Embabel is Rod Johnson’s agent framework for the JVM, built on Spring AI, where a GOAP planner chains our @Action methods at runtime. This tutorial builds a reading plan agent with Embabel 1.5.2 and Spring Boot 4.1.1, runs it on a local Ollama model and tests it with FakeOperationContext and EmbabelMockitoIntegrationTest, without an API key.
Spring AI CallAdvisor and StreamAdvisor provide hooks to wrap around LLM invocations, both for synchronous and streaming scenarios.
Learn to configure Postgres PgVectorStore to store the vectors generated with OpenAI and Ollama embedding models in a Spring AI project.
Learn to build a fully-functional chatbot application (with UI) with RAG (Retrieval-Augmented Generation) capabilities using Spring AI and Spring Web.
In Spring AI, function calling is the ability for the model to request one or more function calls to be made on its behalf by the ChatBot application.
Learn to download, install, and run an LLM model using Ollama. Also learn to configure Spring AI Ollama module to access the model’s chat API.
Inspired by LangChain4j and LlamaIndex, Spring AI project aims to streamline the development and integration of AI capabilities into existing or new Spring applications.
LLMs are quite good at producing the source code when they are provided enough context information and asked very clear questions. The same is true for generating SQL queries when they are provided the table/column information through DDL statements, and clear instructions of output format of the generated SQL statements. …
For creating an ETL pipeline for Spring AI data ingestion microservice, Spring cloud functions are an excellent choice for serverless providers.
The ETL pipeline ingest raw data sources (text, JSON/XML, audio, video, etc.) to a structured vector store for similarity searches using Spring AI.
In Spring AI, the role of a vector database is to store vector embeddings and facilitate similarity searches for these embeddings. Learn with examples.
In Spring AI Vector Embedding tutorial, learn what is a vector or embedding, how it helps in semantic searches, and how to generate embeddings using OpenAI.
Spring AI currently supports only OpenAI’s whisper model for speech transcription to JSON or TEXT files using OpenAiAudioTranscriptionModel class.
In Spring AI, SpeechModel and StreamingSpeechModel interfaces allow to interact with Text-to-Speech APIs of supported LLMs such as tts-1 by OpenAI.
In Spring AI PromptTemplate example, learn different message roles and how they are used to create prompts and send them to the AI models.
Spring AI provides 3 inbuilt classes MapOutputConverter, ListOutputConverter, and BeanOutputConverter to help prompt in communicating expected response structures.
This Spring tutorial discusses the basics of Spring AI APIs for image generation using OpenAI’s DALL-E and Stability AI with examples.
This Spring AI tutorial discusses its core terminology and examples to interact with OpenAI’s chat and image generation APIs using simple text prompts.
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