package cn.iocoder.yudao.module.ai.framework.ai.config;

import cn.hutool.core.util.StrUtil;
import cn.iocoder.yudao.module.ai.framework.ai.core.model.AiModelFactory;
import cn.iocoder.yudao.module.ai.framework.ai.core.model.AiModelFactoryImpl;
import io.micrometer.observation.ObservationRegistry;
import lombok.extern.slf4j.Slf4j;
import org.springframework.ai.document.MetadataMode;
import org.springframework.ai.embedding.BatchingStrategy;
import org.springframework.ai.embedding.TokenCountBatchingStrategy;
import org.springframework.ai.model.tool.ToolCallingManager;
import org.springframework.ai.openai.OpenAiChatModel;
import org.springframework.ai.openai.OpenAiChatOptions;
import org.springframework.ai.openai.OpenAiEmbeddingModel;
import org.springframework.ai.openai.OpenAiEmbeddingOptions;
import org.springframework.ai.tokenizer.JTokkitTokenCountEstimator;
import org.springframework.ai.tokenizer.TokenCountEstimator;
import org.springframework.boot.autoconfigure.condition.ConditionalOnMissingBean;
import org.springframework.context.annotation.Bean;
import org.springframework.context.annotation.Configuration;
import org.springframework.boot.context.properties.EnableConfigurationProperties;

import java.time.Duration;

/**
 * 超级管理员 AI 自动配置
 *
 * 使用 OpenAI 兼容接口对接通义千问 DashScope
 * 向量库配置见 yudao.ai.vector-store 节点，不再依赖 Spring AI 的 Milvus 自动配置属性
 */
@Configuration
@EnableConfigurationProperties(YudaoAiProperties.class)
@Slf4j
public class AiAutoConfiguration {

    private static final String DASHSCOPE_BASE_URL = "https://dashscope.aliyuncs.com/compatible-mode/v1";

    @Bean
    public AiModelFactory aiModelFactory() {
        return new AiModelFactoryImpl();
    }

    @Bean
    @ConditionalOnMissingBean
    public ObservationRegistry observationRegistry() {
        return ObservationRegistry.NOOP;
    }

    @Bean
    @ConditionalOnMissingBean
    public BatchingStrategy batchingStrategy() {
        return new TokenCountBatchingStrategy();
    }

    @Bean
    @ConditionalOnMissingBean
    public TokenCountEstimator tokenCountEstimator() {
        return new JTokkitTokenCountEstimator();
    }

    // ========== 通义千问 Chat（通过 OpenAI 兼容接口）==========

    /**
     * 构建通义千问 Chat 模型
     *
     * @param temperature 采样温度，为 null 时用 0.7（与历史行为一致）
     * @param maxTokens   单次回复的最大 token 数，为 null 时沿用模型默认值。
     *                    不传会让长文档的输出被静默截断，表现为「JSON 尾部缺失、解析失败」
     * @param timeout     单次调用超时。不设超时的话，模型排队时请求会一直挂到 TCP 层超时
     */
    public static OpenAiChatModel buildTongYiChatModel(String apiKey, String model,
                                                       Double temperature, Integer maxTokens,
                                                       Duration timeout) {
        return OpenAiChatModel.builder()
                .options(OpenAiChatOptions.builder()
                        .baseUrl(DASHSCOPE_BASE_URL)
                        .apiKey(apiKey)
                        .model(StrUtil.blankToDefault(model, "qwen-plus"))
                        .temperature(temperature != null ? temperature : 0.7)
                        .maxTokens(maxTokens)
                        .timeout(timeout)
                        .build())
                .toolCallingManager(ToolCallingManager.builder().build())
                .build();
    }

    // ========== 通义千问 Embedding（通过 OpenAI 兼容接口）==========

    public static OpenAiEmbeddingModel buildTongYiEmbeddingModel(String apiKey, String model) {
        return OpenAiEmbeddingModel.builder()
                .options(OpenAiEmbeddingOptions.builder()
                        .baseUrl(DASHSCOPE_BASE_URL)
                        .apiKey(apiKey)
                        .model(StrUtil.blankToDefault(model, "text-embedding-v3"))
                        .build())
                .metadataMode(MetadataMode.EMBED)
                .build();
    }

}