GenAI Solutions with SpringBoot Rest API + AI Agent +GenAI Service+RAG

Use Case Title: "Smart Banking Virtual Assistant" Business Scenario

A digital banking assistant for customers, powered by Generative AI and AI agents, to:

• Answer banking FAQs (e.g., interest rates, loan options)

• Retrieve user-specific data securely (e.g., account balance, transaction history)

• Automate service requests (e.g., card block, checkbook request)

• Escalate complex queries to human agents

Technology Stack

Layer	Tools
Backend	Spring Boot (REST API)
AI Layer	GenAI (OpenAI/GPT or Azure OpenAI)
Agent Layer	LangChain/AgentGPT or custom Python/Java agent
Data Retrieval (RAG)	Pinecone / FAISS + Document loaders (PDFs, CRM exports)
Auth & Security	OAuth2, JWT, Spring Security
Frontend	React + Tailwind (chat UI)
Containerization	Docker
Documentation	Swagger (OpenAPI)

High-Level Architecture

+-----------------+     +---------------------+     +----------------+

| Frontend (UI) | --> | Spring Boot Backend | --> | GenAI Service |

+-----------------+ +---------------------+ +----------------+

| |

v v

+------------------------+

| AI Agent / LangChain |

+------------------------+

|

+------------------------+

| RAG Layer (VectorDB) |

+------------------------+

|

+------------------------+

| Core Banking APIs |

+------------------------+

AI Agent Capabilities

• Uses tools like BankingAPIWrapper, CustomerDataRetriever, and EscalationTool.

• Can break a task into subtasks (e.g., verify KYC → fetch balance → suggest credit card upgrade).

• Uses RAG to ground answers in banking documents like product manuals, T&Cs, RBI regulations.

Sample Workflow

1. Customer Input:"Can I get a loan pre-approved based on my current salary?"

2. Spring Boot:

   • Validates input

   • Routes request to the agent orchestrator

3. AI Agent:

   • Calls GenAI to classify intent: "loan eligibility"

   • Fetches relevant policy docs from Vector DB (RAG)

   • Calls banking API to get user salary & history

   • Generates personalized response

4. Response:

   • "Based on your salary of ₹95,000/month and credit score of 750, you're pre-approved for a ₹10L personal loan. Would you like to proceed?"

Swagger Example Endpoint

POST /api/assistant/ask

Request:

{

"userId": "12345",

"question": "How much can I get as a personal loan?"

}

Response:

{

"response": "Based on your salary and credit history, you may be eligible for ₹10L. Tap here to apply."

}

Docker Setup

Dockerfile

FROM openjdk:17

COPY target/smartbanking.jar app.jar

ENTRYPOINT ["java", "-jar", "/app.jar"]

Enhancements

• Multi-agent collaboration (e.g., KYC Agent, Loan Agent)

• Integration with voice assistants (e.g., Alexa, Google Assistant)

• Fine-tuned models on bank-specific data

• Auditing and Explainability logs for compliance