Google Vertex AI

Integrate Google Vertex AI models through the Adaline Proxy for automatic telemetry and observability.

Google Vertex AI

Integrate Google Vertex AI models through the Adaline Proxy to automatically capture telemetry — requests, responses, token usage, latency, and costs — with minimal code changes. Vertex AI provides enterprise-grade access to Gemini models through your Google Cloud project.

Supported Models

Chat Models

Model	Description
gemini-3.1-pro-preview	Latest Gemini 3.1 Pro preview
gemini-3-pro-preview	Gemini 3 Pro preview
gemini-3-flash-preview	Gemini 3 Flash preview
gemini-2.5-pro	Gemini 2.5 Pro
gemini-2.5-pro-preview-03-25	Gemini 2.5 Pro March 2025 preview
gemini-2.5-flash	Gemini 2.5 Flash
gemini-2.5-flash-preview-04-17	Gemini 2.5 Flash April 2025 preview
gemini-2.5-flash-lite	Lightweight Gemini 2.5
gemini-2.5-flash-lite-preview-09-2025	Gemini 2.5 Flash Lite September 2025 preview
gemini-2.0-flash	Fast Gemini 2.0 model
gemini-2.0-flash-exp	Experimental Gemini 2.0 Flash
gemini-2.0-flash-lite	Lightweight Gemini 2.0
gemini-1.5-pro	Gemini 1.5 Pro, 1M token context
gemini-1.5-pro-latest	Gemini 1.5 Pro latest
gemini-1.5-pro-002	Gemini 1.5 Pro version 002
gemini-1.5-pro-001	Gemini 1.5 Pro version 001
gemini-1.5-flash	Gemini 1.5 Flash
gemini-1.5-flash-latest	Gemini 1.5 Flash latest
gemini-1.5-flash-002	Gemini 1.5 Flash version 002
gemini-1.5-flash-001	Gemini 1.5 Flash version 001

Embedding Models

Model	Description
text-embedding-004	Latest text embedding model
text-multilingual-embedding-002	Multilingual embedding model
textembedding-gecko@003	Gecko embedding model
textembedding-gecko-multilingual@001	Multilingual Gecko

Proxy Base URL

https://gateway.adaline.ai/v1/vertex

Prerequisites

1. A Google Cloud project with Vertex AI API enabled
2. GCP credentials configured (application default credentials or service account)
3. An Adaline API key, project ID, and prompt ID

Chat Completions

Complete Chat

Python

from google import genai
from google.genai import types

client = genai.Client(
    http_options={
        "base_url": "https://gateway.adaline.ai/v1/vertex",
        "headers": {
            "adaline-api-key": "your-adaline-api-key",
            "adaline-project-id": "your-project-id",
            "adaline-prompt-id": "your-prompt-id",
        },
    },
    vertexai=True,
    project="your-gcp-project-id",
    location="us-central1",
)

response = client.models.generate_content(
    model="gemini-1.5-pro",
    contents="What are the advantages of using Google Cloud for AI workloads?",
    config=types.GenerateContentConfig(
        http_options=types.HttpOptions(
            headers={
                "adaline-trace-name": "vertex-chat-completion"  # Optional
            }
        )
    )
)

print(response.text)

Vertex AI uses vertexai=True and requires a GCP project and location, unlike Google AI Studio which only needs an API key. Headers are passed via http_options.

Stream Chat

Python

from google import genai
from google.genai import types

client = genai.Client(
    http_options={
        "base_url": "https://gateway.adaline.ai/v1/vertex",
        "headers": {
            "adaline-api-key": "your-adaline-api-key",
            "adaline-project-id": "your-project-id",
            "adaline-prompt-id": "your-prompt-id",
        },
    },
    vertexai=True,
    project="your-gcp-project-id",
    location="us-central1",
)

stream = client.models.generate_content_stream(
    model="gemini-1.5-pro",
    contents="Explain the concept of serverless computing in detail.",
    config=types.GenerateContentConfig(
        http_options=types.HttpOptions(
            headers={
                "adaline-trace-name": "vertex-stream-chat"  # Optional
            }
        )
    )
)

for chunk in stream:
    if hasattr(chunk, 'text') and chunk.text:
        print(chunk.text, end="")

Embeddings

Python

from google import genai
from google.genai import types

client = genai.Client(
    http_options={
        "base_url": "https://gateway.adaline.ai/v1/vertex",
        "headers": {
            "adaline-api-key": "your-adaline-api-key",
            "adaline-project-id": "your-project-id",
            "adaline-prompt-id": "your-prompt-id",
        },
    },
    vertexai=True,
    project="your-gcp-project-id",
    location="us-central1",
)

response = client.models.embed_content(
    model="text-embedding-004",
    contents="The quick brown fox jumps over the lazy dog",
    config=types.EmbedContentConfig(
        http_options=types.HttpOptions(
            headers={
                "adaline-trace-name": "vertex-embedding"  # Optional
            }
        )
    )
)

Google AI Studio vs Vertex AI

Feature	Google AI Studio	Vertex AI
Authentication	API key	GCP credentials (IAM)
Proxy base URL	gateway.adaline.ai/v1/google	gateway.adaline.ai/v1/vertex
SDK parameter	api_key="..."	vertexai=True, project="...", location="..."
Best for	Prototyping, personal projects	Enterprise, production workloads
Data residency	No control	Region-specific

Next Steps

• Multi-Step Workflows — RAG pipelines, multi-step generation, and conversational agents
• Headers Reference — Complete header documentation

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