Vertex AI RAG Agent

A Retrieval Augmented Generation (RAG) agent built with Google's Agent Development Kit (ADK) and Vertex AI. Query your documents using natural language and get AI-powered answers.

Overview

The Vertex AI RAG Agent allows you to:

• Query document corpora with natural language questions
• List available document corpora
• Create new document corpora
• Add new documents to existing corpora
• Get detailed information about specific corpora
• Delete corpora when they're no longer needed

🏗️ Architecture

flowchart TB
    User["👤 User"]
    Agent["🤖 RAG Agent"]
    Tools["🔧 Python Tools"]
    VertexRAG["☁️ Vertex AI RAG API"]
    
    User --> Agent
    Agent --> Tools
    Agent --> VertexRAG

Loading

🚀 Getting Started

What You'll Need

• ✅ Google Cloud Account with billing enabled
• ✅ Google Cloud Project with Vertex AI API enabled
• ✅ Python 3.9+ and gcloud CLI installed
• ✅ IAM permissions for Vertex AI resources

Setup

1. Authenticate with Google Cloud

gcloud auth application-default login
gcloud services enable aiplatform.googleapis.com

2. Install Dependencies

pip install -r requirements.txt

3. Configure Your Project

Create a .env file with your project details:

cp .env.example .env

Edit .env:

GOOGLE_CLOUD_PROJECT=your-project-id
GOOGLE_CLOUD_LOCATION=us-central1

4. Run the Agent

adk web

Done! 🎉 Your RAG agent is ready.

🔧 What Can the Agent Do?

Tool	Purpose
🔍 rag_query	Query documents with natural language
📋 list_corpora	List all document collections
➕ create_corpus	Create a new document collection
📄 add_data	Add documents from Drive/GCS
ℹ️ get_corpus_info	Get corpus details
🗑️ delete_document	Remove specific documents
🗑️ delete_corpus	Delete entire corpus

---

💻 Usage Examples

Running the Agent

Start the agent interface:

# Web interface (recommended)
adk web

# Command line interface
adk run

Common Workflows

1. Create and Query a Knowledge Base

Create a corpus called 'product_docs'

Add these documents to product_docs:
- https://drive.google.com/file/d/abc123/view
- gs://my-bucket/docs/manual.pdf

What are the main features of the product?

2. Search Across Documents

What does the documentation say about installation?

3. Manage Your Collections

List all available corpora

Show me details about the product_docs corpus

Delete the old_corpus corpus

Real-World Use Cases

• 📚 Knowledge Base - Company documentation search
• 🔬 Research Assistant - Query academic papers
• 💬 Customer Support - Answer questions from support docs
• 📋 Legal Analysis - Search contracts and legal documents

⚙️ Advanced Configuration

Customize RAG settings in rag_agent/config.py:

Setting	Description	Default
DEFAULT_CHUNK_SIZE	Token size for document chunks	512
DEFAULT_CHUNK_OVERLAP	Overlap between chunks	100
DEFAULT_TOP_K	Number of results to retrieve	3
DEFAULT_DISTANCE_THRESHOLD	Similarity threshold	0.5
DEFAULT_EMBEDDING_MODEL	Embedding model to use	text-embedding-005

🔐 Authentication

Uses Google Cloud Application Default Credentials (ADC). Run gcloud auth application-default login before starting the agent.

Required IAM Permissions:

• aiplatform.ragCorpora.* (create, delete, get, list, update)
• storage.objects.get (for GCS access)
• drive.files.read (for Google Drive access)

📚 Documentation

• Vertex AI RAG Docs - Official Vertex AI documentation
• Google ADK Docs - Agent Development Kit documentation