ChatModel chatModel = ChatModel.of("http://127.0.0.1:11434/api/chat")
                .provider("ollama") //Need to specify vendor, used to identify interface style (also called dialect)
                .model("qwen2.5:1.5b")
                .defaultSkillAdd(new ToolGatewaySkill())
                .build();

// Synchronize the call and print the response message
AssistantMessage result = ChatchatModel.prompt("The weather in Hangzhou today？")
         .options(op->op.toolAdd(new WeatherTools())) //Adding tools
         .call()
         .getMessage();
System.out.println(result);

// Stream call
chatModel.prompt("hello").stream(); //Publisher<ChatResponse>

Skill skill = new SkillDesc("order_expert")
        .description("Order Assistant")
        // Dynamic admission: Activated only when "order" is mentioned
        .isSupported(prompt -> prompt.getUserMessageContent().contains("order"))
        // Dynamic instructions: Inject different Sops depending on whether the user is a VIP or not
        .instruction(prompt -> {
            if ("VIP".equals(prompt.getMeta("user_level"))) {
                return "This is a VIP customer, please call fast_track_tool first.";
            }
            return "Process the order inquiry according to the normal process.";
        })
        .toolAdd(new OrderTools());

chatModel.prompt("Where is my order from yesterday？")
         .options(o->o.skillAdd(skill))
         .call();

//Building a Knowledge Warehouse
EmbeddingModel embeddingModel = EmbeddingModel.of(apiUrl).apiKey(apiKey).provider(provider).model(model).batchSize(10).build();
RerankingModel rerankingModel = RerankingModel.of(apiUrl).apiKey(apiKey).provider(provider).model(model).build();
InMemoryRepository repository = new InMemoryRepository(TestUtils.getEmbeddingModel()); //3.初始化知识库

repository.insert(new PdfLoader(pdfUri).load());

//retrieval
List<Document> docs = repository.search(query);

//You can rearrange it if you want
docs = rerankingModel.rerank(query, docs);

//Cue enhancement is
ChatMessage message = ChatMessage.ofUserAugment(query, docs);

//Calling the llm
chatModel.prompt(message) 
    .call();

//server
@McpServerEndpoint(channel = McpChannel.STREAMABLE, mcpEndpoint = "/mcp") 
public class MyMcpServer {
    @ToolMapping(description = "Checking the weather")
    public String getWeather(@Param(description = "city") String location) {
        return "It's sunny, 25 degrees";
    }
}

//client
McpClientProvider clientProvider = McpClientProvider.builder()
        .channel(McpChannel.STREAMABLE)
        .url("http://localhost:8080/mcp")
        .build();

//Reflective intelligent agent:
ReActAgent agent = ReActAgent.of(chatModel) // 或者用 SimpleAgent.of(chatModel)
    .name("weather_expert")
    .description("Check the weather and provide advice")
    .defaultToolAdd(weatherTool) // Inject MCP or local tools
    .build();

agent.prompt("What to wear in Beijing today？").call(); // Autocomplete: Think -> Call tool -> Observe -> Summarize

// Constructing a team agent: Automatically arranging member roles through protocols
TeamAgent team = TeamAgent.of(chatModel)
    .name("marketing_team")
    .protocol(TeamProtocols.HIERARCHICAL) // Hierarchical collaboration (6 preset protocols)
    .agentAdd(copywriterAgent) // Copywriter expert
    .agentAdd(illustratorAgent) // Illustrator expert
    .build();

team.prompt("Plan a promotion scheme for deep-sea mineral water").call(); // Supervisor automatically decomposes tasks and assigns them to corresponding experts    .defaultToolAdd(weatherTool) // Inject MCP or local tools

id: demo1
layout:
  - type: "start"
  - task: "@VarInput"
    meta:
      message: "Solon 是谁开发的？"
  - task: "@EmbeddingModel"
    meta:
      embeddingConfig: # "@type": "org.noear.solon.ai.embedding.EmbeddingConfig"
        provider: "ollama"
        model: "bge-m3"
        apiUrl: "http://127.0.0.1:11434/api/embed"
  - task: "@InMemoryRepository"
    meta:
      documentSources:
        - "https://solon.noear.org/article/about?format=md"
      splitPipeline:
        - "org.noear.solon.ai.rag.splitter.RegexTextSplitter"
        - "org.noear.solon.ai.rag.splitter.TokenSizeTextSplitter"
  - task: "@ChatModel"
    meta:
      systemPrompt: "你是个知识库"
      stream: false
      chatConfig: # "@type": "org.noear.solon.ai.chat.ChatConfig"
        provider: "ollama"
        model: "qwen2.5:1.5b"
        apiUrl: "http://127.0.0.1:11434/api/chat"
  - task: "@ConsoleOutput"

# FlowEngine flowEngine = FlowEngine.newInstance();
# ...
# flowEngine.eval("demo1");