Ollama 聊天

ollama pull <模型名称>

ollama pull hf.co/<用户名>/<模型仓库>

ChatResponse response = chatModel.call(
    new Prompt(
        "生成 5 个著名海盗的名字。",
        OllamaOptions.builder()
            .model(OllamaModel.LLAMA3_1)
            .temperature(0.4)
            .build()
    ));

spring:
  ai:
    ollama:
      init:
        pull-model-strategy: always
        timeout: 60s
        max-retries: 1

spring:
  ai:
    ollama:
      init:
        pull-model-strategy: always
        chat:
          additional-models:
            - llama3.2
            - qwen2.5

spring:
  ai:
    ollama:
      init:
        pull-model-strategy: always
        chat:
          include: false

var imageResource = new ClassPathResource("/multimodal.test.png");

var userMessage = new UserMessage("解释一下你在这张图片上看到了什么？",
        new Media(MimeTypeUtils.IMAGE_PNG, this.imageResource));

ChatResponse response = chatModel.call(new Prompt(this.userMessage,
        OllamaOptions.builder().model(OllamaModel.LLAVA)).build());

图片显示了一个装满成熟香蕉和红苹果的小金属篮子。篮子放在一个表面上，
看起来像一张桌子或台面，因为背景中隐约可见似乎是厨柜或抽屉的东西。
篮子后面还有一个金色的环，这可能表明这张照片是在一个有金属装饰品或固定装置的区域拍摄的。
整体环境表明这是一个家庭环境，水果正在展示，可能是为了方便或美观。

String jsonSchema = """
        {
            "type": "object",
            "properties": {
                "steps": {
                    "type": "array",
                    "items": {
                        "type": "object",
                        "properties": {
                            "explanation": { "type": "string" },
                            "output": { "type": "string" }
                        },
                        "required": ["explanation", "output"],
                        "additionalProperties": false
                    }
                },
                "final_answer": { "type": "string" }
            },
            "required": ["steps", "final_answer"],
            "additionalProperties": false
        }
        """;

Prompt prompt = new Prompt("如何求解 8x + 7 = -23",
        OllamaOptions.builder()
            .model(OllamaModel.LLAMA3_2.getName())
            .format(new ObjectMapper().readValue(jsonSchema, Map.class))
            .build());

ChatResponse response = this.ollamaChatModel.call(this.prompt);

record MathReasoning(
    @JsonProperty(required = true, value = "steps") Steps steps,
    @JsonProperty(required = true, value = "final_answer") String finalAnswer) {

    record Steps(
        @JsonProperty(required = true, value = "items") Items[] items) {

        record Items(
            @JsonProperty(required = true, value = "explanation") String explanation,
            @JsonProperty(required = true, value = "output") String output) {
        }
    }
}

var outputConverter = new BeanOutputConverter<>(MathReasoning.class);

Prompt prompt = new Prompt("如何求解 8x + 7 = -23",
        OllamaOptions.builder()
            .model(OllamaModel.LLAMA3_2.getName())
            .format(outputConverter.getJsonSchemaMap())
            .build());

ChatResponse response = this.ollamaChatModel.call(this.prompt);
String content = this.response.getResult().getOutput().getText();

MathReasoning mathReasoning = this.outputConverter.convert(this.content);

spring.ai.ollama.chat.options.model=hf.co/bartowski/gemma-2-2b-it-GGUF
spring.ai.ollama.init.pull-model-strategy=always

spring:
  ai:
    ollama:
      base-url: http://localhost:11434
      chat:
        options:
          model: mistral
          temperature: 0.7

@RestController
public class ChatController {

    private final OllamaChatModel chatModel;

    @Autowired
    public ChatController(OllamaChatModel chatModel) {
        this.chatModel = chatModel;
    }

    @GetMapping("/ai/generate")
    public Map<String,String> generate(@RequestParam(value = "message", defaultValue = "给我讲个笑话") String message) {
        return Map.of("generation", this.chatModel.call(message));
    }

    @GetMapping("/ai/generateStream")
    public Flux<ChatResponse> generateStream(@RequestParam(value = "message", defaultValue = "给我讲个笑话") String message) {
        Prompt prompt = new Prompt(new UserMessage(message));
        return this.chatModel.stream(prompt);
    }
}

var ollamaApi = OllamaApi.builder().build();

var chatModel = OllamaChatModel.builder()
                    .ollamaApi(ollamaApi)
                    .defaultOptions(
                        OllamaOptions.builder()
                            .model(OllamaModel.MISTRAL)
                            .temperature(0.9)
                            .build())
                    .build();

ChatResponse response = this.chatModel.call(
    new Prompt("生成 5 个著名海盗的名字。"));

// 或使用流式响应
Flux<ChatResponse> response = this.chatModel.stream(
    new Prompt("生成 5 个著名海盗的名字。"));

OllamaApi ollamaApi = new OllamaApi("你的主机:你的端口");

// 同步请求
var request = ChatRequest.builder("orca-mini")
    .stream(false) // 非流式
    .messages(List.of(
            Message.builder(Role.SYSTEM)
                .content("你是一名地理老师。你正在和一名学生交谈。")
                .build(),
            Message.builder(Role.USER)
                .content("保加利亚的首都是哪里，面积有多大？ "
                        + "国歌是什么？")
                .build()))
    .options(OllamaOptions.builder().temperature(0.9).build())
    .build();

ChatResponse response = this.ollamaApi.chat(this.request);

// 流式请求
var request2 = ChatRequest.builder("orca-mini")
    .stream(true) // 流式
    .messages(List.of(Message.builder(Role.USER)
        .content("保加利亚的首都是哪里，面积有多大？ " + "国歌是什么？")
        .build()))
    .options(OllamaOptions.builder().temperature(0.9).build().toMap())
    .build();

Flux<ChatResponse> streamingResponse = this.ollamaApi.streamingChat(this.request2);