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https://github.com/Ed1s0nZ/CyberStrikeAI.git
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公明
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package knowledge
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import (
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"context"
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"fmt"
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"net/http"
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"strings"
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"sync"
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"time"
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"cyberstrike-ai/internal/config"
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einoembedopenai "github.com/cloudwego/eino-ext/components/embedding/openai"
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"github.com/cloudwego/eino/components/embedding"
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"go.uber.org/zap"
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"golang.org/x/time/rate"
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)
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// Embedder 使用 CloudWeGo Eino 的 OpenAI Embedding 组件，并保留速率限制与重试。
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type Embedder struct {
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eino embedding.Embedder
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config *config.KnowledgeConfig
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logger *zap.Logger
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rateLimiter *rate.Limiter
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rateLimitDelay time.Duration
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maxRetries int
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retryDelay time.Duration
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mu sync.Mutex
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}
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// NewEmbedder 基于 Eino eino-ext OpenAI Embedder；openAIConfig 用于在知识库未单独配置 key 时回退 API Key。
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func NewEmbedder(ctx context.Context, cfg *config.KnowledgeConfig, openAIConfig *config.OpenAIConfig, logger *zap.Logger) (*Embedder, error) {
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if cfg == nil {
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return nil, fmt.Errorf("knowledge config is nil")
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}
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var rateLimiter *rate.Limiter
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var rateLimitDelay time.Duration
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if cfg.Indexing.MaxRPM > 0 {
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rpm := cfg.Indexing.MaxRPM
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rateLimiter = rate.NewLimiter(rate.Every(time.Minute/time.Duration(rpm)), rpm)
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if logger != nil {
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logger.Info("知识库索引速率限制已启用", zap.Int("maxRPM", rpm))
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}
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} else if cfg.Indexing.RateLimitDelayMs > 0 {
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rateLimitDelay = time.Duration(cfg.Indexing.RateLimitDelayMs) * time.Millisecond
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if logger != nil {
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logger.Info("知识库索引固定延迟已启用", zap.Duration("delay", rateLimitDelay))
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}
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}
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maxRetries := 3
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retryDelay := 1000 * time.Millisecond
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if cfg.Indexing.MaxRetries > 0 {
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maxRetries = cfg.Indexing.MaxRetries
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}
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if cfg.Indexing.RetryDelayMs > 0 {
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retryDelay = time.Duration(cfg.Indexing.RetryDelayMs) * time.Millisecond
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}
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model := strings.TrimSpace(cfg.Embedding.Model)
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if model == "" {
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model = "text-embedding-3-small"
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}
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baseURL := strings.TrimSpace(cfg.Embedding.BaseURL)
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baseURL = strings.TrimSuffix(baseURL, "/")
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if baseURL == "" {
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baseURL = "https://api.openai.com/v1"
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}
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apiKey := strings.TrimSpace(cfg.Embedding.APIKey)
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if apiKey == "" && openAIConfig != nil {
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apiKey = strings.TrimSpace(openAIConfig.APIKey)
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}
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if apiKey == "" {
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return nil, fmt.Errorf("embedding API key 未配置")
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}
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timeout := 120 * time.Second
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if cfg.Indexing.RequestTimeoutSeconds > 0 {
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timeout = time.Duration(cfg.Indexing.RequestTimeoutSeconds) * time.Second
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}
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httpClient := &http.Client{Timeout: timeout}
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inner, err := einoembedopenai.NewEmbedder(ctx, &einoembedopenai.EmbeddingConfig{
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APIKey: apiKey,
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BaseURL: baseURL,
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ByAzure: false,
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Model: model,
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HTTPClient: httpClient,
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})
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if err != nil {
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return nil, fmt.Errorf("eino OpenAI embedder: %w", err)
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}
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return &Embedder{
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eino: inner,
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config: cfg,
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logger: logger,
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rateLimiter: rateLimiter,
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rateLimitDelay: rateLimitDelay,
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maxRetries: maxRetries,
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retryDelay: retryDelay,
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}, nil
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}
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// EmbeddingModelName 返回配置的嵌入模型名（用于 tiktoken 分块与向量行元数据）。
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func (e *Embedder) EmbeddingModelName() string {
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if e == nil || e.config == nil {
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return ""
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}
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s := strings.TrimSpace(e.config.Embedding.Model)
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if s != "" {
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return s
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}
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return "text-embedding-3-small"
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}
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func (e *Embedder) waitRateLimiter() {
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e.mu.Lock()
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defer e.mu.Unlock()
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if e.rateLimiter != nil {
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ctx := context.Background()
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if err := e.rateLimiter.Wait(ctx); err != nil && e.logger != nil {
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e.logger.Warn("速率限制器等待失败", zap.Error(err))
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}
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}
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if e.rateLimitDelay > 0 {
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time.Sleep(e.rateLimitDelay)
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}
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}
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// EmbedText 单条嵌入（float32，与历史存储格式一致）。
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func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error) {
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vecs, err := e.EmbedStrings(ctx, []string{text})
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if err != nil {
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return nil, err
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}
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if len(vecs) != 1 {
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return nil, fmt.Errorf("unexpected embedding count: %d", len(vecs))
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}
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return vecs[0], nil
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}
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// EmbedStrings 批量嵌入，带重试；实现 [embedding.Embedder]，可供 Eino Indexer 使用。
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func (e *Embedder) EmbedStrings(ctx context.Context, texts []string, opts ...embedding.Option) ([][]float32, error) {
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if e == nil || e.eino == nil {
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return nil, fmt.Errorf("embedder not initialized")
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}
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if len(texts) == 0 {
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return nil, nil
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}
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var lastErr error
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for attempt := 0; attempt < e.maxRetries; attempt++ {
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if attempt > 0 {
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wait := e.retryDelay * time.Duration(attempt)
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if e.logger != nil {
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e.logger.Debug("嵌入重试前等待", zap.Int("attempt", attempt+1), zap.Duration("wait", wait))
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}
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select {
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case <-ctx.Done():
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return nil, ctx.Err()
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case <-time.After(wait):
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}
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} else {
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e.waitRateLimiter()
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}
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raw, err := e.eino.EmbedStrings(ctx, texts, opts...)
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if err == nil {
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out := make([][]float32, len(raw))
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for i, row := range raw {
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out[i] = make([]float32, len(row))
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for j, v := range row {
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out[i][j] = float32(v)
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}
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}
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return out, nil
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}
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lastErr = err
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if !e.isRetryableError(err) {
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return nil, err
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}
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if e.logger != nil {
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e.logger.Debug("嵌入失败，将重试", zap.Int("attempt", attempt+1), zap.Error(err))
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}
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}
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return nil, fmt.Errorf("达到最大重试次数 (%d): %v", e.maxRetries, lastErr)
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}
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// EmbedTexts 批量 float32 嵌入（兼容旧调用；单次请求批量以减小延迟）。
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func (e *Embedder) EmbedTexts(ctx context.Context, texts []string) ([][]float32, error) {
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return e.EmbedStrings(ctx, texts)
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}
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func (e *Embedder) isRetryableError(err error) bool {
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if err == nil {
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return false
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}
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errStr := err.Error()
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if strings.Contains(errStr, "429") || strings.Contains(errStr, "rate limit") {
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return true
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}
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if strings.Contains(errStr, "500") || strings.Contains(errStr, "502") ||
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strings.Contains(errStr, "503") || strings.Contains(errStr, "504") {
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return true
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}
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if strings.Contains(errStr, "timeout") || strings.Contains(errStr, "connection") ||
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strings.Contains(errStr, "network") || strings.Contains(errStr, "EOF") {
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return true
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}
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return false
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}
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// einoFloatEmbedder adapts [][]float32 embedder to Eino's [][]float64 [embedding.Embedder] for Indexer.Store.
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type einoFloatEmbedder struct {
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inner *Embedder
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}
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func (w *einoFloatEmbedder) EmbedStrings(ctx context.Context, texts []string, opts ...embedding.Option) ([][]float64, error) {
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vec32, err := w.inner.EmbedStrings(ctx, texts, opts...)
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if err != nil {
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return nil, err
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}
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out := make([][]float64, len(vec32))
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for i, row := range vec32 {
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out[i] = make([]float64, len(row))
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for j, v := range row {
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out[i][j] = float64(v)
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}
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}
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return out, nil
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}
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func (w *einoFloatEmbedder) GetType() string {
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return "CyberStrikeKnowledgeEmbedder"
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}
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func (w *einoFloatEmbedder) IsCallbacksEnabled() bool {
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return false
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}
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// EinoEmbeddingComponent returns an [embedding.Embedder] that uses the same retry/rate-limit path
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// and produces float64 vectors expected by generic Eino indexer helpers.
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func (e *Embedder) EinoEmbeddingComponent() embedding.Embedder {
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return &einoFloatEmbedder{inner: e}
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}
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