Spring AI + RAG

Introduction

Welcome to our RAG Hack livestream! RAG Hack is a free global hackathon taking place from September 3rd to 16th, centered around the concept of Retrieval-Augmented Generation (RAG), which allows large language (LLM) models to generate responses based on specific datasets. With over 25 live streams dedicated to teaching participants how to build RAG applications using Microsoft technologies, as well as a chance to win 10 cash prizes for the best apps in each category, we are excited to have you join us!

Introduction

In today's session, Mark Heckler, Principal Cloud Advocate for Java and JVM languages at Microsoft, explores RAG's power through a practical programming demonstration. Mark emphasizes that while the landscape of AI evolves rapidly, foundational concepts—such as large language models and retrieval augmentation—remain vital.

Overview of Key Topics

1. Artificial Intelligence (AI) Concepts: AI is continuously evolving; thus, it's crucial to stay updated. Key discussions include the workings of large language models, the concept of embeddings, and the importance of a solid data framework to leverage AI effectively.

2. RAG Value Proposition: RAG allows organizations to harness their data effectively. For example, AI can search documents rapidly, providing insights from potentially inaccessible data. This revolutionizes how organizations manage and utilize information.

3. Implementation Options: Mark dives into practical coding using Java and Spring AI, showing participants how to build a Spring Boot application. He emphasizes the tools available and demonstrates how easy it can be to integrate AI into applications, specifically focusing on Chroma for vector storage and OpenAI for generating responses based on queries.

Coding Demonstration

Mark showcases a live coding session where he develops an application that interacts with OpenAI’s API and populates a vector database using document readers (like Tika). This project serves as a fundamental blueprint for creating applications that can answer domain-specific questions through a RAG approach.

1. Setting Up Dependencies: By utilizing tools such as Spring Boot Starter Web, Chroma, and Spring AI OpenAI, the project begins to take shape, allowing for a robust API to process requests.

2. Handling Queries: Code examples are provided to illustrate how to set up endpoints for both general queries and document-specific queries, effectively transforming how users interact with vast datasets.

3. Interactive Features: Mark introduces additional functionalities, such as multi-modal responses, enhancing the user experience by integrating image analysis with text-based queries.

4. Real-World Applications: As an illustrative example, Mark discusses how pilots can query aviation-related queries using rich datasets that they've integrated, showcasing the practicality of RAG applications in specialized domains.

Conclusion

Mark ends the session by summarizing the potential of using RAG to leverage data efficiently, encouraging attendees to continuously engage with the rapidly changing AI landscape. He shares links to resources and code repositories for participants to further their learning and experimentation in the RAG domain.

Keyword

• Spring AI
• RAG (Retrieval-Augmented Generation)
• Large Language Models (LLMs)
• OpenAI
• Embeddings
• Chroma
• Document Reader
• API
• Vector Store
• AI Applications

FAQ

Q1: What is Retrieval-Augmented Generation (RAG)?
A1: RAG is a technique that enhances the ability of large language models (LLMs) to generate responses based on specific datasets or documents.

Q2: How does Spring AI integrate with RAG applications?
A2: Spring AI provides a framework to build applications that utilize AI capabilities, with features that simplify dependency management and coding efficiencies.

Q3: What programming languages are supported in building RAG apps?
A3: This tutorial primarily focuses on Java, but RAG principles can be applied to various programming languages.

Q4: How can I participate in RAG Hack?
A4: Participants can register at the provided link and start developing their own RAG applications to be submitted for the competition by September 16th.

Q5: Are there resources available for learning more about RAG?
A5: Yes, Mark provided several useful links during the session that participants can use to further explore RAG and Spring AI capabilities.