Google Vertex AI
Google Vertex AI
Start with meaning, then move to detail.
This lesson explains Google Vertex AI as part of Hermes internals and extension points. You will learn what it does, when it matters, and the smallest safe test that proves it works.
If you are new, do not memorize names. Focus on three questions: what problem does this solve, what access does it need, and how can you verify the result?
For practice, inspect the first example, identify its effects, run it on test data, and compare the result with the source claim.
For advanced readers, inspect Prerequisites, Quick Start, Configuration, then verify failure modes and version compatibility.
Know Python, Git, and basic project structure before changing code.
A clear outcome before you read.
- Understand Google Vertex AI without assumed prior knowledge.
- Separate the source description from what still needs testing in your environment.
- Read the first command and identify its inputs and outputs before copying it.
Short definitions before the details.
- Provider
- The service that runs or provides access and authentication to a model.
- Session & memory
- A session holds conversation context, while memory keeps selected facts that should persist.
Use Hermes Agent with Gemini on Google Cloud Vertex AI — OAuth2 service account or ADC, GCP billing and quotas, no static API key
What does the source say, and in what order?
- 01Prerequisites
Start here to understand the core idea or structure.
- 02Quick Start
Read this after the foundation, then connect it to the previous step.
- 03Configuration
Read this after the foundation, then connect it to the previous step.
- 04How authentication works
Read this after the foundation, then connect it to the previous step.
- 05Available Models
Read this after the foundation, then connect it to the previous step.
- 06Switching Models Mid-Session
Read this after the foundation, then connect it to the previous step.
- 07Reasoning / Thinking
Read this after the foundation, then connect it to the previous step.
- 08Diagnostics
Read this after the foundation, then connect it to the previous step.
- 09Troubleshooting
Read this after the foundation, then connect it to the previous step.
- 10"Vertex AI credentials could not be resolved"
Finish here to verify the result and special cases.
Copy only after you understand the effect.
# Option A — service account JSON (recommended for servers / gateways)
echo "VERTEX_CREDENTIALS_PATH=/path/to/service-account.json" >> ~/.hermes/.env
# Option B — Application Default Credentials (good for local dev)
gcloud auth application-default login
# Select Vertex as your provider
hermes model
# → Choose "More providers..." → "Google Vertex AI"
# → Enter your GCP project ID (or leave blank to use the one in your credentials)
# → Choose a region (default: global)
# → Select a Gemini model
# Start chatting
hermes chat# One of these (checked in this order); omit both to use ADC:
VERTEX_CREDENTIALS_PATH=/path/to/service-account.json
GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.jsonmodel:
default: google/gemini-3-flash-preview
provider: vertex
vertex:
project_id: my-gcp-project # blank → use the project embedded in the credentials
region: global # "global" is required for the Gemini 3.x previewsRead the first command and identify its inputs and outputs before copying it.
Match every command to your installed Hermes version, review the files and accounts it can reach, and use non-sensitive data for the first test. If this explanation differs from the source, the official source wins.