spring ai
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Enterprise Java
LLM Tool Call Reasoning with Embabel AI
Large language models become significantly more useful when they can interact with external tools instead of only generating text. A…
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Enterprise Java
LLM-as-a-Judge with Spring AI Recursive Advisors
Large Language Models (LLMs) are increasingly used not only to generate content, but also to evaluate the output produced by…
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Core Java
Java as MCP Infrastructure: What Happens When Every Enterprise JVM Becomes an Agent Host
Spring AI, LangChain4j, Quarkus, and Helidon all now speak the Model Context Protocol. That is a bigger shift than another…
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Enterprise Java
Spring AI with Local LLMs Using LM Studio
Large Language Models (LLMs) are commonly accessed through cloud APIs provided by services such as OpenAI, Anthropic, or Google Gemini.…
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Enterprise Java
Spring AI Short Term Memory Sessions Example
Large Language Models are fundamentally stateless. If an application sends the question “What is my favorite programming language?” to a…
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Core Java
LangChain4j RAG in Java: Embedding, Chunking, and Retrieval Without the Python Envy
A practical pipeline for document ingestion, embedding, and retrieval, all on the JVM, with a Spring AI comparison for good…
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Enterprise Java
Spring AI Agent Skills Example
Large Language Models (LLMs) excel at reasoning, generating natural language, and answering complex questions, but enterprise AI applications require capabilities…
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Enterprise Java
HTML UIs in Spring AI MCP Servers
Modern AI applications are evolving beyond simple text interactions. With the introduction of the Model Context Protocol (MCP), developers can…
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Enterprise Java
Anthropic Agent Skills Support in Spring AI
Large Language Models (LLMs) are rapidly evolving from simple text generators into intelligent agents capable of performing complex tasks. One…
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