vishalmysore

Posted on May 15

Google A2A RAG with Spring, Java, and MongoDB Atlas

This project demonstrates how to build a powerful Retrieval-Augmented Generation (RAG) search application that integrates Google's Agent-to-Agent (A2A) protocol with Spring Boot and MongoDB Atlas. The system enables intelligent task management and semantic search capabilities through vector embeddings.

Code for the project is here

As AI systems move beyond isolated capabilities toward collaborative, autonomous behavior, the convergence of Agent-to-Agent (A2A) protocols with Retrieval-Augmented Generation (RAG) is laying the foundation for a new class of intelligent applications. These systems can not only understand user intent semantically but also delegate, recall, and refine tasks through coordinated agent interactions. The inclusion of vector databases like MongoDB Atlas makes this interaction persistent, retrievable, and context-aware. This architecture is essential for building scalable, explainable AI systems in domains like customer support, DevOps automation, digital assistants, and multi-agent simulations. Looking ahead, we anticipate the rise of decentralized, interoperable AI ecosystems where agents can communicate, collaborate, and learn — making architectures like this one not just valuable, but inevitable

A2AJava is the Java implementation of the Google A2A protocol you can check the project here .

🌟 Key Features

A2A Protocol Integration: Seamless communication between AI agents
Vector-Based Search: Powered by MongoDB Atlas for semantic task retrieval
Real-time Task Storage: Automatic storage of A2A tasks in vector database
RESTful API: Easy-to-use endpoints for task management
Spring Boot Framework: Built with enterprise-grade Java technology
Extensible Architecture: Adaptable for various use cases

🛠️ Technology Stack

Backend: Spring Boot 3.x
Database: MongoDB Atlas with Vector Search
Protocol: Google A2A (Agent-to-Agent)
Search: Vector-based semantic search
API Documentation: OpenAPI/Swagger

📝 How It Works

Vector Storage Integration

The system automatically stores A2A tasks in MongoDB's vector store through a preprocessing hook:

@Override
public void preProcessing(String method, Object params) {
    vectorService.ragStorage(method, params);
}

Task Management

The service handles various A2A task types:

Task sending and querying
Task subscriptions
Push notifications
Task cancellations
Resubscriptions

Here's a brief overview of the task storage mechanism:

public void ragStorage(String method, Object params) {
    switch (method) {
        case "tasks/send":
            addTask(new ObjectMapper().convertValue(params, TaskSendParams.class));
            break;
        case "tasks/get":
            addTask(new ObjectMapper().convertValue(params, TaskQueryParams.class));
            break;
        // Additional task types handled...
    }
}

📸 Visual Guide

1. Agent Setup

Adding an A2A agent to your client application

2. Chat Interface

Example of normal chat interaction

3. Data Storage

Task data stored in MongoDB Atlas

4. Event Monitoring

Event tracking and monitoring interface

🔍 Using the API

Task Retrieval Example

GET http://localhost:8080/getTask?name="cricket"

This endpoint demonstrates the semantic search capabilities, retrieving tasks related to the given query using vector similarity.

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