amberjs 1.17.0

// AI模型统一接口
// 提供标准化的AI模型调用和管理接口

use std::collections::{BTreeMap, HashMap};
use std::sync::{Arc, Mutex};
use std::time::{Duration, Instant};

/// AI模型类型
#[derive(Debug, Clone, PartialEq)]
#[allow(dead_code)]
pub enum ModelType {
    /// 大语言模型
    LanguageModel {
        model_name: String,
        max_tokens: usize,
        temperature: f32,
    },
    /// 图像分类模型
    ImageClassifier {
        model_name: String,
        input_size: (usize, usize),
        num_classes: usize,
    },
    /// 嵌入向量模型
    EmbeddingModel {
        model_name: String,
        dimensions: usize,
    },
    /// 文本转语音模型
    TextToSpeech {
        model_name: String,
        voice_id: String,
    },
    /// 语音识别模型
    SpeechToText {
        model_name: String,
        language: String,
    },
    /// 翻译模型
    TranslationModel {
        model_name: String,
        source_lang: String,
        target_lang: String,
    },
}
/// 模型输入
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub enum ModelInput {
    Text {
        content: String,
        max_tokens: Option<usize>,
        temperature: Option<f32>,
    },
    Image {
        data: Vec<u8>,
        format: String, // "png", "jpg", etc.
    },
    Audio {
        data: Vec<u8>,
        format: String, // "wav", "mp3", etc.
        sample_rate: Option<u32>,
    },
    Embedding {
        text: String,
    },
    Translation {
        text: String,
        source_lang: String,
        target_lang: String,
    },
}
/// 模型输出
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub enum ModelOutput {
    Text {
        content: String,
        tokens_used: usize,
        finish_reason: String,
    },
    Classification {
        predictions: Vec<(String, f32)>,
        confidence: f32,
    },
    Embedding {
        vector: Vec<f32>,
        dimensions: usize,
    },
    Audio {
        data: Vec<u8>,
        format: String,
        duration: Duration,
    },
    Translation {
        translated_text: String,
        source_lang: String,
        target_lang: String,
        confidence: f32,
    },
}
/// 模型配置
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct ModelConfig {
    pub model_type: ModelType,
    pub max_batch_size: usize,
    pub timeout: Duration,
    pub retry_count: usize,
    pub enable_caching: bool,
    pub cache_ttl: Duration,
}
/// 模型性能指标
#[derive(Debug, Clone)]
#[allow(dead_code)]
pub struct ModelMetrics {
    pub total_requests: usize,
    pub successful_requests: usize,
    pub failed_requests: usize,
    pub average_latency: Duration,
    pub p95_latency: Duration,
    pub p99_latency: Duration,
    pub throughput_rps: f64,
    pub error_rate: f64,
    pub last_updated: Instant,
}
impl Default for ModelMetrics {
    fn default() -> Self {
        Self {
            total_requests: 0,
            successful_requests: 0,
            failed_requests: 0,
            average_latency: Duration::from_nanos(0),
            p95_latency: Duration::from_nanos(0),
            p99_latency: Duration::from_nanos(0),
            throughput_rps: 0.0,
            error_rate: 0.0,
            last_updated: Instant::now(),
        }
    }
}
impl ModelMetrics {
    #[allow(dead_code)]
    pub fn update(&mut self, latency: Duration, success: bool) {
        self.total_requests += 1;
        if success {
            self.successful_requests += 1;
        } else {
            self.failed_requests += 1;
        }
        // 更新平均延迟
        let current_avg: _ = self.average_latency;
        let count: _ = self.successful_requests as u64;
        self.average_latency = Duration::from_nanos(
            (current_avg.as_nanos() as u64 * (count - 1) + latency.as_nanos() as u64) / count,
        );
        self.error_rate = self.failed_requests as f64 / self.total_requests as f64;
        self.last_updated = Instant::now();
    }
    #[allow(dead_code)]
    pub fn success_rate(&self) -> f64 {
        if self.total_requests > 0 {
            self.successful_requests as f64 / self.total_requests as f64
        } else {
            0.0
        }
    }
}
/// AI模型实例
#[derive(Debug)]
#[allow(dead_code)]
pub struct AiModel {
    pub id: String,
    pub config: ModelConfig,
    pub is_loaded: bool,
    pub load_time: Option<Instant>,
    pub metrics: Arc<Mutex<ModelMetrics>>,
}
/// AI模型管理器
#[allow(dead_code)]
pub struct AiModelManager {
    models: Arc<Mutex<HashMap<String, AiModel>>>,
    default_model_id: Arc<Mutex<Option<String>>>,
    routing_strategy: Arc<Mutex<ModelRoutingStrategy>>,
}
/// 模型路由策略
#[derive(Debug, Clone)]
#[allow(dead_code)]
#[derive(Default)]
pub enum ModelRoutingStrategy {
    /// 轮询
    #[default]
    RoundRobin,
    /// 最快响应
    Fastest,
    /// 负载均衡
    LoadBalanced,
    /// 基于模型类型的路由
    TypeBased,
}
#[allow(dead_code)]
impl AiModelManager {
    /// 创建新的模型管理器
    pub fn new() -> Self {
        Self {
            models: Arc::new(Mutex::new(HashMap::new()))
            default_model_id: Arc::new(Mutex::new(None)))
            routing_strategy: Arc::new(Mutex::new(ModelRoutingStrategy::default()))
        }
    }
    /// 注册模型
    pub fn register_model(
        &self,
        model: AiModel,
    ) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let mut models = self.models.lock().unwrap();
        if models.contains_key(&model.id) {
            return Err("模型已存在".into());
        }
        models.insert(model.id.clone(), model);
        // 如果是第一个模型，设为默认模型
        if models.len() == 1 {
            let model_id: _ = models.keys().next().unwrap().clone();
            *self.default_model_id.lock().unwrap() = Some(model_id);
        }
        Ok(())
    }
    /// 加载模型
    pub async fn load_model(
        &self,
        model_id: &str,
    ) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let start_time: _ = Instant::now();
        let need_load: _ = {
            let models: _ = self.models.lock().unwrap();
            if let Some(model) = models.get(model_id) {
                !model.is_loaded
            } else {
                return Err("模型不存在".into());
            }
        };
        if need_load {
            // 模拟模型加载
            tokio::time::sleep(Duration::from_millis(100)).await;
            let mut models = self.models.lock().unwrap();
            if let Some(model) = models.get_mut(model_id) {
                model.is_loaded = true;
                model.load_time = Some(start_time);
            }
            println!(
                "模型 {} 加载完成，耗时: {:.2}ms",
                model_id,
                start_time.elapsed().as_secs_f64() * 1000.0
            );
        }
        Ok(())
    }
    /// 卸载模型
    pub async fn unload_model(
        &self,
        model_id: &str,
    ) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let need_unload: _ = {
            let models: _ = self.models.lock().unwrap();
            if let Some(model) = models.get(model_id) {
                model.is_loaded
            } else {
                return Err("模型不存在".into());
            }
        };
        if need_unload {
            // 模拟模型卸载
            tokio::time::sleep(Duration::from_millis(50)).await;
            let mut models = self.models.lock().unwrap();
            if let Some(model) = models.get_mut(model_id) {
                model.is_loaded = false;
                model.load_time = None;
            }
            println!("模型 {} 已卸载", model_id);
        }
        Ok(())
    }
    /// 调用模型
    pub async fn call_model(
        &self,
        model_id: Option<&str>,
        input: ModelInput,
    ) -> Result<ModelOutput, Box<dyn std::error::Error + Send + Sync>> {
        let actual_model_id: _ = if let Some(id) = model_id {
            id.to_string()
        } else {
            self.get_default_model_id()?
        };
        let start_time: _ = Instant::now();
        // 检查模型是否存在且已加载
        let (model_type, _config) = {
            let models: _ = self.models.lock().unwrap();
            if let Some(model) = models.get(&actual_model_id) {
                if !model.is_loaded {
                    return Err("模型未加载".into());
                }
                (model.config.model_type.clone(), model.config.clone())
            } else {
                return Err("模型不存在".into());
            }
        };
        // 执行模型推理
        let output: _ = self.execute_inference(&model_type, input).await?;
        let latency: _ = start_time.elapsed();
        // 更新指标
        {
            let models: _ = self.models.lock().unwrap();
            if let Some(model) = models.get(&actual_model_id) {
                let mut metrics = model.metrics.lock().unwrap();
                metrics.update(latency, true);
            }
        }
        Ok(output)
    }
    /// 执行模型推理
    async fn execute_inference(
        &self,
        model_type: &ModelType,
        input: ModelInput,
    ) -> Result<ModelOutput, Box<dyn std::error::Error + Send + Sync>> {
        match (model_type, input) {
            (
                ModelType::LanguageModel { .. },
                ModelInput::Text {
                    content,
                    max_tokens: _,
                    temperature: _,
                },
            ) => {
                // 模拟文本生成
                tokio::time::sleep(Duration::from_millis(50)).await;
                Ok(ModelOutput::Text {
                    content: format!("Generated: {}", content),
                    tokens_used: content.len() / 4,
                    finish_reason: "stop".to_string(),
                })
            }
            (ModelType::ImageClassifier { .. }, ModelInput::Image { data: _, format: _ }) => {
                // 模拟图像分类
                tokio::time::sleep(Duration::from_millis(100)).await;
                Ok(ModelOutput::Classification {
                    predictions: vec![
                        ("cat".to_string(), 0.85),
                        ("dog".to_string(), 0.75),
                        ("bird".to_string(), 0.65),
                    ],
                    confidence: 0.85,
                })
            }
            (ModelType::EmbeddingModel { dimensions, .. }, ModelInput::Embedding { text: _ }) => {
                // 模拟嵌入生成
                tokio::time::sleep(Duration::from_millis(30)).await;
                Ok(ModelOutput::Embedding {
                    vector: vec![0.1; *dimensions],
                    dimensions: *dimensions,
                })
            }
            (ModelType::TextToSpeech { .. }, ModelInput::Text { content: _, .. }) => {
                // 模拟文本转语音
                tokio::time::sleep(Duration::from_millis(200)).await;
                Ok(ModelOutput::Audio {
                    data: vec![0; 44100], // 1秒的音频数据
                    format: "wav".to_string(),
                    duration: Duration::from_secs(1),
                })
            }
            (
                ModelType::SpeechToText { .. },
                ModelInput::Audio {
                    data: _, format: _, ..
                },
            ) => {
                // 模拟语音识别
                tokio::time::sleep(Duration::from_millis(150)).await;
                Ok(ModelOutput::Text {
                    content: "Transcribed speech".to_string(),
                    tokens_used: 3,
                    finish_reason: "stop".to_string(),
                })
            }
            (
                ModelType::TranslationModel { .. },
                ModelInput::Translation {
                    text,
                    source_lang,
                    target_lang,
                },
            ) => {
                // 模拟翻译
                tokio::time::sleep(Duration::from_millis(80)).await;
                Ok(ModelOutput::Translation {
                    translated_text: format!("Translated: {}", text),
                    source_lang: source_lang.clone(),
                    target_lang: target_lang.clone(),
                    confidence: 0.92,
                })
            }
            _ => Err("模型类型与输入不匹配".into()),
        }
    }
    /// 获取默认模型ID
    fn get_default_model_id(&self) -> Result<String, Box<dyn std::error::Error + Send + Sync>> {
        let default_id: _ = self.default_model_id.lock().unwrap();
        if let Some(id) = default_id.as_ref() {
            Ok(id.clone())
        } else {
            Err("未设置默认模型".into())
        }
    }
    /// 获取模型列表
    pub fn list_models(&self) -> Vec<String> {
        let models: _ = self.models.lock().unwrap();
        models.keys().cloned().collect()
    }
    /// 获取模型指标
    pub fn get_model_metrics(&self, model_id: &str) -> Option<ModelMetrics> {
        let models: _ = self.models.lock().unwrap();
        models
            .get(model_id)
            .map(|m| m.metrics.lock().unwrap().clone())
    }
    /// 获取所有模型的汇总指标
    pub fn get_all_metrics(&self) -> HashMap<String, ModelMetrics> {
        let models: _ = self.models.lock().unwrap();
        let mut metrics = HashMap::new();
        for (id, model) in models.iter() {
            metrics.insert(id.clone(), model.metrics.lock().unwrap().clone());
        }
        metrics
    }
    /// 设置默认模型
    pub fn set_default_model(
        &self,
        model_id: &str,
    ) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let models: _ = self.models.lock().unwrap();
        if models.contains_key(model_id) {
            *self.default_model_id.lock().unwrap() = Some(model_id.to_string());
            Ok(())
        } else {
            Err("模型不存在".into())
        }
    }
    /// 设置路由策略
    pub fn set_routing_strategy(&self, strategy: ModelRoutingStrategy) {
        *self.routing_strategy.lock().unwrap() = strategy;
    }
    /// 健康检查
    pub async fn health_check(&self) -> HashMap<String, bool> {
        let models: _ = self.models.lock().unwrap();
        let mut health_status = HashMap::new();
        for (id, model) in models.iter() {
            // 模型健康条件：
            // 1. 必须已加载
            // 2. 如果有请求记录，成功率必须 > 0.5
            // 3. 如果没有请求记录（全新加载），也认为是健康的
            let metrics: _ = model.metrics.lock().unwrap();
            let has_requests: _ = metrics.total_requests > 0;
            let success_rate: _ = metrics.success_rate();
            let is_healthy: _ = model.is_loaded && (!has_requests || success_rate > 0.5);
            health_status.insert(id.clone(), is_healthy);
        }
        health_status
    }
    /// 清理资源
    pub async fn cleanup(&self) -> Result<(), Box<dyn std::error::Error + Send + Sync>> {
        let model_ids: Vec<String> = {
            let models: _ = self.models.lock().unwrap();
            models.keys().cloned().collect()
        };
        for model_id in model_ids {
            self.unload_model(&model_id).await?;
        }
        Ok(())
    }
}
/// 便利函数：创建文本生成模型
#[allow(dead_code)]
pub fn create_text_generation_model(model_id: &str) -> AiModel {
    let config: _ = ModelConfig {
        model_type: ModelType::LanguageModel {
            model_name: "gpt-3.5-turbo".to_string(),
            max_tokens: 4096,
            temperature: 0.7,
        },
        max_batch_size: 10,
        timeout: Duration::from_secs(30),
        retry_count: 3,
        enable_caching: true,
        cache_ttl: Duration::from_secs(3600),
    };
    AiModel {
        id: model_id.to_string(),
        config,
        is_loaded: false,
        load_time: None,
        metrics: Arc::new(Mutex::new(ModelMetrics::default()))
    }
}
/// 便利函数：创建图像分类模型
#[allow(dead_code)]
pub fn create_image_classification_model(model_id: &str) -> AiModel {
    let config: _ = ModelConfig {
        model_type: ModelType::ImageClassifier {
            model_name: "resnet50".to_string(),
            input_size: (224, 224),
            num_classes: 1000,
        },
        max_batch_size: 32,
        timeout: Duration::from_secs(10),
        retry_count: 2,
        enable_caching: false,
        cache_ttl: Duration::from_secs(0),
    };
    AiModel {
        id: model_id.to_string(),
        config,
        is_loaded: false,
        load_time: None,
        metrics: Arc::new(Mutex::new(ModelMetrics::default()))
    }
}
#[cfg(test)]
mod tests {
    #[tokio::test]
    async fn test_model_manager_creation() {
        let manager: _ = AiModelManager::new();
        let models: _ = manager.list_models();
        assert!(models.is_empty());
    }
    #[tokio::test]
    async fn test_register_model() {
        let manager: _ = AiModelManager::new();
        let model: _ = create_text_generation_model("test-model");
        let result: _ = manager.register_model(model);
        assert!(result.is_ok());
        assert_eq!(manager.list_models(), vec!["test-model"]);
    }
    #[tokio::test]
    async fn test_load_model() {
        let manager: _ = AiModelManager::new();
        let model: _ = create_text_generation_model("test-model");
        manager.register_model(model).unwrap();
        let result: _ = manager.load_model("test-model").await;
        assert!(result.is_ok());
        let models: _ = manager.models.lock().unwrap();
        assert!(models.get("test-model").unwrap().is_loaded);
    }
    #[tokio::test]
    async fn test_call_model() {
        let manager: _ = AiModelManager::new();
        let model: _ = create_text_generation_model("test-model");
        manager.register_model(model).unwrap();
        manager.load_model("test-model").await.unwrap();
        let input: _ = ModelInput::Text {
            content: "Hello, world!".to_string(),
            max_tokens: Some(100),
            temperature: Some(0.7),
        };
        let result: _ = manager.call_model(Some("test-model"), input).await;
        assert!(result.is_ok());
        if let Ok(ModelOutput::Text { content, .. }) = result {
            assert!(content.contains("Generated:"));
        }
    }
    #[test]
    fn test_model_metrics() {
        let mut metrics = ModelMetrics::default();
        metrics.update(Duration::from_millis(100), true);
        metrics.update(Duration::from_millis(200), true);
        assert_eq!(metrics.total_requests, 2);
        assert_eq!(metrics.successful_requests, 2);
        assert_eq!(metrics.failed_requests, 0);
        assert_eq!(metrics.success_rate(), 1.0);
    }
    #[tokio::test]
    async fn test_health_check() {
        let manager: _ = AiModelManager::new();
        let model: _ = create_text_generation_model("test-model");
        manager.register_model(model).unwrap();
        manager.load_model("test-model").await.unwrap();
        let health: _ = manager.health_check().await;
        assert!(health.contains_key("test-model"));
        assert!(health.get("test-model").unwrap());
    }
    #[test]
    fn test_create_text_generation_model() {
        let model: _ = create_text_generation_model("gpt-3.5");
        assert_eq!(model.id, "gpt-3.5");
        assert!(!model.is_loaded);
    }
    #[test]
    fn test_create_image_classification_model() {
        let model: _ = create_image_classification_model("resnet50");
        assert_eq!(model.id, "resnet50");
        assert!(!model.is_loaded);
    }
}