Spring AI: Integration with Large Language Models

This article delves into the exciting synergy between Spring AI and LLMs. We’ll explore how Spring AI bridges the gap between developers and these powerful AI models. We’ll also give a Spring AI example features illustrating how you can leverage LLMs in your own projects.

Spring AI is an application framework designed to streamline the development of applications that incorporate artificial intelligence (AI) functionality.

It draws inspiration from notable Python projects like LangChain and LlamaIndex, aiming to make AI development more accessible and standardized across various programming languages.

In this article, we will discover a novel Spring project providing Java developers with a user-friendly API abstraction tailored for handling large-language models (LLMs) such as ChatGPT.

Spring AI and LLMs

LLMs are trained on massive amounts of text data, enabling them to understand and generate human-like text. The API provides a way for software to interact with these LLMs

Developers can integrate LLMs into their applications without needing the immense computational resources required to train or run them directly.

One of the ways to access LLM functionality is via its dedicated API, offered by major companies like OpenAI, Google AI, and Mistral AI for their in-house LLMs.

This gives a chance for individuals and organizations that dont have huge computing resources in disposal to use the benefits that these large LLMs offer.

However different LLMs have different APIs and the developer would need to learn and implement each integration if he wants to use more than one LLM.

Spring AI facilitates this by offering a middle layer, providing a consistent API for interacting with various large language models. This frees developers from needing to learn the specific quirks of each LLM’s interface. They can focus on crafting the content prompt and handling the generated output without worrying about the underlying LLM infrastructure.

This portability allows developers to switch between different LLM providers (e.g., OpenAI, Google AI) without major code changes, as long as the provider offers an API compatible with Spring AI’s abstractions.

The project supports all major Model providers such as OpenAI, Microsoft, Amazon, Google, and Huggingface.

How does Spring AI interact with LLMs?

Prompts. In the context of AI, especially with large language models (LLMs), a prompt refers to a piece of information you provide to guide the model’s response. It’s like giving an instruction or setting the stage for what you want the LLM to do. Prompts can take various formats depending on the specific LLM and the desired outcome.

Spring AI utilizes prompt templates to structure prompts. These templates act as blueprints with predefined text and placeholders.

You can insert dynamic content into these placeholders to tailor the prompt to your specific needs. This approach helps standardize prompt formats and steer the AI’s response in a desired direction.

Handling prompts this way provides a level of control over the AI’s response direction by guiding its generation process.

Chat Completion Feature

The Spring AI Chat Completion API is a tool for developers to integrate AI-powered conversation continuation features into their applications. It acts as a bridge between your application and powerful large language models that can understand and respond to natural language.

In short, Chat Completion API exposes this feature via ChatClient Interface. It has a call method that receives a prompt and returns ChatResponse class.

The ChatResponse class holds the AI Model’s output, with a list containing one of potentially multiple outputs resulting from a single prompt.

Spring LLM integration significantly facilitates, spring ai example projects, like chatbot support services.

Text Classification Feature

Text Classification is done with a technique called Embedding.

Word embeddings represent words as dense vectors in a lower-dimensional space. These vectors encode semantic similarities and relationships between words. Words with similar meanings will have closer vector representations in this space.

The Spring AI Embedding API via its EmbeddingClient interface provides a standardized way to interact with embedding models within the Spring AI framework. By using this interface developers can implement LLM specific Client such as : OpenAIEmbeddingClient, OlllamaEmbeddingClient, MistralAIEmbeddingClient etc.

This makes it easy to develop, spring ai example projects like text classification and sentiment analysis applications.

Image Generation Feature

Image generation refers to the field of artificial intelligence (AI) concerned with converting users’ text instructions to new images from scratch or modifying existing ones.

The Spring Image Generation API is a tool designed to streamline the process of interacting with various AI models capable of generating images from text descriptions. classes like ImagePrompt for input encapsulation and ImageResponse for output handling, the Image Generation API unifies the communication with AI Models dedicated to image generation.

ImageClient implementations are provided for the OpenAI Image Generation and StabilityAI Image Generation

Transcription Feature

Transcription, in the context of AI and computing, refers to the process of automatically converting audio or video recordings into written text.

Spring AI offers an AudioTranscriptionClient class within its framework. This client focuses on interacting with audio transcription models at the time specific to OpenAI.

This is convenient way to integrate OpenAI’s Transcription API into your audio or video transcription service application by offering a pre-built client with configuration options.

Bringing Your Data to LLM

LLMs are trained on massive datasets of text and code, but they might not have been exposed to every possible scenario or piece of information.

What are some strategies for incorporating fresh data or fine-tuning an AI model with information not included in its original training set?

Retrieval Augmented Generation (RAG) , colloquially known as “stuffing the prompt” involves strategically including relevant background information or context that the LLM might not have explicitly learned during training.

This system utilizes a batch processing paradigm, where a program reads unstructured data from documents, applies transformations, and finally stores the resulting vectors in a dedicated vector database.

A vector database is a specialized type of database designed to store and efficiently retrieve high-dimensional vectors. These vectors are mathematical representations of data points, often used in machine learning and artificial intelligence (AI) applications.

Spring AI exposes an abstraction layer (VectorStore interface) for interacting with vector databases. This API enables developers to work with various vector database implementations without dealing with their underlying complexities.

Some of the implementations of the VectorStore interface include:

Neo4jVectorStore — for Neo4j vector database, Azure Vector Search, for Azure vector database, ChromaVectorStore, for Chroma vector database etc.

When a prompt is passed to LLM, contents of document form vector database are then “‘stuffed’” into the prompt as context for the user’s question.

These documents are then used to build a richer context for your question, like giving the AI more details to consider before answering.


As of this writing latest version is 0.8.1 with no General Availability yet, Spring AI promises a range of tools and features aimed at simplifying the incorporation of diverse AI capabilities into applications.

Prompt Templates streamline prompt formatting for specific tasks, reducing developers’ time and effort.

With spring ai example projects like text classification, developers can utilize capabilities such as sentiment analysis, topic categorization, or spam detection without the need to construct their own model. 

Additionally, Spring AI supports RAG by facilitating communication with an external knowledge, which essentially is a vector embedding database, alongside the LLM, ensuring factual accuracy and broader context for tasks where it’s essential.

Spring AI acts as a bridge between developers and various AI models simplifying the process of incorporating AI capabilities into applications.

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