Spring Intelligence

Spring AI Course

Welcome to the Spring AI course! This course is designed to teach you how to effectively converse with large language models (LLMs) using various techniques and frameworks. Below is an outline of the topics we will cover:

Course Outline

1. Syntax for Conversing with LLMs

Learn the basics of how to structure your inputs to get the best responses from LLMs. This includes understanding:

• Prompt Engineering: Crafting effective prompts to elicit the desired responses.
• Token Limits: Managing the number of tokens to stay within the model's constraints.

2. Prompt Stuffing

Understand how to use prompt stuffing to include necessary context within the prompt to get more accurate and relevant responses.

• Definition and Use Cases
• Techniques for Effective Prompt Stuffing
• Examples

3. Output Parsers

Explore methods to parse the output from LLMs to make it useful for your applications.

• Parsing JSON Responses
• Extracting Specific Information
• Using Output Parsers in Practice

4. Retrieval-Augmented Generation (RAG)

Learn about RAG and how to implement it using vector stores and embeddings to improve the performance and accuracy of LLM responses.

• Introduction to RAG
• Vector Stores
• Embeddings
• Implementing RAG

5. Spring AI Functions

Get to know the functions provided by Spring AI to enhance your AI applications.

• Spring AI Overview
• Key Functions and Their Uses
• Integrating Spring AI Functions into Your Workflow

6. Going Multimodal

Discover how to expand the capabilities of LLMs by going multimodal, incorporating text, images, and other data types.

• Introduction to Multimodal AI
• Combining Text and Images
• Use Cases and Applications

Additional Resources

For further reading and reference, check out these links:

• Spring Documentation
• Spring AI Documentation
• Conceptual Understanding of LLMs
• RAG and Vector Stores

Spring AI Functions: Detailed Explanation

Spring AI provides a robust framework to integrate AI functionalities into your applications. Below are some key functions and how they can be used:

Spring AI Overview

Spring AI simplifies the process of incorporating AI models into your applications, providing tools and frameworks to manage model lifecycle, inference, and deployment.

Key Functions

• Model Management: Functions to load, train, and deploy AI models.
• Inference APIs: Simplified APIs to get predictions from models.
• Data Handling: Tools to preprocess and manage input data for models.
• Performance Monitoring: Functions to monitor and optimize model performance.

Integrating Spring AI Functions into Your Workflow

Integrate Spring AI functions seamlessly into your existing Spring applications:

import org.springframework.ai.ModelLoader;
import org.springframework.ai.InferenceService;

public class AiApplication {
    public static void main(String[] args) {
        // Load a pre-trained model
        ModelLoader modelLoader = new ModelLoader("path/to/model");
        
        // Perform inference
        InferenceService inferenceService = new InferenceService(modelLoader);
        String result = inferenceService.predict("Your input data");
        
        // Output the result
        System.out.println("Model Prediction: " + result);
    }
}