LLM and AI-Infused Applications with Java and Spring AI
The llm-apps-java-spring-ai project offers a collection of samples demonstrating the integration of Java applications with Generative AI and Large Language Models (LLMs) using the Spring AI framework. These samples serve as a practical guide for developers looking to leverage advanced AI technologies in their Java projects.
Pre-Requisites
To work with the samples provided in this project, users should have:
- Java 23
- Docker or Podman for containerization
Use Cases
The project showcases a wide variety of use cases involving LLMs:
- Chatbot: Implements conversational agents using LLMs through a platform called Ollama.
- Question Answering: Utilizes LLMs to answer questions based on documents using retrieval-augmented generation techniques via Ollama and PGVector.
- Semantic Search: Employing LLMs to perform context-aware searches via Ollama and PGVector.
- Structured Data Extraction: Extracts structured data from unstructured text using LLMs via Ollama.
- Text Classification: Categorizes text data with the help of LLMs through Ollama.
Chat Completion Models
This section explores various chat completion models:
- Mistral AI, Ollama, and OpenAI offer different methods of generating text using their respective platforms.
- Multiple provider options allow for flexibility in choosing the right provider for specific text-generation needs.
Prompts, Messages, and Templates
Using structured messages, templates, and multimodal prompts, this section demonstrates how Ollama and OpenAI models can generate contextually rich outputs for enhanced user interaction.
Structured Output
Two primary projects focus on converting LLM-generated outputs into structured formats like JSON and Java objects, accelerating integration with existing systems and workflows.
Multimodality
Multimodality allows developers to include various media forms—such as text, images, and videos—in a single prompt, enhancing the richness of the interactions. This is made possible through Ollama and OpenAI platforms.
Function Calling
An essential feature of integrating LLMs is the ability to perform function calls, and this aspect is explored using Mistral AI, Ollama, and OpenAI, allowing for dynamic computing and utility within applications.
Embedding Models
This includes transforming text into vector representations, known as embeddings. These vectors enable complex computations and comparisons, and samples are provided through platforms like Mistral AI, Ollama, OpenAI, and ONNX Transformers.
Data Ingestion
This section helps in preparing and vectorizing data for use with LLMs. It includes reading and processing JSON, PDF, and text documents with Ollama and enriching their retrieval capabilities with metadata for improved search and retrieval.
Upcoming Features
The project also hints at future enhancements including vector stores, memory, moderation models, and evaluation metrics—all crucial for creating robust, AI-enhanced Java applications.
Observability and Evaluation
Efforts towards observability ensure that LLM operations are transparent and measurable, crucial for maintaining production-grade systems.
Integration with Images and Audio
Examples of integrating image and audio models are provided, showing how to generate images and transcribe speech using LLMs through OpenAI.
References and Additional Resources
Developers are encouraged to refer to the Spring AI Reference Documentation for deeper insights into the integration process. Additionally, the project points to various video tutorials, demos, workshops, and conference talks to provide a comprehensive understanding of the capabilities and implementations of Spring AI with LLMs.
This project provides a comprehensive framework for integrating Java applications with cutting-edge AI technologies, offering developers a practical resource to explore the potential of Generative AI and Large Language Models within the well-known Spring ecosystem.