Vertex AI RAG Agent with ADK

This repository contains a Google Agent Development Kit (ADK) implementation of a Retrieval Augmented Generation (RAG) agent using Google Cloud Vertex AI.

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

Prerequisites

• A Google Cloud account with billing enabled
• A Google Cloud project with the Vertex AI API enabled
• Appropriate access to create and manage Vertex AI resources
• Python 3.9+ environment

Setting Up Google Cloud Authentication

Before running the agent, you need to set up authentication with Google Cloud:

1. Install Google Cloud CLI:

   • Visit Google Cloud SDK for installation instructions for your OS

2. Initialize the Google Cloud CLI:

   gcloud init

   This will guide you through logging in and selecting your project.

3. Set up Application Default Credentials:

   gcloud auth application-default login

   This will open a browser window for authentication and store credentials in: ~/.config/gcloud/application_default_credentials.json

4. Verify Authentication:

   gcloud auth list
   gcloud config list

5. Enable Required APIs (if not already enabled):

   gcloud services enable aiplatform.googleapis.com

Installation

1. Set up a virtual environment:

   python -m venv .venv
   source .venv/bin/activate  # On Windows: .venv\Scripts\activate

2. Install Dependencies:

   pip install -r requirements.txt

Using the Agent

The agent provides the following functionality through its tools:

1. Query Documents

Allows you to ask questions and get answers from your document corpus:

• Automatically retrieves relevant information from the specified corpus
• Generates informative responses based on the retrieved content

2. List Corpora

Shows all available document corpora in your project:

• Displays corpus names and basic information
• Helps you understand what data collections are available

3. Create Corpus

Create a new empty document corpus:

• Specify a custom name for your corpus
• Sets up the corpus with recommended embedding model configuration
• Prepares the corpus for document ingestion

4. Add New Data

Add documents to existing corpora or create new ones:

• Supports Google Drive URLs and GCS (Google Cloud Storage) paths
• Automatically creates new corpora if they don't exist

5. Get Corpus Information

Provides detailed information about a specific corpus:

• Shows document count, file metadata, and creation time
• Useful for understanding corpus contents and structure

6. Delete Corpus

Removes corpora that are no longer needed:

• Requires confirmation to prevent accidental deletion
• Permanently removes the corpus and all associated files

Troubleshooting

If you encounter issues:

• Authentication Problems:

  • Run gcloud auth application-default login again
  • Check if your service account has the necessary permissions

• API Errors:

  • Ensure the Vertex AI API is enabled: gcloud services enable aiplatform.googleapis.com
  • Verify your project has billing enabled

• Quota Issues:

  • Check your Google Cloud Console for any quota limitations
  • Request quota increases if needed

• Missing Dependencies:

  • Ensure all requirements are installed: pip install -r requirements.txt

Additional Resources

• Vertex AI RAG Documentation
• Google Agent Development Kit (ADK) Documentation
• Google Cloud Authentication Guide