fix: 修复记忆力差和跨群聊上下文泄漏

记忆嵌入修复:
- 新增 memory.APIEmbedder 使用 text-embedding-3-small 替代 SimpleEmbedder
- Extractor 保存记忆时自动生成向量嵌入
- Embedder 接口增加 IsAvailable() 方法

跨群聊上下文隔离:
- Thinker 新增 thinkSessionID 字段，performThink 启动时绑定会话
- storeThought 优先使用绑定的 session 推送思考结果
- 防止思考过程中其他群消息改变 activeSessionID 导致串台

Co-Authored-By: Claude <noreply@anthropic.com>

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

@@ -0,0 +1,104 @@
+package memory
+
+import (
+	"bytes"
+	"context"
+	"encoding/json"
+	"fmt"
+	"io"
+	"net/http"
+	"time"
+)
+
+// APIEmbedder generates text embeddings via OpenAI-compatible API.
+type APIEmbedder struct {
+	baseURL    string
+	apiKey     string
+	model      string
+	httpClient *http.Client
+}
+
+// NewAPIEmbedder creates a new embedding service.
+func NewAPIEmbedder(baseURL, apiKey, model string) *APIEmbedder {
+	return &APIEmbedder{
+		baseURL: baseURL,
+		apiKey:  apiKey,
+		model:   model,
+		httpClient: &http.Client{
+			Timeout: 30 * time.Second,
+		},
+	}
+}
+
+type embRequest struct {
+	Input []string `json:"input"`
+	Model string   `json:"model"`
+}
+
+type embResponse struct {
+	Data  []embData  `json:"data"`
+	Error *embError  `json:"error,omitempty"`
+}
+
+type embData struct {
+	Embedding []float64 `json:"embedding"`
+}
+
+type embError struct {
+	Message string `json:"message"`
+}
+
+// Embed generates an embedding vector for the given text.
+func (e *APIEmbedder) Embed(ctx context.Context, text string) ([]float64, error) {
+	if !e.IsAvailable() {
+		return nil, fmt.Errorf("embedding service not available")
+	}
+
+	reqBody := embRequest{
+		Input: []string{text},
+		Model: e.model,
+	}
+
+	jsonBody, err := json.Marshal(reqBody)
+	if err != nil {
+		return nil, fmt.Errorf("marshal embedding request: %w", err)
+	}
+
+	req, err := http.NewRequestWithContext(ctx, "POST", e.baseURL+"/embeddings", bytes.NewReader(jsonBody))
+	if err != nil {
+		return nil, fmt.Errorf("create embedding request: %w", err)
+	}
+	req.Header.Set("Content-Type", "application/json")
+	req.Header.Set("Authorization", "Bearer "+e.apiKey)
+
+	resp, err := e.httpClient.Do(req)
+	if err != nil {
+		return nil, fmt.Errorf("embedding request failed: %w", err)
+	}
+	defer resp.Body.Close()
+
+	body, err := io.ReadAll(resp.Body)
+	if err != nil {
+		return nil, fmt.Errorf("read embedding response: %w", err)
+	}
+
+	var embResp embResponse
+	if err := json.Unmarshal(body, &embResp); err != nil {
+		return nil, fmt.Errorf("parse embedding response: %w", err)
+	}
+
+	if embResp.Error != nil {
+		return nil, fmt.Errorf("embedding API error: %s", embResp.Error.Message)
+	}
+
+	if len(embResp.Data) == 0 {
+		return nil, fmt.Errorf("no embedding returned")
+	}
+
+	return embResp.Data[0].Embedding, nil
+}
+
+// IsAvailable checks if the embedding service is configured.
+func (e *APIEmbedder) IsAvailable() bool {
+	return e.apiKey != "" && e.baseURL != ""
+}

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

@@ -6,14 +6,16 @@ import (
 	"fmt"
 	"git.yeij.top/AskaEth/Cyrene/pkg/logger"
 	"strings"
+	"time"
 
 	"git.yeij.top/AskaEth/Cyrene/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)
+	store    *Store
+	llmChat  func(ctx context.Context, messages []model.LLMMessage) (*model.LLMResponse, error)
+	embedder Embedder // 可选：为保存的记忆生成向量嵌入
 }
 
 // NewExtractor 创建记忆提取器
@@ -26,6 +28,11 @@ func NewExtractor(store *Store, llmChat func(ctx context.Context, messages []mod
 	}
 }
 
+// SetEmbedder sets the embedder for generating vector embeddings on saved memories.
+func (e *Extractor) SetEmbedder(embedder Embedder) {
+	e.embedder = embedder
+}
+
 // 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) {
@@ -54,6 +61,21 @@ func (e *Extractor) storeMemories(ctx context.Context, userID, sessionID string,
 		mem.SessionID = sessionID
 		mem.Source = "conversation"
 
+		// 生成向量嵌入（异步，不阻塞主流程）
+		if e.embedder != nil && e.embedder.IsAvailable() {
+			embedCtx, cancel := context.WithTimeout(context.Background(), 15*time.Second)
+			embedding, embErr := e.embedder.Embed(embedCtx, mem.Content)
+			cancel()
+			if embErr != nil {
+				logger.Printf("[memory] 嵌入生成失败: %v，将保存无嵌入的记忆", embErr)
+			} else {
+				mem.Embedding = make([]float32, len(embedding))
+				for i, v := range embedding {
+					mem.Embedding[i] = float32(v)
+				}
+			}
+		}
+
 		existing, err := e.findSimilar(ctx, userID, &mem)
 		if err == nil && existing != nil {
 			e.mergeMemory(ctx, existing, &mem)

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

@@ -20,6 +20,7 @@ type Retriever struct {
 // Embedder 文本嵌入接口
 type Embedder interface {
 	Embed(ctx context.Context, text string) ([]float64, error)
+	IsAvailable() bool
 }
 
 // SimpleEmbedder 基于关键词的简单嵌入（MVP阶段可用，无需外部API）
@@ -43,6 +44,9 @@ func (e *SimpleEmbedder) Embed(ctx context.Context, text string) ([]float64, err
 	return vec, nil
 }
 
+// IsAvailable returns true (SimpleEmbedder is always available as fallback).
+func (e *SimpleEmbedder) IsAvailable() bool { return true }
+
 // NewRetriever 创建记忆检索器
 func NewRetriever(store *Store, embedder Embedder) *Retriever {
 	if embedder == nil {