Architecture, Tech Strategy, Solution Implementation using Spring Boot, Docker, Swagger, RAG,

🔷 Architecture

You're designing a modular, scalable system that integrates:

• Spring Boot as the backend framework

• RAG (Retrieval-Augmented Generation) for grounding GenAI responses in internal data

• A microservices or layered architecture, likely with clear API boundaries

• Components for authentication, data ingestion, vector DBs, and LLM integration

🔷 Tech Strategy

You're aligning the solution with strategic goals:

• Security-first approach (especially for BFSI)

• Cloud-native deployment, possibly hybrid or multi-cloud

• Leveraging open-source tech for flexibility and cost-efficiency

• Incorporating GenAI responsibly (bias handling, auditability, explainability)

🔷 Implementation

You're putting it all into practice:

• Spring Boot services with REST APIs (documented using Swagger/OpenAPI)

• Docker for containerization and easy environment setup

• Integrating a RAG pipeline with:

  • Document ingestion

  • Chunking and embedding (e.g., using LangChain, Haystack)

  • Vector DB (like FAISS, Pinecone, or ChromaDB)

  • LLM API calls (OpenAI, Azure OpenAI, or open-source models)

• Possibly using Kafka, Redis, or PostgreSQL for supporting services