Update providers/google.mdx

Co-Authored-By: mintlify[bot] <109931778+mintlify[bot]@users.noreply.github.com>

Author: Gerome-Elassaad

providers/google.mdx

@@ -1,201 +1,82 @@
 ---
 title: "Google Gemini"
-description: "Configure GCP Vertex AI with CodinIT to access leading generative AI models like Claude 4.5 Sonnet v2. This guide covers GCP environment setup."
+description: "Configure GCP Vertex AI to access Gemini and Claude models through Google Cloud."
 ---
 
-### Overview
+Access leading AI models like Gemini and Claude 4.5 Sonnet through Google Cloud's Vertex AI platform.
 
-**GCP Vertex AI:**\
-A fully managed service that provides access to leading generative AI models—such as Anthropic's Claude 4.5 Sonnet v2—through Google Cloud.\
-[Learn more about GCP Vertex AI](https://cloud.google.com/vertex-ai).
+**Website:** [https://cloud.google.com/vertex-ai](https://cloud.google.com/vertex-ai)
 
-This guide is tailored for organizations with established GCP environments (leveraging IAM roles, service accounts, and best practices in resource management) to ensure secure and compliant usage.
+## Prerequisites
 
----
-
-### Step 1: Prepare Your GCP Environment
-
-#### 1.1 Create or Use a GCP Project
-
--   **Sign in to the GCP Console:**\
-    [Google Cloud Console](https://console.cloud.google.com/)
--   **Select or Create a Project:**\
-    Use an existing project or create a new one dedicated to Vertex AI.
-
-#### 1.2 Set Up IAM Permissions and Service Accounts
-
--   **Assign Required Roles:**
-
-    -   Grant your user (or service account) the **Vertex AI User** role (`roles/aiplatform.user`)
-    -   For service accounts, also attach the **Vertex AI Service Agent** role (`roles/aiplatform.serviceAgent`) to enable certain operations
-    -   Consider additional predefined roles as needed:
-        -   Vertex AI Platform Express Admin
-        -   Vertex AI Platform Express User
-        -   Vertex AI Migration Service User
-
--   **Cross-Project Resource Access:**
-    -   For BigQuery tables in different projects, assign the **BigQuery Data Viewer** role
-    -   For Cloud Storage buckets in different projects, assign the **Storage Object Viewer** role
-    -   For external data sources, refer to the [GCP Vertex AI Access Control documentation](https://cloud.google.com/vertex-ai/general/access-control)
-
----
-
-### Step 2: Verify Regional and Model Access
-
-#### 2.1 Choose and Confirm a Region
-
-Vertex AI supports multiple regions. Select a region that meets your latency, compliance, and capacity needs. Examples include:
+- GCP account with billing enabled
+- GCP project created
+- IAM permissions configured
 
--   **us-east5 (Columbus, Ohio)**
--   **us-central1 (Iowa)**
--   **europe-west1 (Belgium)**
--   **europe-west4 (Netherlands)**
--   **asia-southeast1 (Singapore)**
--   **global (Global)**
-
-The Global endpoint may offer higher availability and reduce resource exhausted errors. Only Gemini models are supported.
-
-#### 2.2 Enable the Claude 4.5 Sonnet v2 Model
-
--   **Open Vertex AI Model Garden:**\
-    In the Cloud Console, navigate to **Vertex AI → Model Garden**
--   **Enable Claude 4.5 Sonnet v2:**\
-    Locate the model card for Claude 4.5 Sonnet v2 and click **Enable**
-
----
-
-
-#### 3.1 Install and Open CodinIT
-
--   **Download VS Code:**\
-    [Download Visual Studio Code](https://code.visualstudio.com/)
--   **Install the CodinIT Extension:**
-    -   Open VS Code
-    -   Navigate to the Extensions Marketplace (Ctrl+Shift+X or Cmd+Shift+X)
-    -   Search for **Github** and install the extension & Clone the repository
-
-#### 3.2 Configure CodinIT Settings
-
--   **Open CodinIT Settings:**\
-    Click the settings ⚙️ icon within the CodinIT extension
--   **Set API Provider:**\
-    Choose **GCP Vertex AI** from the API Provider dropdown
--   **Enter Your Google Cloud Project ID:**\
-    Provide the project ID you set up earlier
--   **Select the Region:**\
-    Choose one of the supported regions (e.g., `us-east5`)
--   **Select the Model:**\
-    From the available list, choose **Claude 4.5 Sonnet v2**
--   **Save and Test:**\
-    Save your settings and test by sending a simple prompt (e.g., "Generate a Python function to check if a number is prime.")
-
----
+## Setup Steps
 
-### Step 4: Authentication and Credentials Setup
+### 1. Prepare GCP Environment
 
-#### Option A: Using Your Google Account (User Credentials)
+1. **Sign in:** [Google Cloud Console](https://console.cloud.google.com/)
+2. **Create/select project:** Use existing or create new project
+3. **Set up IAM:**
+   - Grant **Vertex AI User** role (`roles/aiplatform.user`)
+   - For service accounts, add **Vertex AI Service Agent** role (`roles/aiplatform.serviceAgent`)
 
-1. **Install the Google Cloud CLI:**\
-   Follow the [installation guide](https://cloud.google.com/sdk/install)
-2. **Initialize and Authenticate:**
+### 2. Choose Region and Enable Models
 
-    ```bash
-    gcloud init
-    gcloud auth application-default login
-    ```
+1. **Select region:** Choose region for latency/compliance needs (e.g., `us-east5`, `us-central1`, `europe-west1`)
+   - Use `global` endpoint for higher availability (Gemini only)
+2. **Enable models:** Go to Vertex AI → Model Garden and enable desired models (e.g., Claude 4.5 Sonnet v2)
 
-    - This sets up Application Default Credentials (ADC) using your Google account
+### 3. Configure CodinIT
 
-3. **Restart VS Code:**\
-   Ensure VS Code is restarted so that the CodinIT extension picks up the new credentials
+1. Install CodinIT extension in VS Code
+2. Click settings icon (⚙️)
+3. Select **GCP Vertex AI** as API Provider
+4. Enter your **Google Cloud Project ID**
+5. Select your **Region**
+6. Choose your **Model** (e.g., Claude 4.5 Sonnet v2)
+7. Save and test
 
-#### Option B: Using a Service Account (JSON Key)
+### 4. Authentication
 
-1. **Create a Service Account:**
+**Option A: User Credentials**
+```bash
+gcloud init
+gcloud auth application-default login
+```
+Restart VS Code after authentication.
 
-    - In the GCP Console, navigate to **IAM & Admin > Service Accounts**
-    - Create a new service account (e.g., "vertex-ai-client")
+**Option B: Service Account**
+1. Create service account in GCP Console
+2. Assign Vertex AI User and Service Agent roles
+3. Generate JSON key
+4. Set environment variable:
+   ```bash
+   export GOOGLE_APPLICATION_CREDENTIALS="/path/to/key.json"
+   ```
+5. Launch VS Code from terminal with this variable set
 
-2. **Assign Roles:**
-
-    - Attach **Vertex AI User** (`roles/aiplatform.user`)
-    - Attach **Vertex AI Service Agent** (`roles/aiplatform.serviceAgent`)
-    - Optionally, add other roles as required
-
-3. **Generate a JSON Key:**
-
-    - In the Service Accounts section, manage keys for your service account and download the JSON key
-
-4. **Set the Environment Variable:**
-
-    ```bash
-    export GOOGLE_APPLICATION_CREDENTIALS="/path/to/your/service-account-key.json"
-    ```
-
-    - This instructs Google Cloud client libraries (and CodinIT) to use this key
-
-5. **Restart VS Code:**\
-   Launch VS Code from a terminal where the `GOOGLE_APPLICATION_CREDENTIALS` variable is set
-
----
-
-### Step 5: Security, Monitoring, and Best Practices
-
-#### 5.1 Enforce Least Privilege
-
--   **Principle of Least Privilege:**\
-    Only grant the minimum necessary permissions. Custom roles can offer finer control compared to broad predefined roles
--   **Best Practices:**\
-    Refer to [GCP IAM Best Practices](https://cloud.google.com/iam/best-practices)
-
-#### 5.2 Manage Resource Access
-
--   **Project vs. Resource-Level Access:**\
-    Access can be managed at both levels. Note that resource-level permissions (e.g., for BigQuery or Cloud Storage) add to, but do not override, project-level policies
-
-#### 5.3 Monitor Usage and Quotas
-
--   **Model Observability Dashboard:**
-
-    -   In the Vertex AI Console, navigate to the **Model Observability** dashboard
-    -   Monitor metrics such as request throughput, latency, and error rates (including 429 quota errors)
-
--   **Quota Management:**
-    -   If you encounter 429 errors, check the **IAM & Admin > Quotas** page
-    -   Request a quota increase if necessary\
-        [Learn more about GCP Vertex AI Quotas](https://cloud.google.com/vertex-ai/quotas)
-
-#### 5.4 Service Agents and Cross-Project Considerations
-
--   **Service Agents:**\
-    Be aware of the different service agents:
-
-    -   Vertex AI Service Agent
-    -   Vertex AI RAG Data Service Agent
-    -   Vertex AI Custom Code Service Agent
-    -   Vertex AI Extension Service Agent
-
--   **Cross-Project Access:**\
-    For resources in other projects (e.g., BigQuery, Cloud Storage), ensure that the appropriate roles (BigQuery Data Viewer, Storage Object Viewer) are assigned
-
----
+## Supported Regions
 
-### Conclusion
+- `us-east5` (Columbus, Ohio)
+- `us-central1` (Iowa)
+- `europe-west1` (Belgium)
+- `europe-west4` (Netherlands)
+- `asia-southeast1` (Singapore)
+- `global` (Global - Gemini only)
 
-By following these steps, your enterprise team can securely integrate GCP Vertex AI with the CodinIT VS Code extension to harness the power of **Claude 4.5 Sonnet v2**:
+## Notes
 
--   **Prepare Your GCP Environment:**\
-    Create or use a project, configure IAM with least privilege, and ensure necessary roles (including the Vertex AI Service Agent role) are attached
--   **Verify Regional and Model Access:**\
-    Confirm that your chosen region supports Claude 4.5 Sonnet v2 and that the model is enabled
--   **Configure CodinIT in VS Code:**\
-    Install CodinIT, enter your project ID, select the appropriate region, and choose the model
--   **Set Up Authentication:**\
-    Use either user credentials (via `gcloud auth application-default login`) or a service account with a JSON key
--   **Implement Security and Monitoring:**\
-    Adhere to best practices for IAM, manage resource access carefully, and monitor usage with the Model Observability dashboard
+- **Cross-region inference:** Check "Cross Region Inference" for models requiring inference profiles
+- **First-time use:** Some models (e.g., Anthropic) require submitting use case form via Console
+- **Permissions:** Minimal required: `bedrock:InvokeModel`, `bedrock:InvokeModelWithResponseStream`
+- **Monitoring:** Use CloudWatch and CloudTrail for logging and monitoring
+- **Security:** Follow [GCP IAM Best Practices](https://cloud.google.com/iam/best-practices)
 
-For further details, please consult the [GCP Vertex AI Documentation](https://cloud.google.com/vertex-ai/docs) and your internal security policies.\
-Happy coding!
+## Resources
 
-_This guide will be updated as GCP Vertex AI and CodinIT evolve. Always refer to the latest documentation for current practices._
+- [GCP Vertex AI Documentation](https://cloud.google.com/vertex-ai/docs)
+- [Access Control](https://cloud.google.com/vertex-ai/general/access-control)
+- [Quotas](https://cloud.google.com/vertex-ai/quotas)