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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsA Spring Boot endpoint can pass a prompt to an AI model through Spring AI’s fluent ChatClient and return the model’s response. The four-line version below counts only the Java handler method—not the imports, controller annotation, dependencies, provider settings, or API credential needed to run it.
What the four lines do
This small example accepts a prompt as a query parameter, sends it to the configured model, and returns the response text. It assumes a Spring application where a ChatClient bean is available.
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@GetMapping("/ask")
String ask(@RequestParam String prompt) {
return chatClient.prompt(prompt).call().content();
}The line count refers only to the method body, including its mapping and parameter annotations. It does not count the surrounding controller, imports, dependency declarations, application configuration, or credentials. “20 lines to 4” is a way to compare a concise handler with more explicit Java code; it is not a measured benchmark or a claim that a complete working application takes four lines.
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Spring AI describes ChatClient as a fluent, Spring-idiomatic interface for communicating with a model, in a style comparable to WebClient or RestClient. The method’s brevity comes from that higher-level interface, not from eliminating the provider connection and application setup.
#1 Best Overall
Set up compatible Spring Boot and Spring AI versions
Use a Spring AI release line compatible with your Spring Boot version. The current Spring AI getting-started reference lists stable releases 2.0.1, 1.1.8, and 1.0.9; it says Spring AI 2.0.x supports Spring Boot 4.0.x and 4.1.x. This example’s version guidance is for that documented 2.0.x compatibility, not a claim that all Spring AI releases work with those Boot versions.
Manage Spring AI dependency versions consistently with its BOM, or follow the dependency-management approach for your chosen release. The official Spring AI getting-started guide covers release compatibility, the BOM, Spring Initializr, and component dependencies.
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Add a model provider starter and its configuration
ChatClient is the client interface used by the handler; a provider integration supplies the model connection. Add the starter for the provider you intend to call, then configure that provider’s required properties. Starter names and configuration keys can change between Spring AI versions, so use the provider documentation for the same release line as your project.
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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallFor a concrete example, Spring AI’s Groq Chat reference documents the OpenAI-compatible starter artifact spring-ai-starter-model-openai. In that Groq-compatible setup, it uses spring.ai.openai.api-key and spring.ai.openai.base-url. These are details of that integration, not universal property names for every provider. Spring AI’s Groq Chat documentation explains the setup, and its upgrade notes cover starter renaming and the current component-specific naming pattern.
Keep API credentials out of source control. For local development, supply the key through an environment variable and reference it from configuration, rather than writing the secret directly into a committed properties file. Check the selected provider’s current instructions for the exact environment-variable and property setup it supports.
Connect the handler to ChatClient
In the controller, make ChatClient available through Spring dependency injection. The method above then submits the incoming prompt with prompt(prompt), performs the call with call(), and extracts response text with content(). The configured model and provider determine where the call goes; the endpoint mapping alone does not select or configure a provider.
For a beginner example, the query parameter keeps the handler compact. A real application may instead accept a request body, particularly when prompts are long or the request needs additional fields. Either way, the HTTP mapping and the model integration remain separate pieces: the mapping receives the request, while the configured client communicates with the model.
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The handler illustrates the request-to-model path, not production readiness. Before exposing an AI endpoint beyond a local demonstration, consider the controls appropriate to the application:
- Authenticate and authorize callers; do not expose an unrestricted endpoint just because the handler is short.
- Set timeouts and handle provider errors so slow or failed model calls do not become uncontrolled application failures.
- Apply rate limits and usage controls to protect the service and manage provider consumption.
- Validate inputs and decide how to handle model output; returned text is not automatically safe or suitable for every downstream use.
- Check the selected provider’s current availability, terms, and model behavior for the deployment region and account.
The official Spring AI ChatClient API reference describes the client abstraction. Provider-specific starter and property details should be verified against the corresponding provider guide when upgrading.
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