Setup with Google Cloud (Vertex AI)

Time needed: ~15-20 minutes Difficulty: Advanced — requires a Google Cloud project When to use: You want EU data residency (when available), GCP billing integration, or enterprise controls.

Most users should use the Gemini API Key setup instead. It takes 2 minutes and uses the same AI model. This guide is for advanced users who need GCP-specific features.

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Prerequisites

• A Google account
• A credit card for Google Cloud billing (you won’t be charged until you exceed free tier)

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Step 1: Create a Google Cloud Project

1. Go to Google Cloud Console
2. Sign in with your Google account
3. Click the project dropdown at the top → “New Project”
4. Name it (e.g., “golden-retriever”) → click “Create”
5. Make sure your new project is selected in the dropdown

[Screenshot placeholder: GCP Console project creation dialog]

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Step 2: Enable billing

1. Go to Billing
2. Link a billing account to your project
3. If you don’t have a billing account, create one (requires credit card)

[Screenshot placeholder: Billing account linking page]

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Step 3: Enable required APIs

You need two APIs enabled:

Vertex AI API

1. Go to API Library
2. Search for “Vertex AI API”
3. Click it → click “Enable”

[Screenshot placeholder: Vertex AI API page with Enable button]

Cloud Storage JSON API

1. Back in the API Library, search for “Cloud Storage JSON API”
2. Click it → click “Enable”

[Screenshot placeholder: Cloud Storage JSON API page with Enable button]

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Step 4: Create a Cloud Storage bucket

1. Go to Cloud Storage
2. Click “Create”
3. Name your bucket (e.g., “golden-retriever-yourname”)
4. Choose a region:
   • us-central1 — Recommended (lowest latency for embeddings)
   • europe-west1 — For EU data at rest (note: embedding model currently only runs in US)
5. Leave other settings as default → click “Create”
6. Note your bucket name — you’ll need it in Step 6

[Screenshot placeholder: Bucket creation dialog with name and region fields]

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Step 5: Create OAuth credentials

Configure the consent screen (first time only)

1. Go to OAuth Consent Screen
2. Choose “External” → click “Create”
3. Fill in:
   • App name: Golden Retriever
   • User support email: your email
   • Developer contact email: your email
4. Click “Save and Continue” through Scopes (no changes needed)
5. Under Test Users, click “Add Users” → add your email
6. Click “Save and Continue” → “Back to Dashboard”

[Screenshot placeholder: OAuth consent screen configuration]

Create the client credentials

1. Go to Credentials
2. Click ”+ Create Credentials” → “OAuth client ID”
3. Application type: “Desktop app”
4. Name: “Golden Retriever”
5. Click “Create”
6. A dialog appears with your Client ID and Client Secret — copy both

[Screenshot placeholder: OAuth client ID creation, highlighting Desktop app selection]

[Screenshot placeholder: The credentials dialog showing Client ID and Client Secret with copy buttons]

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Step 6: Configure Golden Retriever

1. Launch Golden Retriever
2. On the setup wizard, click “Google Cloud (Vertex AI)”
3. The GCP Setup Wizard opens with 4 phases:

Phase 1: Credentials

• Paste your Client ID
• Paste your Client Secret
• Click “Sign In & Continue”
• Your browser opens for Google sign-in — authorize the app

[Screenshot placeholder: GCP wizard Phase 1 with credential fields]

Phase 2: Project Configuration

• Enter your GCP Project ID (from Step 1, e.g., “golden-retriever”)
• Enter your Bucket name (from Step 4)
• Select your Region from the dropdown
• Click “Validate Project”

[Screenshot placeholder: GCP wizard Phase 2 with project config fields]

Phase 3: Validation

• The wizard automatically checks:
  • OAuth token validity
  • Vertex AI API enabled
  • Cloud Storage API enabled
  • Bucket accessibility
  • Embedding endpoint reachability
• All checks should show green checkmarks
• Click “Continue”

[Screenshot placeholder: GCP wizard Phase 3 showing validation results]

Phase 4: Complete

• Summary of your configuration
• Click “Done”

[Screenshot placeholder: GCP wizard Phase 4 completion screen]

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Costs

With Vertex AI, costs appear on your Google Cloud bill:

Service	Cost
Vertex AI embeddings	~$0.20 per million tokens
Cloud Storage	~$0.023/GB/month
Vertex AI Q&A (Gemini 2.5 Flash)	~$0.15 per million input tokens

For a typical personal knowledge base (a few hundred documents), expect under $1/month.

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Privacy & Data Residency

• Your library stays on your Mac. Original files are not uploaded to GCS by default — the GCS upload step is opt-in.
• Content is sent to Vertex AI for AI features. To make search, transcription, description, and Q&A work, the relevant content of your files (document text, audio bytes, image bytes, video bytes) is sent through your GCP project’s Vertex AI endpoint. Golden Retriever (the company) doesn’t see this traffic — it goes directly from your Mac to Google through your project’s auth.
• Region pinning: You choose the processing region. However, gemini-embedding-2-preview is currently only available in us-central1. When Google adds EU regions, your Vertex AI setup will automatically use them.
• GCP data controls: You get full IAM, VPC-SC, and organization-level controls through your GCP project.

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Troubleshooting

Problem	Solution
”Vertex AI API not enabled”	Go to API Library and enable it (Step 3)
“Bucket not found”	Check the bucket name — it must match exactly
”OAuth token invalid”	Click “Sign Out” in Settings → Cloud, then re-authenticate
”Permission denied”	Your Google account may not have the right IAM roles. You need at minimum: Vertex AI User + Storage Object Admin
3 Keychain prompts on launch	Known issue (#34) — click “Always Allow” for each prompt

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Switching to Gemini API Key

If you want to simplify your setup:

1. Open Settings → Cloud tab
2. Click “Switch to Gemini API Key”
3. Follow the Gemini API Key setup guide

Your existing embeddings and search data will continue to work — both paths use the same model.