Prashant - Blogspot

  Overview With Spring AI Multi-Agent RAG, I wanted to explore how AI capabilities can be integrated into a Java and Spring-based backend in a way that makes sense for enterprise teams. Instead of forcing organizations to jump to a completely different ecosystem just to work with LLMs, this project shows that existing Spring Boot applications, Java teams, and enterprise architectures can adopt AI capabilities directly within the stack they already use. A lot of AI tooling still assumes teams are ready to rebuild around Python-first frameworks. That is fine for experimentation, but it is not how most enterprise systems work. The practical question is not whether AI can be built in Python. Of course it can. The real question is whether existing enterprise teams can add AI capabilities without abandoning the stack, language, and operational model they already trust. ·         Java ·         Spring Boot ·...

Spring AI : Multi-Provider AI Chat  Overview I recently published a small hands-on project to explore how to build AI capabilities in Java using Spring Boot and Spring AI. Project links: Project Description Git Hub  Explore different options using spring ai chatclient GitHUB Implementing Tools calling concept with spring ai GitHUB I’ve kept the repository README detailed so anyone can run and test the APIs quickly. If you’re learning Spring AI and want a compact reference project, feel free to explore, fork, or share feedback. Why Spring AI? Most AI examples today are Python-first, which makes sense from a research perspective. But in many enterprise environments, core systems are still Java-based. Spring AI brings AI integration into the familiar Spring ecosystem. That means: Dependency injection and clean configuration Provider abstraction (switch models without rewriting business log...

 Overview In the digital age, we can use the power of generative AI to build custom innovative solutions by enabling users to interact with their documents or websites through natural language conversations. This blog-post focuses on a simple use-case where the user can chat/query an uploaded PDF, JSON data or an existing website with natural language conversations using RAG(Retrieval-Augmented Generation). The entire stack is based on open source technologies and can be implemented without having to pay a dime. Tech stack: Node js - Popular Backend framework based on Javascript Langchain JS   - Javascript based framework for developing applications powered by language models Cohere - Open source LLM model Mongo DB Atlas -  an integrated suite of data services centered around a cloud database which can also store vector embeddings. Pre-Requisites Node JS -  Install version 18 and above. Cohere API Key - Sign up to Cohere and procure an API Key (free of cost) Mongo ...