Files
CyberStrikeAI/internal/knowledge/embedder.go
T
Sycun cfaa95d7bd refactor(runtime): remove Eino and make Pi the sole agent engine
Delete the Eino model adapters, callbacks, middleware, orchestration,
summarization and single-agent runner together with their dependencies.
Chat, batch tasks, workflow Agent nodes and sub-agents now share the Pi
runtime, and dispatch is a single code path instead of an engine switch.

Configuration migrates eino_middleware/eino_skills to runtime/skills and
drops the retired callback settings while old files still load. Settings,
i18n and docs describe Pi only. Also carry the Pi follow-up fixes: empty
required lists are omitted from tool schemas, historical tool results stay
with their call batch, model proxies work over HTTP/1.1, single-query
retrieval skips query expansion, and finalization docs match Pi lifecycle
events.
2026-11-12 17:55:36 +08:00

247 lines
6.7 KiB
Go
Raw Blame History

This file contains ambiguous Unicode characters
This file contains Unicode characters that might be confused with other characters. If you think that this is intentional, you can safely ignore this warning. Use the Escape button to reveal them.
package knowledge
import (
"context"
"fmt"
"net/http"
"strings"
"sync"
"time"
"cyberstrike-ai/internal/config"
"go.uber.org/zap"
"golang.org/x/time/rate"
)
// Embedder uses native OpenAI-compatible HTTP with rate limits and bounded retries.
type Embedder struct {
provider TextEmbedder
config *config.KnowledgeConfig
logger *zap.Logger
rateLimiter *rate.Limiter
rateLimitDelay time.Duration
maxRetries int
retryDelay time.Duration
mu sync.Mutex
}
// NewEmbedder 基于 native OpenAI-compatible Embedding HTTP;openAIConfig 用于在知识库未单独配置 key 时回退 API Key。
func NewEmbedder(ctx context.Context, cfg *config.KnowledgeConfig, openAIConfig *config.OpenAIConfig, logger *zap.Logger) (*Embedder, error) {
if cfg == nil {
return nil, fmt.Errorf("knowledge config is nil")
}
var rateLimiter *rate.Limiter
var rateLimitDelay time.Duration
if cfg.Indexing.MaxRPM > 0 {
rpm := cfg.Indexing.MaxRPM
rateLimiter = rate.NewLimiter(rate.Every(time.Minute/time.Duration(rpm)), rpm)
if logger != nil {
logger.Info("知识库索引速率限制已启用", zap.Int("maxRPM", rpm))
}
} else if cfg.Indexing.RateLimitDelayMs > 0 {
rateLimitDelay = time.Duration(cfg.Indexing.RateLimitDelayMs) * time.Millisecond
if logger != nil {
logger.Info("知识库索引固定延迟已启用", zap.Duration("delay", rateLimitDelay))
}
}
maxRetries := 3
retryDelay := 1000 * time.Millisecond
if cfg.Indexing.MaxRetries > 0 {
maxRetries = cfg.Indexing.MaxRetries
}
if cfg.Indexing.RetryDelayMs > 0 {
retryDelay = time.Duration(cfg.Indexing.RetryDelayMs) * time.Millisecond
}
model := strings.TrimSpace(cfg.Embedding.Model)
if model == "" {
model = "text-embedding-3-small"
}
baseURL := strings.TrimSpace(cfg.Embedding.BaseURL)
baseURL = strings.TrimSuffix(baseURL, "/")
if baseURL == "" {
baseURL = "https://api.openai.com/v1"
}
apiKey := strings.TrimSpace(cfg.Embedding.APIKey)
if apiKey == "" && openAIConfig != nil {
apiKey = strings.TrimSpace(openAIConfig.APIKey)
}
if apiKey == "" {
return nil, fmt.Errorf("embedding API key 未配置")
}
timeout := 120 * time.Second
if cfg.Indexing.RequestTimeoutSeconds > 0 {
timeout = time.Duration(cfg.Indexing.RequestTimeoutSeconds) * time.Second
}
httpClient := &http.Client{Timeout: timeout}
inner := &httpTextEmbedder{client: httpClient, endpoint: baseURL + "/embeddings", key: apiKey, model: model}
return &Embedder{
provider: inner,
config: cfg,
logger: logger,
rateLimiter: rateLimiter,
rateLimitDelay: rateLimitDelay,
maxRetries: maxRetries,
retryDelay: retryDelay,
}, nil
}
// EmbeddingModelName 返回配置的嵌入模型名(用于 tiktoken 分块与向量行元数据)。
func (e *Embedder) EmbeddingModelName() string {
if e == nil || e.config == nil {
return ""
}
s := strings.TrimSpace(e.config.Embedding.Model)
if s != "" {
return s
}
return "text-embedding-3-small"
}
func (e *Embedder) waitRateLimiter(ctx context.Context) error {
e.mu.Lock()
defer e.mu.Unlock()
if e.rateLimiter != nil {
if err := e.rateLimiter.Wait(ctx); err != nil {
return err
}
}
if e.rateLimitDelay > 0 {
select {
case <-ctx.Done():
return ctx.Err()
case <-time.After(e.rateLimitDelay):
}
}
return ctx.Err()
}
// EmbedText 单条嵌入(float32,与历史存储格式一致)。
func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error) {
vecs, err := e.EmbedStrings(ctx, []string{text})
if err != nil {
return nil, err
}
if len(vecs) != 1 {
return nil, fmt.Errorf("unexpected embedding count: %d", len(vecs))
}
return vecs[0], nil
}
// EmbedStrings 批量嵌入,带重试;实现 [TextEmbedder],可供 Agent Indexer 使用。
func (e *Embedder) EmbedStrings(ctx context.Context, texts []string, opts ...EmbeddingOption) ([][]float32, error) {
if e == nil || e.provider == nil {
return nil, fmt.Errorf("embedder not initialized")
}
if len(texts) == 0 {
return nil, nil
}
var lastErr error
for attempt := 0; attempt < e.maxRetries; attempt++ {
if attempt > 0 {
wait := e.retryDelay * time.Duration(attempt)
if e.logger != nil {
e.logger.Debug("嵌入重试前等待", zap.Int("attempt", attempt+1), zap.Duration("wait", wait))
}
select {
case <-ctx.Done():
return nil, ctx.Err()
case <-time.After(wait):
}
} else {
if err := e.waitRateLimiter(ctx); err != nil {
return nil, err
}
}
raw, err := e.provider.EmbedStrings(ctx, texts, opts...)
if err == nil {
out := make([][]float32, len(raw))
for i, row := range raw {
out[i] = make([]float32, len(row))
for j, v := range row {
out[i][j] = float32(v)
}
}
return out, nil
}
lastErr = err
if !e.isRetryableError(err) {
return nil, err
}
if e.logger != nil {
e.logger.Debug("嵌入失败,将重试", zap.Int("attempt", attempt+1), zap.Error(err))
}
}
return nil, fmt.Errorf("达到最大重试次数 (%d): %v", e.maxRetries, lastErr)
}
// EmbedTexts 批量 float32 嵌入(兼容旧调用;单次请求批量以减小延迟)。
func (e *Embedder) EmbedTexts(ctx context.Context, texts []string) ([][]float32, error) {
return e.EmbedStrings(ctx, texts)
}
func (e *Embedder) isRetryableError(err error) bool {
if err == nil {
return false
}
errStr := err.Error()
if strings.Contains(errStr, "429") || strings.Contains(errStr, "rate limit") {
return true
}
if strings.Contains(errStr, "500") || strings.Contains(errStr, "502") ||
strings.Contains(errStr, "503") || strings.Contains(errStr, "504") {
return true
}
if strings.Contains(errStr, "timeout") || strings.Contains(errStr, "connection") ||
strings.Contains(errStr, "network") || strings.Contains(errStr, "EOF") {
return true
}
return false
}
// floatEmbedder adapts [][]float32 embedder to native [][]float64 [TextEmbedder] for Indexer.Store.
type floatEmbedder struct {
inner *Embedder
}
func (w *floatEmbedder) EmbedStrings(ctx context.Context, texts []string, opts ...EmbeddingOption) ([][]float64, error) {
vec32, err := w.inner.EmbedStrings(ctx, texts, opts...)
if err != nil {
return nil, err
}
out := make([][]float64, len(vec32))
for i, row := range vec32 {
out[i] = make([]float64, len(row))
for j, v := range row {
out[i][j] = float64(v)
}
}
return out, nil
}
func (w *floatEmbedder) GetType() string {
return "CyberStrikeKnowledgeEmbedder"
}
func (w *floatEmbedder) IsCallbacksEnabled() bool {
return false
}
// EmbeddingComponent returns an [TextEmbedder] that uses the same retry/rate-limit path
// and produces float64 vectors expected by native indexer helpers.
func (e *Embedder) EmbeddingComponent() TextEmbedder {
return &floatEmbedder{inner: e}
}