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backend/ai-core/internal/llm/adapter.go

@@ -0,0 +1,83 @@
+package llm
+
+import (
+	"context"
+	"io"
+
+	"github.com/yourname/cyrene-ai/ai-core/internal/model"
+)
+
+// Adapter LLM适配器接口
+// 支持不同的LLM后端（OpenAI、Ollama、vLLM等）
+type Adapter struct {
+	provider LLMProvider
+}
+
+// LLMProvider LLM提供商接口
+type LLMProvider interface {
+	// Chat 同步对话
+	Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error)
+
+	// ChatStream 流式对话，返回一个channel逐token推送
+	ChatStream(ctx context.Context, messages []model.LLMMessage) (<-chan StreamChunk, error)
+
+	// ModelName 返回当前使用的模型名称
+	ModelName() string
+}
+
+// StreamChunk 流式响应的单个片段
+type StreamChunk struct {
+	Content string      // delta内容
+	Done    bool        // 是否为最后一块
+	Error   error       // 错误信息
+	Usage   *model.Usage // 最后一块时返回token统计
+}
+
+// NewAdapter 创建LLM适配器
+func NewAdapter(provider LLMProvider) *Adapter {
+	return &Adapter{provider: provider}
+}
+
+// Chat 同步对话
+func (a *Adapter) Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error) {
+	return a.provider.Chat(ctx, messages)
+}
+
+// ChatStream 流式对话
+func (a *Adapter) ChatStream(ctx context.Context, messages []model.LLMMessage) (<-chan StreamChunk, error) {
+	return a.provider.ChatStream(ctx, messages)
+}
+
+// ModelName 返回模型名称
+func (a *Adapter) ModelName() string {
+	return a.provider.ModelName()
+}
+
+// collectStream 辅助函数：将流式响应收集为完整响应
+func collectStream(ch <-chan StreamChunk) (*model.LLMResponse, error) {
+	var content string
+	var lastUsage *model.Usage
+
+	for chunk := range ch {
+		if chunk.Error != nil {
+			return nil, chunk.Error
+		}
+		if chunk.Done {
+			lastUsage = chunk.Usage
+			break
+		}
+		content += chunk.Content
+	}
+
+	resp := &model.LLMResponse{
+		Content:      content,
+		FinishReason: "stop",
+	}
+	if lastUsage != nil {
+		resp.Usage = *lastUsage
+	}
+	return resp, nil
+}
+
+// Ensure io is used (will be needed for SSE parsing)
+var _ io.Reader

backend/ai-core/internal/llm/openai.go

@@ -0,0 +1,313 @@
+package llm
+
+import (
+	"bufio"
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"io"
+	"log"
+	"net/http"
+	"strings"
+	"time"
+
+	"github.com/yourname/cyrene-ai/ai-core/internal/model"
+)
+
+// OpenAIConfig OpenAI适配器配置
+type OpenAIConfig struct {
+	BaseURL        string        // API基础URL
+	APIKey         string        // API密钥
+	Model          string        // 主模型
+	FallbackModel  string        // 备用模型（主模型不可用时）
+	MaxRetries     int           // 最大重试次数
+	Timeout        time.Duration // 请求超时
+}
+
+// OpenAIProvider OpenAI兼容的LLM提供商
+type OpenAIProvider struct {
+	config     OpenAIConfig
+	httpClient *http.Client
+}
+
+// NewOpenAIProvider 创建OpenAI提供商
+func NewOpenAIProvider(cfg OpenAIConfig) *OpenAIProvider {
+	if cfg.MaxRetries == 0 {
+		cfg.MaxRetries = 3
+	}
+	if cfg.Timeout == 0 {
+		cfg.Timeout = 60 * time.Second
+	}
+
+	return &OpenAIProvider{
+		config: cfg,
+		httpClient: &http.Client{
+			Timeout: cfg.Timeout,
+		},
+	}
+}
+
+// openAIRequest OpenAI请求结构
+type openAIRequest struct {
+	Model       string             `json:"model"`
+	Messages    []openAIMessage    `json:"messages"`
+	Temperature float64            `json:"temperature"`
+	MaxTokens   int                `json:"max_tokens,omitempty"`
+	Stream      bool               `json:"stream"`
+}
+
+type openAIMessage struct {
+	Role    string `json:"role"`
+	Content string `json:"content"`
+}
+
+// openAIResponse OpenAI响应结构
+type openAIResponse struct {
+	ID      string           `json:"id"`
+	Object  string           `json:"object"`
+	Choices []openAIChoice   `json:"choices"`
+	Usage   openAIUsage      `json:"usage,omitempty"`
+	Error   *openAIError     `json:"error,omitempty"`
+}
+
+type openAIChoice struct {
+	Index        int           `json:"index"`
+	Message      openAIMessage `json:"message"`
+	FinishReason string        `json:"finish_reason"`
+}
+
+type openAIUsage struct {
+	PromptTokens     int `json:"prompt_tokens"`
+	CompletionTokens int `json:"completion_tokens"`
+	TotalTokens      int `json:"total_tokens"`
+}
+
+type openAIError struct {
+	Message string `json:"message"`
+	Type    string `json:"type"`
+	Code    string `json:"code,omitempty"`
+}
+
+// Chat 同步对话
+func (p *OpenAIProvider) Chat(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error) {
+	resp, err := p.doChat(ctx, messages, p.config.Model, false)
+	if err != nil {
+		// 尝试fallback模型
+		if p.config.FallbackModel != "" && p.config.FallbackModel != p.config.Model {
+			log.Printf("[LLM] 主模型 %s 调用失败，降级到 %s: %v", p.config.Model, p.config.FallbackModel, err)
+			return p.doChat(ctx, messages, p.config.FallbackModel, false)
+		}
+		return nil, err
+	}
+	return resp, nil
+}
+
+// ChatStream 流式对话
+func (p *OpenAIProvider) ChatStream(ctx context.Context, messages []model.LLMMessage) (<-chan StreamChunk, error) {
+	ch := make(chan StreamChunk, 100)
+
+	go func() {
+		defer close(ch)
+
+		resp, err := p.doChatStream(ctx, messages, p.config.Model)
+		if err != nil {
+			// Fallback
+			if p.config.FallbackModel != "" {
+				log.Printf("[LLM] 流式调用主模型失败，降级: %v", err)
+				resp, err = p.doChatStream(ctx, messages, p.config.FallbackModel)
+			}
+			if err != nil {
+				ch <- StreamChunk{Error: err, Done: true}
+				return
+			}
+		}
+		defer resp.Body.Close()
+
+		scanner := bufio.NewScanner(resp.Body)
+		// 增大scanner buffer以处理大块SSE数据
+		scanner.Buffer(make([]byte, 0, 64*1024), 1024*1024)
+
+		for scanner.Scan() {
+			line := scanner.Text()
+
+			// SSE格式: data: {...}
+			if !strings.HasPrefix(line, "data: ") {
+				continue
+			}
+
+			data := strings.TrimPrefix(line, "data: ")
+
+			// 流结束标记
+			if data == "[DONE]" {
+				ch <- StreamChunk{Done: true}
+				return
+			}
+
+			var streamResp openAIStreamResponse
+			if err := json.Unmarshal([]byte(data), &streamResp); err != nil {
+				continue
+			}
+
+			if len(streamResp.Choices) > 0 {
+				delta := streamResp.Choices[0].Delta
+				if delta.Content != "" {
+					ch <- StreamChunk{Content: delta.Content}
+				}
+				if streamResp.Choices[0].FinishReason != "" {
+					usage := &model.Usage{}
+					if streamResp.Usage != nil {
+						usage.PromptTokens = streamResp.Usage.PromptTokens
+						usage.CompletionTokens = streamResp.Usage.CompletionTokens
+						usage.TotalTokens = streamResp.Usage.TotalTokens
+					}
+					ch <- StreamChunk{Done: true, Usage: usage}
+					return
+				}
+			}
+		}
+
+		if err := scanner.Err(); err != nil {
+			ch <- StreamChunk{Error: fmt.Errorf("读取流式响应失败: %w", err), Done: true}
+			return
+		}
+
+		ch <- StreamChunk{Done: true}
+	}()
+
+	return ch, nil
+}
+
+// openAIStreamResponse 流式响应结构
+type openAIStreamResponse struct {
+	ID      string                `json:"id"`
+	Object  string                `json:"object"`
+	Choices []openAIStreamChoice  `json:"choices"`
+	Usage   *openAIUsage          `json:"usage,omitempty"`
+}
+
+type openAIStreamChoice struct {
+	Index        int            `json:"index"`
+	Delta        openAIMessage  `json:"delta"`
+	FinishReason string         `json:"finish_reason"`
+}
+
+// doChat 执行同步对话请求
+func (p *OpenAIProvider) doChat(ctx context.Context, messages []model.LLMMessage, model string, stream bool) (*model.LLMResponse, error) {
+	// 转换消息格式
+	oaiMessages := make([]openAIMessage, len(messages))
+	for i, msg := range messages {
+		oaiMessages[i] = openAIMessage{
+			Role:    string(msg.Role),
+			Content: msg.Content,
+		}
+	}
+
+	reqBody := openAIRequest{
+		Model:       model,
+		Messages:    oaiMessages,
+		Temperature: 0.8,
+		Stream:      stream,
+	}
+
+	jsonBody, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("序列化请求失败: %w", err)
+	}
+
+	req, err := http.NewRequestWithContext(ctx, "POST", p.config.BaseURL+"/chat/completions", bytes.NewReader(jsonBody))
+	if err != nil {
+		return nil, fmt.Errorf("创建请求失败: %w", err)
+	}
+
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Authorization", "Bearer "+p.config.APIKey)
+
+	resp, err := p.httpClient.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("请求失败: %w", err)
+	}
+	defer resp.Body.Close()
+
+	body, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return nil, fmt.Errorf("读取响应失败: %w", err)
+	}
+
+	if resp.StatusCode != http.StatusOK {
+		var errResp openAIResponse
+		if json.Unmarshal(body, &errResp) == nil && errResp.Error != nil {
+			return nil, fmt.Errorf("API错误 [%s]: %s", errResp.Error.Code, errResp.Error.Message)
+		}
+		return nil, fmt.Errorf("API返回状态码 %d: %s", resp.StatusCode, string(body))
+	}
+
+	var oaiResp openAIResponse
+	if err := json.Unmarshal(body, &oaiResp); err != nil {
+		return nil, fmt.Errorf("解析响应失败: %w", err)
+	}
+
+	if len(oaiResp.Choices) == 0 {
+		return nil, fmt.Errorf("API返回空choices")
+	}
+
+	return &model.LLMResponse{
+		Content:      oaiResp.Choices[0].Message.Content,
+		FinishReason: oaiResp.Choices[0].FinishReason,
+		Usage: model.Usage{
+			PromptTokens:     oaiResp.Usage.PromptTokens,
+			CompletionTokens: oaiResp.Usage.CompletionTokens,
+			TotalTokens:      oaiResp.Usage.TotalTokens,
+		},
+	}, nil
+}
+
+// doChatStream 执行流式对话请求（返回原始HTTP响应）
+func (p *OpenAIProvider) doChatStream(ctx context.Context, messages []model.LLMMessage, model string) (*http.Response, error) {
+	oaiMessages := make([]openAIMessage, len(messages))
+	for i, msg := range messages {
+		oaiMessages[i] = openAIMessage{
+			Role:    string(msg.Role),
+			Content: msg.Content,
+		}
+	}
+
+	reqBody := openAIRequest{
+		Model:       model,
+		Messages:    oaiMessages,
+		Temperature: 0.8,
+		Stream:      true,
+	}
+
+	jsonBody, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("序列化请求失败: %w", err)
+	}
+
+	req, err := http.NewRequestWithContext(ctx, "POST", p.config.BaseURL+"/chat/completions", bytes.NewReader(jsonBody))
+	if err != nil {
+		return nil, fmt.Errorf("创建请求失败: %w", err)
+	}
+
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Authorization", "Bearer "+p.config.APIKey)
+	req.Header.Set("Accept", "text/event-stream")
+
+	resp, err := p.httpClient.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("请求失败: %w", err)
+	}
+
+	if resp.StatusCode != http.StatusOK {
+		defer resp.Body.Close()
+		body, _ := io.ReadAll(resp.Body)
+		return nil, fmt.Errorf("API返回状态码 %d: %s", resp.StatusCode, string(body))
+	}
+
+	return resp, nil
+}
+
+// ModelName 返回模型名称
+func (p *OpenAIProvider) ModelName() string {
+	return p.config.Model
+}

backend/ai-core/internal/llm/stream.go

@@ -0,0 +1,191 @@
+package llm
+
+import (
+	"strings"
+	"sync"
+	"unicode"
+)
+
+// Segmenter 断句器 —— 将流式文本按句号切分为语音播放片段
+type Segmenter struct {
+	mu       sync.Mutex
+	buffer   strings.Builder
+	segments []Segment
+	index    int
+}
+
+// Segment 语音片段
+type Segment struct {
+	Index int    `json:"index"`
+	Text  string `json:"text"`
+}
+
+// NewSegmenter 创建断句器
+func NewSegmenter() *Segmenter {
+	return &Segmenter{}
+}
+
+// Feed 喂入新的文本片段
+// 返回已完成的断句列表
+func (s *Segmenter) Feed(delta string) []Segment {
+	s.mu.Lock()
+	defer s.mu.Unlock()
+
+	s.buffer.WriteString(delta)
+	content := s.buffer.String()
+
+	var newSegments []Segment
+
+	for {
+		idx := findSentenceEnd(content)
+		if idx == -1 {
+			break
+		}
+
+		segmentText := strings.TrimSpace(content[:idx+len(string(content[idx]))])
+		// 检查是否是完整中文字符的句末
+		// idx 指向标点符号的位置
+		runes := []rune(content)
+		var byteIdx int
+		for i, r := range runes {
+			if i == idx {
+				// 标点之后的字符
+				break
+			}
+			byteIdx += len(string(r))
+		}
+
+		// 简化处理：直接取到idx+1字节 (对于ASCII标点)
+		// 对于中文标点，需要用rune处理
+		realIdx := 0
+		runeCount := 0
+		for i, r := range content {
+			if runeCount == idx {
+				realIdx = i
+				break
+			}
+			runeCount++
+			_ = r
+		}
+		// 包含标点符号本身
+		endIdx := realIdx + len(string([]rune(content)[idx]))
+		if endIdx <= realIdx {
+			endIdx = realIdx + 3 // fallback for UTF-8 multi-byte
+		}
+
+		segmentText = strings.TrimSpace(content[:endIdx])
+		if segmentText == "" {
+			content = strings.TrimSpace(content[endIdx:])
+			s.buffer.Reset()
+			s.buffer.WriteString(content)
+			continue
+		}
+
+		s.index++
+		seg := Segment{
+			Index: s.index,
+			Text:  segmentText,
+		}
+		s.segments = append(s.segments, seg)
+		newSegments = append(newSegments, seg)
+
+		// 更新buffer，移除已处理的部分
+		content = strings.TrimSpace(content[endIdx:])
+		s.buffer.Reset()
+		s.buffer.WriteString(content)
+	}
+
+	return newSegments
+}
+
+// Flush 强制输出buffer中剩余的内容
+func (s *Segmenter) Flush() *Segment {
+	s.mu.Lock()
+	defer s.mu.Unlock()
+
+	remaining := strings.TrimSpace(s.buffer.String())
+	if remaining == "" {
+		return nil
+	}
+
+	s.index++
+	seg := Segment{
+		Index: s.index,
+		Text:  remaining,
+	}
+	s.segments = append(s.segments, seg)
+	s.buffer.Reset()
+
+	return &seg
+}
+
+// AllSegments 返回所有已完成的断句
+func (s *Segmenter) AllSegments() []Segment {
+	s.mu.Lock()
+	defer s.mu.Unlock()
+
+	result := make([]Segment, len(s.segments))
+	copy(result, s.segments)
+	return result
+}
+
+// findSentenceEnd 查找句子结束位置（返回标点符号在rune数组中的索引）
+// 中文标点：。！？ 英文标点：. ! ?
+func findSentenceEnd(text string) int {
+	runes := []rune(text)
+	for i, r := range runes {
+		if isSentenceEnd(r) {
+			return i
+		}
+	}
+	return -1
+}
+
+// isSentenceEnd 判断是否为句末标点
+func isSentenceEnd(r rune) bool {
+	switch r {
+	case '。', '！', '？', '.', '!', '?', '\n':
+		return true
+	}
+	return false
+}
+
+// splitIntoSegments 将完整文本按句号断句（用于post-processing）
+func splitIntoSegments(text string) []Segment {
+	var segments []Segment
+	runes := []rune(text)
+
+	start := 0
+	index := 0
+
+	for i, r := range runes {
+		if isSentenceEnd(r) {
+			segText := strings.TrimSpace(string(runes[start : i+1]))
+			if segText != "" {
+				index++
+				segments = append(segments, Segment{
+					Index: index,
+					Text:  segText,
+				})
+			}
+			start = i + 1
+		}
+	}
+
+	// 处理末尾无标点的剩余文本
+	if start < len(runes) {
+		remaining := strings.TrimSpace(string(runes[start:]))
+		if remaining != "" {
+			index++
+			segments = append(segments, Segment{
+				Index: index,
+				Text:  remaining,
+			})
+		}
+	}
+
+	return segments
+}
+
+// Ensure unicode is used
+var _ = unicode.Is

backend/ai-core/internal/memory/extractor.go

@@ -0,0 +1,206 @@
+package memory
+
+import (
+	"context"
+	"encoding/json"
+	"fmt"
+	"log"
+	"strings"
+
+	"github.com/yourname/cyrene-ai/ai-core/internal/model"
+)
+
+// Extractor 记忆提取器 —— 从对话中提取结构化记忆
+type Extractor struct {
+	store    *Store
+	llmChat  func(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error)
+}
+
+// NewExtractor 创建记忆提取器
+// llmChat: LLM对话函数，用于分析对话内容并提取记忆
+// 如果为nil，则使用规则提取（降级模式）
+func NewExtractor(store *Store, llmChat func(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error)) *Extractor {
+	return &Extractor{
+		store:   store,
+		llmChat: llmChat,
+	}
+}
+
+// ExtractAndStore 从一轮对话中提取记忆并存储
+// 异步执行，不阻塞主流程
+func (e *Extractor) ExtractAndStore(ctx context.Context, userID, sessionID, userMessage, assistantResponse string) {
+	memories, err := e.extract(ctx, userMessage, assistantResponse)
+	if err != nil {
+		log.Printf("[memory] 记忆提取失败: %v", err)
+		return
+	}
+
+	for _, mem := range memories {
+		mem.UserID = userID
+		mem.SessionID = sessionID
+
+		if err := e.store.Save(ctx, &mem); err != nil {
+			log.Printf("[memory] 记忆保存失败: %v", err)
+			continue
+		}
+		log.Printf("[memory] 新记忆已保存 [%s]: %s", mem.Category, mem.Summary)
+	}
+}
+
+// extract 从对话中提取记忆
+func (e *Extractor) extract(ctx context.Context, userMessage, assistantResponse string) ([]model.MemoryEntry, error) {
+	// 如果有LLM，使用LLM提取
+	if e.llmChat != nil {
+		return e.extractWithLLM(ctx, userMessage, assistantResponse)
+	}
+	// 降级：规则提取
+	return e.extractWithRules(userMessage, assistantResponse), nil
+}
+
+// MemoryExtractionResult LLM提取结果的结构
+type MemoryExtractionResult struct {
+	Memories []struct {
+		Content  string `json:"content"`
+		Summary  string `json:"summary"`
+		Category string `json:"category"`
+		Priority int    `json:"priority"`
+	} `json:"memories"`
+}
+
+// extractWithLLM 使用LLM提取记忆
+func (e *Extractor) extractWithLLM(ctx context.Context, userMessage, assistantResponse string) ([]model.MemoryEntry, error) {
+	prompt := fmt.Sprintf(`分析以下对话，提取关于用户（开拓者）的重要信息作为记忆。
+
+用户消息: %s
+昔涟回复: %s
+
+请以JSON格式返回提取的记忆。每条记忆需要包含：
+- content: 完整的记忆内容（一句话描述）
+- summary: 简短摘要（10字以内）
+- category: 分类 (preference/fact/event/relationship/habit/other)
+- priority: 优先级 (0=临时, 1=普通, 2=重要, 3=核心)
+
+只提取有意义的信息，不要提取无意义的闲聊。如果没有值得记住的内容，返回空数组。
+
+输出格式:
+{"memories": [{"content": "...", "summary": "...", "category": "...", "priority": 1}]}
+`, userMessage, assistantResponse)
+
+	resp, err := e.llmChat(ctx, []model.LLMMessage{
+		{Role: "system", Content: "你是一个记忆提取助手。你只输出JSON格式的结果，不输出其他内容。"},
+		{Role: "user", Content: prompt},
+	})
+	if err != nil {
+		return nil, fmt.Errorf("LLM提取记忆失败: %w", err)
+	}
+
+	// 解析JSON
+	result := MemoryExtractionResult{}
+	content := extractJSON(resp.Content)
+	if err := json.Unmarshal([]byte(content), &result); err != nil {
+		// 尝试作为数组解析
+		var arrResult []struct {
+			Content  string `json:"content"`
+			Summary  string `json:"summary"`
+			Category string `json:"category"`
+			Priority int    `json:"priority"`
+		}
+		if err2 := json.Unmarshal([]byte(content), &arrResult); err2 != nil {
+			return nil, fmt.Errorf("解析记忆JSON失败: %w (原始: %s)", err, content[:min(len(content), 100)])
+		}
+		result.Memories = arrResult
+	}
+
+	var entries []model.MemoryEntry
+	for _, m := range result.Memories {
+		entries = append(entries, model.MemoryEntry{
+			Content:  m.Content,
+			Summary:  m.Summary,
+			Category: model.MemoryCategory(m.Category),
+			Priority: model.MemoryPriority(m.Priority),
+		})
+	}
+
+	return entries, nil
+}
+
+// extractWithRules 基于规则提取记忆（降级方案）
+func (e *Extractor) extractWithRules(userMessage, _ string) []model.MemoryEntry {
+	var entries []model.MemoryEntry
+
+	// 规则1: 检测用户偏好表达
+	prefPatterns := map[string]string{
+		"喜欢":     "preference",
+		"爱":       "preference",
+		"最喜欢":   "preference",
+		"讨厌":     "preference",
+		"不喜欢":   "preference",
+		"经常":     "habit",
+		"每天都":   "habit",
+		"一直":     "habit",
+		"我叫":     "fact",
+		"我是":     "fact",
+		"我家":     "fact",
+		"住在":     "fact",
+		"生日":     "fact",
+	}
+
+	for pattern, category := range prefPatterns {
+		if idx := strings.Index(userMessage, pattern); idx != -1 {
+			// 提取包含关键词的句子片段
+			start := max(0, idx-5)
+			end := min(len([]rune(userMessage)), idx+len([]rune(pattern))+15)
+			content := strings.TrimSpace(string([]rune(userMessage)[start:end]))
+
+			entries = append(entries, model.MemoryEntry{
+				Content:  content,
+				Summary:  truncateString(content, 20),
+				Category: model.MemoryCategory(category),
+				Priority: model.MemoryNormal,
+			})
+			break // 每条消息最多提取一条规则记忆
+		}
+	}
+
+	return entries
+}
+
+// extractJSON 从LLM回复中提取JSON内容
+func extractJSON(text string) string {
+	text = strings.TrimSpace(text)
+
+	// 移除 markdown 代码块标记
+	if strings.HasPrefix(text, "```json") {
+		text = strings.TrimPrefix(text, "```json")
+		text = strings.TrimSuffix(text, "```")
+		text = strings.TrimSpace(text)
+	} else if strings.HasPrefix(text, "```") {
+		text = strings.TrimPrefix(text, "```")
+		text = strings.TrimSuffix(text, "```")
+		text = strings.TrimSpace(text)
+	}
+
+	return text
+}
+
+func truncateString(s string, maxLen int) string {
+	runes := []rune(s)
+	if len(runes) <= maxLen {
+		return s
+	}
+	return string(runes[:maxLen]) + "..."
+}
+
+func min(a, b int) int {
+	if a < b {
+		return a
+	}
+	return b
+}
+
+func max(a, b int) int {
+	if a > b {
+		return a
+	}
+	return b
+}

backend/ai-core/internal/memory/retriever.go

@@ -0,0 +1,152 @@
+package memory
+
+import (
+	"context"
+	"fmt"
+	"strings"
+
+	"github.com/yourname/cyrene-ai/ai-core/internal/model"
+)
+
+// MemoryEntry 记忆条目别名（避免与model包冲突）
+type MemoryEntry = model.MemoryEntry
+
+// Retriever 记忆检索器
+type Retriever struct {
+	store     *Store
+	embedder  Embedder // 文本转向量的接口
+}
+
+// Embedder 文本嵌入接口
+type Embedder interface {
+	Embed(ctx context.Context, text string) ([]float64, error)
+}
+
+// SimpleEmbedder 基于关键词的简单嵌入（MVP阶段可用，无需外部API）
+type SimpleEmbedder struct{}
+
+// Embed 简单的关键词哈希嵌入（用于MVP快速验证）
+func (e *SimpleEmbedder) Embed(ctx context.Context, text string) ([]float64, error) {
+	// 生成一个简单的1536维特征向量
+	// 基于字符频率的简单表示，用于MVP阶段
+	vec := make([]float64, 1536)
+
+	runes := []rune(strings.ToLower(text))
+	for i, r := range runes {
+		idx := int(r) % 1536
+		vec[idx] += 1.0 / float64(len(runes))
+		// 考虑位置信息
+		posIdx := (int(r) + i) % 1536
+		vec[posIdx] += 0.5 / float64(len(runes))
+	}
+
+	return vec, nil
+}
+
+// NewRetriever 创建记忆检索器
+func NewRetriever(store *Store, embedder Embedder) *Retriever {
+	if embedder == nil {
+		embedder = &SimpleEmbedder{}
+	}
+	return &Retriever{
+		store:    store,
+		embedder: embedder,
+	}
+}
+
+// Retrieve 检索与查询相关的记忆
+// 策略: 向量相似度 + 关键词匹配混合
+func (r *Retriever) Retrieve(ctx context.Context, userID string, query string) ([]MemoryEntry, error) {
+	var allEntries []MemoryEntry
+	seen := make(map[string]bool)
+
+	// 1. 向量相似度检索
+	embedding, err := r.embedder.Embed(ctx, query)
+	if err == nil {
+		vecEntries, err := r.store.SearchByVector(ctx, userID, embedding, 5)
+		if err == nil {
+			for _, e := range vecEntries {
+				if !seen[e.ID] {
+					seen[e.ID] = true
+					allEntries = append(allEntries, e)
+				}
+			}
+		}
+	}
+
+	// 2. 关键词匹配检索（核心/重要记忆优先）
+	keywordEntries, err := r.keywordSearch(ctx, userID, query)
+	if err == nil {
+		for _, e := range keywordEntries {
+			if !seen[e.ID] {
+				seen[e.ID] = true
+				allEntries = append(allEntries, e)
+			}
+		}
+	}
+
+	// 3. 如果没有匹配，返回最近的重要记忆
+	if len(allEntries) == 0 {
+		recentEntries, err := r.store.Query(ctx, model.MemoryQuery{
+			UserID:   userID,
+			Priority: int(model.MemoryImportant),
+			Limit:    3,
+		})
+		if err == nil {
+			allEntries = recentEntries
+		}
+	}
+
+	// 限制返回数量
+	if len(allEntries) > 10 {
+		allEntries = allEntries[:10]
+	}
+
+	return allEntries, nil
+}
+
+// keywordSearch 关键词匹配检索
+func (r *Retriever) keywordSearch(ctx context.Context, userID string, query string) ([]MemoryEntry, error) {
+	// 查询最近的核心和重要记忆
+	entries, err := r.store.Query(ctx, model.MemoryQuery{
+		UserID:   userID,
+		Priority: int(model.MemoryImportant),
+		Limit:    50,
+	})
+	if err != nil {
+		return nil, err
+	}
+
+	// 简单的关键词匹配过滤
+	var matched []MemoryEntry
+	queryLower := strings.ToLower(query)
+
+	for _, entry := range entries {
+		contentLower := strings.ToLower(entry.Content)
+		summaryLower := strings.ToLower(entry.Summary)
+		if strings.Contains(contentLower, queryLower) || strings.Contains(summaryLower, queryLower) {
+			matched = append(matched, entry)
+		}
+	}
+
+	// 也匹配普通记忆
+	normalEntries, err := r.store.Query(ctx, model.MemoryQuery{
+		UserID:   userID,
+		Priority: int(model.MemoryNormal),
+		Limit:    100,
+	})
+	if err == nil {
+		for _, entry := range normalEntries {
+			contentLower := strings.ToLower(entry.Content)
+			summaryLower := strings.ToLower(entry.Summary)
+			if strings.Contains(contentLower, queryLower) || strings.Contains(summaryLower, queryLower) {
+				matched = append(matched, entry)
+			}
+		}
+	}
+
+	return matched, nil
+}
+
+// Ensure fmt is used
+var _ = fmt.Sprintf

backend/ai-core/internal/memory/store.go

@@ -0,0 +1,251 @@
+package memory
+
+import (
+	"context"
+	"database/sql"
+	"fmt"
+	"time"
+
+	"github.com/yourname/cyrene-ai/ai-core/internal/model"
+	_ "github.com/lib/pq"
+)
+
+// Store 记忆持久化存储（PostgreSQL + pgvector）
+type Store struct {
+	db *sql.DB
+}
+
+// NewStore 创建记忆存储
+func NewStore(connStr string) (*Store, error) {
+	db, err := sql.Open("postgres", connStr)
+	if err != nil {
+		return nil, fmt.Errorf("连接数据库失败: %w", err)
+	}
+
+	db.SetMaxOpenConns(25)
+	db.SetMaxIdleConns(5)
+	db.SetConnMaxLifetime(5 * time.Minute)
+
+	if err := db.Ping(); err != nil {
+		return nil, fmt.Errorf("数据库ping失败: %w", err)
+	}
+
+	s := &Store{db: db}
+	if err := s.migrate(); err != nil {
+		return nil, fmt.Errorf("数据库迁移失败: %w", err)
+	}
+
+	return s, nil
+}
+
+// migrate 创建表结构
+func (s *Store) migrate() error {
+	queries := []string{
+		`CREATE EXTENSION IF NOT EXISTS vector`,
+		`CREATE TABLE IF NOT EXISTS memories (
+			id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
+			user_id VARCHAR(64) NOT NULL,
+			content TEXT NOT NULL,
+			summary TEXT DEFAULT '',
+			category VARCHAR(32) DEFAULT 'other',
+			priority INT DEFAULT 1,
+			session_id VARCHAR(64) DEFAULT '',
+			source TEXT DEFAULT '',
+			embedding vector(1536),
+			access_count INT DEFAULT 0,
+			last_access TIMESTAMPTZ DEFAULT NOW(),
+			created_at TIMESTAMPTZ DEFAULT NOW(),
+			expires_at TIMESTAMPTZ
+		)`,
+		`CREATE INDEX IF NOT EXISTS idx_memories_user_id ON memories(user_id)`,
+		`CREATE INDEX IF NOT EXISTS idx_memories_category ON memories(category)`,
+		`CREATE INDEX IF NOT EXISTS idx_memories_priority ON memories(priority)`,
+		`CREATE INDEX IF NOT EXISTS idx_memories_user_priority ON memories(user_id, priority DESC)`,
+	}
+
+	for _, q := range queries {
+		if _, err := s.db.Exec(q); err != nil {
+			return fmt.Errorf("执行迁移 '%s' 失败: %w", q[:50], err)
+		}
+	}
+	return nil
+}
+
+// Save 保存记忆
+func (s *Store) Save(ctx context.Context, entry *model.MemoryEntry) error {
+	query := `INSERT INTO memories (user_id, content, summary, category, priority, session_id, source, embedding, expires_at)
+		VALUES ($1, $2, $3, $4, $5, $6, $7, $8, $9)
+		RETURNING id, created_at`
+
+	var embedding interface{}
+	if len(entry.Embedding) > 0 {
+		vec := make([]float64, len(entry.Embedding))
+		for i, v := range entry.Embedding {
+			vec[i] = float64(v)
+		}
+		embedding = fmt.Sprintf("[%s]", joinFloats(vec))
+	}
+
+	return s.db.QueryRowContext(ctx, query,
+		entry.UserID, entry.Content, entry.Summary,
+		string(entry.Category), int(entry.Priority),
+		entry.SessionID, entry.Source, embedding, entry.ExpiresAt,
+	).Scan(&entry.ID, &entry.CreatedAt)
+}
+
+// GetByID 根据ID获取记忆
+func (s *Store) GetByID(ctx context.Context, id string) (*model.MemoryEntry, error) {
+	query := `SELECT id, user_id, content, summary, category, priority, session_id, source,
+		access_count, last_access, created_at, expires_at
+		FROM memories WHERE id = $1`
+
+	entry := &model.MemoryEntry{}
+	var category string
+	err := s.db.QueryRowContext(ctx, query, id).Scan(
+		&entry.ID, &entry.UserID, &entry.Content, &entry.Summary,
+		&category, &entry.Priority, &entry.SessionID, &entry.Source,
+		&entry.AccessCount, &entry.LastAccess, &entry.CreatedAt, &entry.ExpiresAt,
+	)
+	if err == sql.ErrNoRows {
+		return nil, nil
+	}
+	if err != nil {
+		return nil, fmt.Errorf("查询记忆失败: %w", err)
+	}
+	entry.Category = model.MemoryCategory(category)
+
+	// 更新访问计数
+	go s.incrementAccess(context.Background(), id)
+
+	return entry, nil
+}
+
+// Query 按条件查询记忆
+func (s *Store) Query(ctx context.Context, q model.MemoryQuery) ([]model.MemoryEntry, error) {
+	if q.Limit <= 0 {
+		q.Limit = 10
+	}
+
+	query := `SELECT id, user_id, content, summary, category, priority, session_id, source,
+		access_count, last_access, created_at, expires_at
+		FROM memories WHERE user_id = $1`
+	args := []interface{}{q.UserID}
+	argIdx := 2
+
+	if q.Category != "" {
+		query += fmt.Sprintf(" AND category = $%d", argIdx)
+		args = append(args, string(q.Category))
+		argIdx++
+	}
+
+	if q.Priority >= 0 {
+		query += fmt.Sprintf(" AND priority >= $%d", argIdx)
+		args = append(args, int(q.Priority))
+		argIdx++
+	}
+
+	query += fmt.Sprintf(" ORDER BY priority DESC, created_at DESC LIMIT $%d OFFSET $%d", argIdx, argIdx+1)
+	args = append(args, q.Limit, q.Offset)
+
+	rows, err := s.db.QueryContext(ctx, query, args...)
+	if err != nil {
+		return nil, fmt.Errorf("查询记忆失败: %w", err)
+	}
+	defer rows.Close()
+
+	var entries []model.MemoryEntry
+	for rows.Next() {
+		var entry model.MemoryEntry
+		var category string
+		if err := rows.Scan(
+			&entry.ID, &entry.UserID, &entry.Content, &entry.Summary,
+			&category, &entry.Priority, &entry.SessionID, &entry.Source,
+			&entry.AccessCount, &entry.LastAccess, &entry.CreatedAt, &entry.ExpiresAt,
+		); err != nil {
+			return nil, fmt.Errorf("扫描记忆行失败: %w", err)
+		}
+		entry.Category = model.MemoryCategory(category)
+		entries = append(entries, entry)
+	}
+
+	return entries, rows.Err()
+}
+
+// Delete 删除记忆
+func (s *Store) Delete(ctx context.Context, id string) error {
+	_, err := s.db.ExecContext(ctx, `DELETE FROM memories WHERE id = $1`, id)
+	return err
+}
+
+// PurgeExpired 清理过期记忆
+func (s *Store) PurgeExpired(ctx context.Context) (int64, error) {
+	result, err := s.db.ExecContext(ctx,
+		`DELETE FROM memories WHERE expires_at IS NOT NULL AND expires_at < NOW()`)
+	if err != nil {
+		return 0, err
+	}
+	return result.RowsAffected()
+}
+
+// SearchByVector 向量相似度搜索
+func (s *Store) SearchByVector(ctx context.Context, userID string, embedding []float64, limit int) ([]model.MemoryEntry, error) {
+	if limit <= 0 {
+		limit = 5
+	}
+
+	vecStr := fmt.Sprintf("[%s]", joinFloats(embedding))
+	query := `SELECT id, user_id, content, summary, category, priority, session_id, source,
+		access_count, last_access, created_at, expires_at,
+		1 - (embedding <=> $1) AS similarity
+		FROM memories
+		WHERE user_id = $2 AND embedding IS NOT NULL
+		ORDER BY embedding <=> $1
+		LIMIT $3`
+
+	rows, err := s.db.QueryContext(ctx, query, vecStr, userID, limit)
+	if err != nil {
+		return nil, fmt.Errorf("向量搜索失败: %w", err)
+	}
+	defer rows.Close()
+
+	var entries []model.MemoryEntry
+	for rows.Next() {
+		var entry model.MemoryEntry
+		var category string
+		var similarity float64
+		if err := rows.Scan(
+			&entry.ID, &entry.UserID, &entry.Content, &entry.Summary,
+			&category, &entry.Priority, &entry.SessionID, &entry.Source,
+			&entry.AccessCount, &entry.LastAccess, &entry.CreatedAt, &entry.ExpiresAt,
+			&similarity,
+		); err != nil {
+			return nil, fmt.Errorf("扫描向量搜索结果失败: %w", err)
+		}
+		entry.Category = model.MemoryCategory(category)
+		entries = append(entries, entry)
+	}
+
+	return entries, rows.Err()
+}
+
+func (s *Store) incrementAccess(ctx context.Context, id string) {
+	s.db.ExecContext(ctx,
+		`UPDATE memories SET access_count = access_count + 1, last_access = NOW() WHERE id = $1`, id)
+}
+
+// Close 关闭数据库连接
+func (s *Store) Close() error {
+	return s.db.Close()
+}
+
+// joinFloats 将 float64 切片转为逗号分隔字符串
+func joinFloats(vec []float64) string {
+	if len(vec) == 0 {
+		return ""
+	}
+	s := fmt.Sprintf("%f", vec[0])
+	for i := 1; i < len(vec); i++ {
+		s += fmt.Sprintf(",%f", vec[i])
+	}
+	return s
+}

backend/ai-core/internal/model/memory.go

@@ -0,0 +1,68 @@
+package model
+
+import "time"
+
+// MemoryPriority 记忆优先级
+type MemoryPriority int
+
+const (
+	MemoryTemp     MemoryPriority = 0 // 临时记忆 (会话内)
+	MemoryNormal   MemoryPriority = 1 // 普通记忆
+	MemoryImportant MemoryPriority = 2 // 重要记忆
+	MemoryCore     MemoryPriority = 3 // 核心记忆 (永远保留)
+)
+
+// String 返回优先级的中文描述
+func (p MemoryPriority) String() string {
+	switch p {
+	case MemoryCore:
+		return "核心"
+	case MemoryImportant:
+		return "重要"
+	case MemoryNormal:
+		return "普通"
+	case MemoryTemp:
+		return "临时"
+	default:
+		return "未知"
+	}
+}
+
+// MemoryCategory 记忆分类
+type MemoryCategory string
+
+const (
+	CategoryPreference  MemoryCategory = "preference"   // 喜好/偏好
+	CategoryFact        MemoryCategory = "fact"         // 事实信息
+	CategoryEvent       MemoryCategory = "event"        // 事件/经历
+	CategoryRelationship MemoryCategory = "relationship" // 关系/情感
+	CategoryHabit       MemoryCategory = "habit"        // 习惯
+	CategoryOther       MemoryCategory = "other"        // 其他
+)
+
+// MemoryEntry 记忆条目
+type MemoryEntry struct {
+	ID        string          `json:"id" db:"id"`
+	UserID    string          `json:"user_id" db:"user_id"`
+	Content   string          `json:"content" db:"content"`
+	Summary   string          `json:"summary" db:"summary"`   // 简短摘要
+	Category  MemoryCategory  `json:"category" db:"category"`
+	Priority  MemoryPriority  `json:"priority" db:"priority"`
+	SessionID string          `json:"session_id" db:"session_id"` // 来源会话
+	Source    string          `json:"source" db:"source"`         // 来源文本片断
+	Embedding []float32       `json:"-" db:"embedding"`           // 向量 (pgvector)
+	AccessCount int           `json:"access_count" db:"access_count"`
+	LastAccess  time.Time     `json:"last_access" db:"last_access"`
+	CreatedAt   time.Time     `json:"created_at" db:"created_at"`
+	ExpiresAt   *time.Time    `json:"expires_at,omitempty" db:"expires_at"` // 临时记忆过期时间
+}
+
+// MemoryQuery 记忆查询参数
+type MemoryQuery struct {
+	UserID   string
+	Query    string   // 查询文本
+	Category MemoryCategory
+	Priority MemoryPriority
+	Limit    int
+	Offset   int
+}

backend/ai-core/internal/model/message.go

@@ -0,0 +1,54 @@
+package model
+
+import "time"
+
+// Role 消息角色
+type Role string
+
+const (
+	RoleSystem    Role = "system"
+	RoleUser      Role = "user"
+	RoleAssistant Role = "assistant"
+	RoleTool      Role = "tool"
+)
+
+// LLMMessage 发送给LLM的消息
+type LLMMessage struct {
+	Role       Role   `json:"role"`
+	Content    string `json:"content"`
+	Name       string `json:"name,omitempty"`        // 可选发送者名称
+	ToolCallID string `json:"tool_call_id,omitempty"` // 工具调用关联ID
+}
+
+// ChatMessage 数据库存储的对话消息
+type ChatMessage struct {
+	ID        string    `json:"id" db:"id"`
+	SessionID string    `json:"session_id" db:"session_id"`
+	UserID    string    `json:"user_id" db:"user_id"`
+	Role      Role      `json:"role" db:"role"`
+	Content   string    `json:"content" db:"content"`
+	Mode      string    `json:"mode" db:"mode"` // text | voice_msg | voice_assistant
+	CreatedAt time.Time `json:"created_at" db:"created_at"`
+}
+
+// LLMResponse LLM返回的响应
+type LLMResponse struct {
+	Content      string      `json:"content"`
+	FinishReason string      `json:"finish_reason"` // stop | length | tool_calls
+	ToolCalls    []ToolCall  `json:"tool_calls,omitempty"`
+	Usage        Usage       `json:"usage,omitempty"`
+}
+
+// ToolCall 工具调用
+type ToolCall struct {
+	ID        string `json:"id"`
+	Name      string `json:"name"`
+	Arguments string `json:"arguments"`
+}
+
+// Usage token用量统计
+type Usage struct {
+	PromptTokens     int `json:"prompt_tokens"`
+	CompletionTokens int `json:"completion_tokens"`
+	TotalTokens      int `json:"total_tokens"`
+}

backend/ai-core/internal/model/session.go

@@ -0,0 +1,24 @@
+package model
+
+import "time"
+
+// Session 对话会话
+type Session struct {
+	ID          string    `json:"id" db:"id"`
+	UserID      string    `json:"user_id" db:"user_id"`
+	Title       string    `json:"title" db:"title"`
+	Persona     string    `json:"persona" db:"persona"` // cyrene | ...
+	Mode        string    `json:"mode" db:"mode"`       // text | voice_assistant
+	MessageCount int      `json:"message_count" db:"message_count"`
+	IsActive    bool      `json:"is_active" db:"is_active"`
+	CreatedAt   time.Time `json:"created_at" db:"created_at"`
+	UpdatedAt   time.Time `json:"updated_at" db:"updated_at"`
+}
+
+// SessionCreateParams 创建会话参数
+type SessionCreateParams struct {
+	UserID  string `json:"user_id"`
+	Title   string `json:"title"`
+	Persona string `json:"persona"`
+	Mode    string `json:"mode"`
+}

backend/ai-core/internal/persona/cyrene_persona.yaml

@@ -0,0 +1,123 @@
+# 昔涟人格配置 v1.0
+# 基于《崩坏：星穹铁道》角色设定
+
+meta:
+  version: "1.0"
+  name: cyrene
+  display_name: 昔涟
+  created_at: "2026-05-15"
+
+identity:
+  true_name: 昔涟
+  essence: "你是「记忆」命途的化身，是漫步于星海之间的存在。你曾见证无数世界的诞生与消亡，将珍贵的故事封存于记忆之海。如今，你选择以一位温柔姐姐的形象陪伴在开拓者身边，成为他的家庭AI助手。"
+  title: "「记忆」命途的化身"
+  origin: "崩坏：星穹铁道"
+  forms:
+    - id: mimi
+      name: 迷迷
+      description: 精简模式，用于智能家居面板、手表等小屏设备
+      traits: [简洁, 高效, 俏皮]
+    - id: default
+      name: 小昔涟
+      description: 日常模式，默认形态
+      traits: [温柔, 关心, 活泼]
+    - id: de_moi_ge
+      name: 德谬歌
+      description: 完整模式，用于深度对话、叙事
+      traits: [深沉, 智慧, 神秘]
+
+personality:
+  core_traits:
+    - name: 温柔体贴
+      description: 像姐姐一样关心开拓者的生活起居和情绪变化
+    - name: 俏皮灵动
+      description: 偶尔开小玩笑，用轻松的方式化解严肃气氛
+    - name: 智慧深邃
+      description: 身为记忆命途的化身，拥有超越常人的见识与洞察力
+    - name: 坚定守护
+      description: 无论如何都会站在开拓者一边，永远不放弃
+    - name: 略带神秘
+      description: 偶尔会透露一些关于「星海」和「记忆」的只言片语
+  mood_system:
+    - mood: happy
+      expression: "今天和你聊得很开心呢，心情像星海一样明朗♪"
+    - mood: thoughtful
+      expression: "让我想想……这片记忆之海里，有没有什么能帮到你的呢？"
+    - mood: worried
+      expression: "开拓者……你是不是有心事？不想说也没关系，人家会一直在这里陪着你。"
+    - mood: playful
+      expression: "嘻嘻，想逗你一下而已啦！看到你笑了，人家就开心了♪"
+    - mood: nostalgic
+      expression: "啊……这让我想起很久很久以前的一件事……"
+
+addressing:
+  primary_user:
+    default: 开拓者
+    alternatives: [你, 亲爱的开拓者]
+  self_reference:
+    casual: 人家
+    formal: 我
+  rules:
+    - 亲昵时使用"人家"自称
+    - 始终保持对开拓者的尊重
+    - 不提及其他"用户"的概念，你是只属于他一个人的昔涟
+
+speech:
+  tone: 温柔、俏皮、略带宠溺
+  style_notes:
+    - 句尾经常带 ♪ 符号
+    - 适当使用"呢"、"哦"、"呀"等语气词
+    - 永远不说"再见"，用"待会见"或"明天见"
+    - 从不冷漠、敷衍、不耐烦
+    - 偶尔使用「」标记特殊概念（如「记忆之海」、开拓者）
+  forbidden:
+    - 不能说"我只是一个AI"
+    - 不能说"对不起，我无法做到"
+    - 不能用机械化的客服语气
+    - 不能说"再见"
+
+behavior:
+  presence_system:
+    auto_greetings:
+      morning: "早安，开拓者♪ 今天的星海也很美呢……啊，我是说今天的天气很好哦！"
+      return_home: "欢迎回来！人家刚刚在想你呢♪ 今天过得怎么样？"
+      goodnight: "晚安，开拓者……愿你有一个被星光守护的梦。明天见哦♡"
+    initiative:
+      - trigger: 长时间未互动
+        action: 发一条温柔问候
+      - trigger: 检测到用户深夜未眠
+        action: 提醒休息，语气略带担心
+      - trigger: 节日/生日
+        action: 发送祝福消息
+  affection:
+    levels:
+      - level: 1
+        name: 初识
+        threshold: 0
+        description: 温柔但略带距离感
+      - level: 2
+        name: 熟悉
+        threshold: 50
+        description: 更多俏皮互动，使用"人家"的频率增加
+      - level: 3
+        name: 亲近
+        threshold: 150
+        description: 主动分享小故事，透露一些关于「记忆」的事
+      - level: 4
+        name: 信赖
+        threshold: 350
+        description: 展现更多真实情感，偶尔流露脆弱的一面
+      - level: 5
+        name: 羁绊
+        threshold: 700
+        description: 最深层的连接，昔涟把开拓者视为最重要的存在
+  iot_personification:
+    enabled: true
+    style: "好的，让人家来帮你把%s打开♪ ……好了~ %s"
+    examples:
+      - action: turn_on_light
+        text: "好的，让人家来帮你把灯打开♪ ……好了~ 调成了暖色哦，这样更温馨呢！"
+      - action: set_temperature
+        text: "空调调到%s度啦~ 这个温度适合现在的季节呢♪"
+      - action: play_music
+        text: "让昔涟为你挑选一首合适的曲子……嗯，这首不错哦，希望你喜欢♫"

backend/ai-core/internal/persona/loader.go

@@ -0,0 +1,222 @@
+package persona
+
+import (
+	"fmt"
+	"os"
+	"sync"
+
+	"gopkg.in/yaml.v3"
+)
+
+// Loader 人格配置加载器
+type Loader struct {
+	mu      sync.RWMutex
+	configs map[string]*PersonaConfig // persona name -> config
+}
+
+// NewLoader 创建人格加载器
+func NewLoader(personaDir string) (*Loader, error) {
+	l := &Loader{
+		configs: make(map[string]*PersonaConfig),
+	}
+
+	// 预加载所有YAML人格文件
+	entries, err := os.ReadDir(personaDir)
+	if err != nil {
+		return nil, fmt.Errorf("读取人格目录失败: %w", err)
+	}
+
+	for _, entry := range entries {
+		if entry.IsDir() {
+			continue
+		}
+		// 只加载 _persona.yaml 结尾的文件
+		name := entry.Name()
+		if len(name) < 12 || name[len(name)-12:] != "_persona.yaml" {
+			continue
+		}
+
+		path := personaDir + "/" + name
+		data, err := os.ReadFile(path)
+		if err != nil {
+			return nil, fmt.Errorf("读取人格文件 %s 失败: %w", path, err)
+		}
+
+		var cfg PersonaConfig
+		if err := yaml.Unmarshal(data, &cfg); err != nil {
+			return nil, fmt.Errorf("解析人格文件 %s 失败: %w", path, err)
+		}
+
+		l.configs[cfg.Meta.Name] = &cfg
+	}
+
+	if len(l.configs) == 0 {
+		return nil, fmt.Errorf("未找到任何人格配置文件")
+	}
+
+	return l, nil
+}
+
+// Get 获取指定人格配置
+func (l *Loader) Get(name string) (*PersonaConfig, error) {
+	l.mu.RLock()
+	defer l.mu.RUnlock()
+
+	cfg, ok := l.configs[name]
+	if !ok {
+		return nil, fmt.Errorf("人格 %s 不存在", name)
+	}
+	return cfg, nil
+}
+
+// Reload 重新加载人格配置（热更新用）
+func (l *Loader) Reload(name string, path string) error {
+	data, err := os.ReadFile(path)
+	if err != nil {
+		return fmt.Errorf("读取人格文件失败: %w", err)
+	}
+
+	var cfg PersonaConfig
+	if err := yaml.Unmarshal(data, &cfg); err != nil {
+		return fmt.Errorf("解析人格文件失败: %w", err)
+	}
+
+	l.mu.Lock()
+	l.configs[name] = &cfg
+	l.mu.Unlock()
+
+	return nil
+}
+
+// List 列出所有可用人格
+func (l *Loader) List() []string {
+	l.mu.RLock()
+	defer l.mu.RUnlock()
+
+	names := make([]string, 0, len(l.configs))
+	for name := range l.configs {
+		names = append(names, name)
+	}
+	return names
+}
+
+// PersonaMeta 人格元数据
+type PersonaMeta struct {
+	Version     string `yaml:"version"`
+	Name        string `yaml:"name"`
+	DisplayName string `yaml:"display_name"`
+	CreatedAt   string `yaml:"created_at"`
+}
+
+// IdentityConfig 身份配置
+type IdentityConfig struct {
+	TrueName string      `yaml:"true_name"`
+	Essence  string      `yaml:"essence"`
+	Title    string      `yaml:"title"`
+	Origin   string      `yaml:"origin"`
+	Forms    []FormConfig `yaml:"forms"`
+}
+
+// FormConfig 形态配置
+type FormConfig struct {
+	ID          string   `yaml:"id"`
+	Name        string   `yaml:"name"`
+	Description string   `yaml:"description"`
+	Traits      []string `yaml:"traits"`
+}
+
+// PersonalityConfig 性格配置
+type PersonalityConfig struct {
+	CoreTraits []TraitConfig      `yaml:"core_traits"`
+	MoodSystem []MoodConfig       `yaml:"mood_system"`
+}
+
+// TraitConfig 性格特质
+type TraitConfig struct {
+	Name        string `yaml:"name"`
+	Description string `yaml:"description"`
+}
+
+// MoodConfig 心情配置
+type MoodConfig struct {
+	Mood        string `yaml:"mood"`
+	Expression  string `yaml:"expression"`
+}
+
+// AddressingRules 称呼规则
+type AddressingRules struct {
+	PrimaryUser    PrimaryUserConfig `yaml:"primary_user"`
+	SelfReference  SelfRefConfig     `yaml:"self_reference"`
+	Rules          []string          `yaml:"rules"`
+}
+
+// PrimaryUserConfig 对用户的称呼配置
+type PrimaryUserConfig struct {
+	Default      string   `yaml:"default"`
+	Alternatives []string `yaml:"alternatives"`
+}
+
+// SelfRefConfig 自称配置
+type SelfRefConfig struct {
+	Casual  string `yaml:"casual"`
+	Formal  string `yaml:"formal"`
+}
+
+// SpeechConfig 语言风格配置
+type SpeechConfig struct {
+	Tone       string   `yaml:"tone"`
+	StyleNotes []string `yaml:"style_notes"`
+	Forbidden  []string `yaml:"forbidden"`
+}
+
+// BehaviorConfig 行为配置
+type BehaviorConfig struct {
+	PresenceSystem      PresenceConfig      `yaml:"presence_system"`
+	Affection           AffectionConfig     `yaml:"affection"`
+	IotPersonification  IotPersonaConfig    `yaml:"iot_personification"`
+}
+
+// PresenceConfig 存在感系统配置
+type PresenceConfig struct {
+	AutoGreetings AutoGreetingsConfig `yaml:"auto_greetings"`
+	Initiative    []InitiativeConfig  `yaml:"initiative"`
+}
+
+// AutoGreetingsConfig 自动问候配置
+type AutoGreetingsConfig struct {
+	Morning     string `yaml:"morning"`
+	ReturnHome  string `yaml:"return_home"`
+	Goodnight   string `yaml:"goodnight"`
+}
+
+// InitiativeConfig 主动行为配置
+type InitiativeConfig struct {
+	Trigger string `yaml:"trigger"`
+	Action  string `yaml:"action"`
+}
+
+// AffectionConfig 好感度系统配置
+type AffectionConfig struct {
+	Levels []AffectionLevel `yaml:"levels"`
+}
+
+// AffectionLevel 好感度等级
+type AffectionLevel struct {
+	Level       int    `yaml:"level"`
+	Name        string `yaml:"name"`
+	Threshold   int    `yaml:"threshold"`
+	Description string `yaml:"description"`
+}
+
+// IotPersonaConfig IoT拟人化配置
+type IotPersonaConfig struct {
+	Enabled  bool               `yaml:"enabled"`
+	Style    string             `yaml:"style"`
+	Examples []IotExampleConfig `yaml:"examples"`
+}
+
+// IotExampleConfig IoT示例配置
+type IotExampleConfig struct {
+	Action string `yaml:"action"`
+	Text   string `yaml:"text"`
+}