see a screen like the one below.
+Select SET UP CONNECTION.
 
-5. From there, select "Use an automatically allocated IP range" and Continue.  After reviewing the information, select CREATE CONNECTION. 
+5. From there, select "Use an automatically allocated IP range" and Continue.  After reviewing the information, select CREATE CONNECTION.
 
 6. Once your network is set up, you can continue to create your cluster. Click CREATE CLUSTER to complete setting up of the cluster as shown below:
- 
+
 7. Make sure to change the instance id to vector-instance
 If you cannot change it, remember to change the instance id in all the upcoming references.
 
@@ -49,8 +49,8 @@ CREATE EXTENSION IF NOT EXISTS vector;
 select extname, extversion from pg_extension;
 
 
-4. Create a table 
-You can create a table using the DDL statement below in the AlloyDB Studio: 
+4. Create a table
+You can create a table using the DDL statement below in the AlloyDB Studio:
 
 CREATE TABLE patents_data ( id VARCHAR(25), type VARCHAR(25), number VARCHAR(20), country VARCHAR(2), date VARCHAR(20), abstract VARCHAR(300000), title VARCHAR(100000), kind VARCHAR(5), num_claims BIGINT, filename VARCHAR(100), withdrawn BIGINT, abstract_embeddings vector(768)) ;
 
@@ -62,7 +62,7 @@ Run the below statement to grant execute on the “embedding” function:
 GRANT EXECUTE ON FUNCTION embedding TO postgres;
 
 6. Grant Vertex AI User ROLE to the AlloyDB service account
-   
+
 From Google Cloud IAM console, grant the AlloyDB service account (that looks like this: service-<<PROJECT_NUMBER>>@gcp-sa-alloydb.iam.gserviceaccount.com) access to the role “Vertex AI User”. PROJECT_NUMBER will have your project number.
 
 PROJECT_ID=$(gcloud config get-value project)
@@ -72,10 +72,10 @@ gcloud projects add-iam-policy-binding $PROJECT_ID \
 --role="roles/aiplatform.user"
 
 7. Load patent data into the database
-The [Google Patents Public Datasets]([url](https://console.cloud.google.com/launcher/browse?q=google%20patents%20public%20datasets&filter=solution-type:dataset&_ga=2.179551075.-653757248.1714456172)) from BigQuery will be used as our dataset. Here is the link: https://console.cloud.google.com/launcher/browse?q=google%20patents%20public%20datasets&filter=solution-type:dataset&_ga=2.179551075.-653757248.1714456172
+The [Google Patents Public Datasets](https://console.cloud.google.com/launcher/browse?q=google%20patents%20public%20datasets&filter=solution-type:dataset&_ga=2.179551075.-653757248.1714456172) from BigQuery will be used as our dataset. Here is the link: https://console.cloud.google.com/launcher/browse?q=google%20patents%20public%20datasets&filter=solution-type:dataset&_ga=2.179551075.-653757248.1714456172
 
-We will use the AlloyDB Studio to run our queries. The [alloydb-pgvector]([url](https://github.com/AbiramiSukumaran/alloydb-pgvector)) repository includes the
-[insert_scripts.sql]([url](https://github.com/AbiramiSukumaran/alloydb-pgvector/blob/main/insert_scripts.sql)) script we will run to load the patent data:
+We will use the AlloyDB Studio to run our queries. The [alloydb-pgvector](https://github.com/AbiramiSukumaran/alloydb-pgvector) repository includes the
+[insert_scripts.sql](https://github.com/AbiramiSukumaran/alloydb-pgvector/blob/main/insert_scripts.sql) script we will run to load the patent data:
 https://github.com/AbiramiSukumaran/alloydb-pgvector/blob/main/insert_scripts.sql
 
 a. In the Google Cloud console, open the AlloyDB page.
@@ -122,11 +122,11 @@ Maximum instances* 3
 instance type* fi-micro
 
 4. Click CREATE and this connector should be listed in the egress settings now.
-   
+
 6. Select the newly created connector.
-   
+
 8. Opt for all traffic to be routed through this VPC connector.
-   
+
 10. Click NEXT and then DEPLOY.
 
     Once the updated Cloud Function is deployed, you should see the endpoint generated. Copy that and replace in the following command:
@@ -139,6 +139,3 @@ curl -X POST <<YOUR_ENDPOINT>> \
   | jq .
 
 That's it! It is that simple to perform an advanced Contextual Similarity Vector Search using the Embeddings model on AlloyDB data.
-
-
-