CodeWithClarity

 Spring AI ChatClient– Response Types Explained  Whenyou invoke .call() on a ChatClient, there are different ways to get the result, depending on what you want to do with it Streaming Responses •  To stream reponses, we can use. stream() instead of. call()  • Good for real-time or chunked responses (like streaming output to UI. Structured Output Converter in Spring AI Why DoWe Need Structured Output? LLMs typically return plain text responses. But in real-world apps, we often need structured data like, • JSON • XML • JavaClasses (POJOs) Structured data is easier to parse, use, and integrate in applications. What is a Structured Output Converter ? Before sending prompt to LLM: ➤Addsformatting instructions to guide the model. ➤Ensures it replies in a parseable format. After getting response from LLM: ➤Converts raw text into a Java object, like Map, List, or custom class Structured Output Converter in Spring AI Example: Convert reponse to a Java object CountryCitie...

 Chat Client APIs and Streaming call(). content() (Most Common) Returns only the generated text. @GetMapping ( "/content" ) public String content ( @RequestParam String question) { return chatClient.prompt() .user(question) .call() .content() ; } Flow: User │ ▼ ChatClient │ ▼ LLM │ ▼ String Use when you only need the answer. call().chatResponse() Returns the complete response object, not just the text. @GetMapping ( "/response" ) public ChatResponse response ( @RequestParam String question) { return chatClient.prompt() .user(question) .call() .chatResponse() ; } Chat Response contains much more than the content. http://localhost:8080/api/response?question=Explain Kafka output are in json format .  Streaming Instead of waiting for the complete answer, stream it token by token. @GetMapping (value= "/stream" , produces = MediaType. TEXT_EVENT_STREAM_VALUE ...

                              What is ChatOptions? Q1. What is ChatOptions? Answer ChatOptions is a configuration object in Spring AI that controls how the underlying language model generates responses.   It allows developers to configure parameters such as model selection, temperature, maximum output tokens, sampling behavior, and other provider-supported settings.    Instead of changing the prompt, it changes how the model generates the answer . Architecture User │ ▼ ChatClient │ ├── Prompt ├── Advisors └── ChatOptions │ ▼ OpenAI / Ollama │ ▼ AI Response Notice: Prompt → tells the model what to do. ChatOptions → tells the model how to do it. Example String response = chatClient . prompt () . user ( "Explain Kafka" ) . options ( OpenAiChatOptions . builder () . temperature ( 0.7 ...