OpenAI on SivaLabs

Spring AI : Advisors API

Thu, 30 Jul 2026 06:00:00 +0530

Once an AI feature moves beyond a demo, the model call is rarely the whole story.

You need to log requests, restore conversation history, retrieve relevant documents, reject unsafe input, record metrics, and perhaps validate the model’s output. Putting all of that code directly around every ChatClient call works for a while. Then a second endpoint arrives, followed by streaming, and suddenly every model call has its own slightly different pile of plumbing.

Spring AI : Chat Memory

Sat, 25 Jul 2026 06:00:00 +0530

Large language models are stateless. Send two independent requests to a model, and the second request knows nothing about the first one. That is perfectly fine for one-shot questions, but it makes a chatbot feel surprisingly forgetful:

Spring AI : Structured Output

Wed, 22 Jul 2026 06:00:00 +0530

Chat models are great at producing prose, but most applications don’t want prose — they want a Java object, a List<String>, or a Map they can bind to a response and return from a REST API.

Spring AI’s Structured Output Converters bridge that gap: they turn plain-text chat responses into typed Java objects, without you having to hand-write a JSON parser or repeat “please respond with valid JSON” in every prompt.

Spring AI : Chat with LLMs

Mon, 20 Jul 2026 06:00:00 +0530

Calling an LLM is just an HTTP request. The trouble is that every provider has its own API, configuration, and response format.

Spring AI handles that provider-specific plumbing and gives us a familiar Spring API. In this article, we will use ChatClient to talk to OpenAI’s GPT-5 model and then make our prompts reusable with PromptTemplate.

Spring AI RAG using Embedding Models and Vector Databases

Thu, 14 Mar 2024 23:29:17 +0000