Solon 4.0 ChatModel: A Practical Guide to Building LLM-Powered Applications

Solon 4.0 introduces ChatModel, a unified LLM client that abstracts away boilerplate code for integrating large language models into Java applications. The API supports multiple model providers including OpenAI, Ollama, Gemini, Anthropic, and DashScope through a dialect pattern, and offers both synchronous and streaming chat capabilities with a builder-oriented API.

If you've ever tried integrating a large language model LLM into a Java application, you've probably written a lot of boilerplate: HTTP clients, JSON parsing, streaming handling, session management. Solon 4.0's ChatModel abstracts all of that away with a clean, builder-oriented API. In this guide, I'll walk through building real, working AI features using ChatModel — from a simple chat call to a streaming chatbot with conversation memory. ChatModel package org.noear.solon.ai.chat is a unified LLM client in Solon's AI ecosystem. Instead of writing raw HTTP calls for different model providers, you use a single API that supports: Flux<ChatResponse The best part? It uses a dialect pattern — you point it at any compatible LLM endpoint, and it adapts automatically. Add the dependency to your pom.xml no parent POM needed — Solon works standalone : <dependency <groupId org.noear</groupId <artifactId solon-ai</artifactId <version ${solon.version}</version </dependency This pulls in all built-in dialects OpenAI, Ollama, Gemini, Anthropic, DashScope . solon.ai.chat: demo: apiUrl: "http://127.0.0.1:11434/api/chat" Full URL, not baseUrl provider: "ollama" Dialect identifier model: "llama3.2" Model name headers: x-demo: "demo1" Then create a @Bean to get a ready-to-use ChatModel : python import org.noear.solon.ai.chat.ChatConfig; import org.noear.solon.ai.chat.ChatModel; import org.noear.solon.annotation.Bean; import org.noear.solon.annotation.Configuration; import org.noear.solon.annotation.Inject; @Configuration public class AiConfig { @Bean public ChatModel chatModel @Inject "${solon.ai.chat.demo}" ChatConfig config { return ChatModel.of config .build ; } } Prefer code over config? Use the builder directly: @Bean public ChatModel chatModel { return ChatModel.of "http://127.0.0.1:11434/api/chat" .standard "ollama" // or .provider "ollama" pre-4.0 .model "llama3.2" .timeout Duration.ofSeconds 60 .build ; } The standard or provider field selects the dialect: | Standard | Example apiUrl | Models | |---|---|---| openai default | https://api.openai.com/v1/chat/completions | GPT, DeepSeek, Qwen, GLM, Kimi, etc. | ollama | http://127.0.0.1:11434/api/chat | Any local Ollama model | anthropic | https://api.anthropic.com/v1/messages | Claude | gemini | https://generativelanguage.googleapis.com/v1beta/models/... | Gemini | dashscope | Aliyun DashScope endpoint | Qwen DashScope native | The most basic use case — send a prompt and get a full response: python import org.noear.solon.ai.chat.ChatModel; import org.noear.solon.ai.chat.ChatResponse; import org.noear.solon.annotation.Inject; import org.noear.solon.annotation.Component; @Component public class ChatService { @Inject ChatModel chatModel; public String ask String question throws IOException { ChatResponse resp = chatModel.prompt question .call ; return resp.getMessage .getContent ; } } That's it. Three lines of business code. For chatbots and assistants, streaming is essential. ChatModel returns a Reactor Flux<ChatResponse : python import reactor.core.publisher.Flux; public Flux<String askStream String question throws IOException { return chatModel.prompt question .stream .filter ChatResponse::hasContent // skip empty chunks .map resp - resp.getMessage .getContent ; } You can then subscribe, or — if you're using Solon Web Reactive — return the Flux directly to an SSE endpoint: python import org.noear.solon.web.sse.SseEvent; import org.noear.solon.annotation.Mapping; import reactor.core.publisher.Flux; @Mapping "/chat/stream" public Flux<SseEvent chatStream String prompt throws IOException { return chatModel.prompt prompt .stream .filter ChatResponse::hasContent .map resp - new SseEvent .data resp.getMessage .getContent ; } The streaming protocol uses standard SSE text/event-stream or x-ndjson depending on the provider. LLMs are stateless. To maintain conversation context, you need to pass history with each request. ChatSession handles this automatically. python import org.noear.solon.ai.chat.ChatSession; import org.noear.solon.ai.chat.session.InMemoryChatSession; ChatSession session = InMemoryChatSession.builder .sessionId "user-123" .maxMessages 10 // keep last 10 turns .build ; // First turn ChatResponse resp1 = chatModel.prompt "Hello " .session session .call ; // Second turn — model remembers context ChatResponse resp2 = chatModel.prompt "What did I just say?" .session session .call ; In a real web app, you'll want one session per user. Here's a controller that does exactly that: python import org.noear.solon.annotation.Controller; import org.noear.solon.web.sse.SseEvent; import reactor.core.publisher.Flux; import java.util.Map; import java.util.concurrent.ConcurrentHashMap; @Controller public class ChatController { @Inject ChatModel chatModel; final Map<String, ChatSession sessionMap = new ConcurrentHashMap< ; @Mapping "/chat" public Flux<SseEvent chat String sessionId, String prompt throws IOException { ChatSession session = sessionMap.computeIfAbsent sessionId, k - InMemoryChatSession.builder .sessionId k .build ; return chatModel.prompt prompt .session session .options o - o.systemPrompt "You are a helpful and friendly assistant." .stream .filter ChatResponse::hasContent .map resp - new SseEvent .data resp.getMessage .getContent ; } } | Implementation | Storage | Use Case | |---|---|---| InMemoryChatSession | Local Map | Dev, single-node | FileChatSession | File system | CLI tools, desktop apps | RedisChatSession | Redis | Production, distributed | Control model behavior per-request with ChatOptions : php chatModel.prompt "Write a poem about Java" .options o - o .temperature 0.8 .max tokens 500 .top p 0.9 .systemPrompt "You are a creative poet." .call ; Common options include: | Method | Description | |---|---| temperature val | Sampling temperature 0.0–2.0 | max tokens val | Max output tokens | top p val | Nucleus sampling | top k val | Top-K sampling | frequency penalty val | Reduce repetition | presence penalty val | Encourage new topics | tool choice val | Force tool use: none , auto , required , or tool name | systemPrompt val | System message for this request | role val | Agent role v3.9.1+ | instruction val | Agent instruction v3.9.1+ | Sometimes you need more than a simple string. Use Prompt and ChatMessage : python import org.noear.solon.ai.chat.Prompt; import org.noear.solon.ai.chat.message.ChatMessage; Prompt prompt = Prompt.of ChatMessage.ofSystem "You translate English to French." , ChatMessage.ofUser "Hello, how are you?" , ChatMessage.ofAssistant "Bonjour, comment allez-vous?" , ChatMessage.ofUser "What is your name?" ; ChatResponse resp = chatModel.prompt prompt .call ; Let's build a simple knowledge-aware chatbot — the kind of RAG-lite pattern you see in real projects. This example uses ChatMessage.ofUserAugment to inject context into the prompt: python import org.noear.solon.ai.chat.ChatModel; import org.noear.solon.ai.chat.ChatResponse; import org.noear.solon.ai.chat.message.ChatMessage; import org.noear.solon.annotation.Component; import org.noear.solon.annotation.Inject; @Component public class KnowledgeChatbot { @Inject ChatModel chatModel; public String answer String question, String referenceContext throws Exception { // Augment the user message with reference context ChatMessage augmented = ChatMessage.ofUserAugment question, referenceContext ; ChatResponse resp = chatModel.prompt augmented .options o - o .temperature 0.3 .systemPrompt "You are a knowledgeable assistant. Answer based on the provided references." .call ; return resp.getMessage .getContent ; } } This pattern — augment user input with context, then call the model — is the foundation of RAG Retrieval-Augmented Generation in Solon AI. ChatModel is just the entry point. Solon AI also offers: @ToolMapping methods the LLM can invoke ReActAgent and TeamAgent for multi-step reasoningFor the full documentation, check out the official Solon AI guide: 👉 https://solon.noear.org/article/918 https://solon.noear.org/article/918 Model construction 👉 https://solon.noear.org/article/920 https://solon.noear.org/article/920 API reference Have you tried integrating LLMs in Java? What's your biggest pain point? Let me know in the comments — I might cover it in the next post.