Spring AI学习笔记二十六:聊天模型之Ollama(四)

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("how can I solve 8x + 7 = -23",        OllamaChatOptions.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("how can I solve 8x + 7 = -23",        OllamaChatOptions.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);

// Configure Spring AI OpenAI client to point to Ollama@Configurationclass OllamaConfig {    @Bean    OpenAiChatModel ollamaChatModel() {        var openAiApi = new OpenAiApi("http://localhost:11434", "ollama");        return new OpenAiChatModel(openAiApi,            OpenAiChatOptions.builder()                .model("deepseek-r1")  // or qwen3, deepseek-v3.1, etc.                .build());    }}// Use the model with thinking-capable modelsChatResponse response = chatModel.call(    new Prompt("How many letter 'r' are in the word 'strawberry'?"));// Access the reasoning process from metadataString reasoning = response.getResult().getMetadata().get("reasoningContent");if (reasoning != null && !reasoning.isEmpty()) {    System.out.println("Model's reasoning process:");    System.out.println(reasoning);}// Get the final answerString answer = response.getResult().getOutput().getContent();System.out.println("Answer: " + answer);