Google Cloud Vertex AI: Managed RAG Solutions

Google Cloud Accelerates on Enterprise RAG

Google Cloud announces major evolutions of its RAG capabilities on Vertex AI. The new RAG Engine simplifies deployments, the Grounding API improves reliability, and integration with Gemini 2.0 offers unmatched performance.

"Vertex AI RAG Engine makes enterprise RAG accessible to everyone," declares Thomas Kurian, CEO of Google Cloud. "No need to be an AI expert to deploy quality solutions."

New Features

RAG Engine

A managed service for end-to-end RAG:

DEVELOPERpython
from google.cloud import aiplatform

# Initialization
aiplatform.init(project="my-project", location="us-central1")

# Create a RAG corpus
rag_corpus = aiplatform.RagCorpus.create(
    display_name="company-docs",
    embedding_model="text-embedding-005",
    chunking_config={
        "strategy": "semantic",
        "chunk_size": 512,
        "overlap": 50
    }
)

# Import documents
rag_corpus.import_files(
    gcs_source="gs://my-bucket/documents/",
    import_config={
        "file_types": ["pdf", "docx", "html"],
        "ocr_enabled": True
    }
)

# RAG query
response = rag_corpus.query(
    text="How to configure the product?",
    model="gemini-2.0-pro",
    retrieval_config={
        "top_k": 5,
        "reranking": True
    }
)

Grounding API

Response validation becomes native:

DEVELOPERpython
from google.cloud import aiplatform

# Grounding configuration
grounding_config = {
    "grounding_source": {
        "type": "RETRIEVAL",
        "retrieval_config": {
            "rag_corpus": rag_corpus.resource_name,
            "threshold": 0.7
        }
    },
    "grounding_enforcement": {
        "level": "STRICT",  # STRICT, MODERATE, PERMISSIVE
        "citation_required": True
    }
}

# Generation with grounding
response = aiplatform.Gemini.generate(
    model="gemini-2.0-pro",
    prompt="Explain the return policy",
    grounding_config=grounding_config
)

# Result with grounding metadata
print(response.grounding_metadata)
# {
#   "grounding_score": 0.92,
#   "citations": [...],
#   "unsupported_claims": []
# }

This feature aligns with our guide on hallucination detection.

Gemini 2.0 Integration

Integration with Gemini 2.0 brings:

DEVELOPERpython
# Multimodal RAG with Gemini 2.0
response = rag_corpus.query(
    inputs=[
        {"type": "text", "value": "Which product matches this image?"},
        {"type": "image", "value": "gs://bucket/product-image.jpg"}
    ],
    model="gemini-2.0-pro-vision",
    multimodal_config={
        "image_understanding": True,
        "cross_modal_retrieval": True
    }
)

Agent Builder RAG

Create RAG agents without code:

1. Visual interface: Drag-and-drop components
2. Pre-configured connectors: Drive, Confluence, Salesforce
3. Workflows: Visual orchestration
4. Deployment: One-click to production

DEVELOPERpython
# Or via API
agent = aiplatform.Agent.create(
    display_name="support-agent",
    rag_corpus=rag_corpus.resource_name,
    instructions="You are a support agent. Respond by citing sources.",
    tools=[
        {"type": "rag_retrieval"},
        {"type": "code_execution"},
        {"type": "web_search"}
    ]
)

# Deploy
agent.deploy(
    endpoint="support-agent-endpoint",
    min_replica_count=1,
    max_replica_count=10
)

Architecture

Recommended Architecture

Cloud Storage / Drive / BigQuery
              ↓
    [RAG Engine - Ingestion]
              ↓
    [Chunking + Embedding]
              ↓
    Vertex AI Vector Search
              ↓
    [Retrieval + Reranking]
              ↓
    [Gemini + Grounding]
              ↓
    Cloud Run / GKE

Native GCP Integration

Performance

Benchmarks

Limits

Pricing

Pricing

Comparison

*For 100K requests/month, 10GB of data

Check our guide on RAG cost optimization.

Use Cases

When to Use Vertex AI RAG

Ideal for:

• GCP-first enterprises
• Need for advanced multimodal
• BigQuery/Data analytics integration
• Critical grounding

Less suitable for:

• Multi-cloud
• Limited budget
• Need for open-source models

Complete Example

DEVELOPERpython
from google.cloud import aiplatform

# 1. Setup
aiplatform.init(project="my-project")

# 2. Create RAG corpus
corpus = aiplatform.RagCorpus.create(
    display_name="knowledge-base",
    embedding_model="text-embedding-005"
)

# 3. Import documents
corpus.import_files(gcs_source="gs://docs/")

# 4. Create an endpoint
endpoint = corpus.deploy_rag_endpoint(
    model="gemini-2.0-pro",
    grounding_config={"level": "STRICT"}
)

# 5. Query
response = endpoint.predict(
    instances=[{"query": "What is the procedure?"}]
)

Our Take

Vertex AI RAG Engine represents a solid option:

Strengths:

• Native GCP integration
• Performant Gemini 2.0
• Unique Grounding API
• Advanced multimodal

Points of attention:

• Google Cloud lock-in
• High cost
• Initial complexity

For GCP-first enterprises, it's a natural choice. Native integration with BigQuery and the Google data ecosystem is a decisive advantage.

Platforms like Ailog offer a cloud-agnostic alternative with French hosting.

Check our guide to best RAG platforms.