Add files via upload

This commit is contained in:
公明
2026-03-06 22:39:30 +08:00
committed by GitHub
parent 81f7a601b7
commit 7493e70686
11 changed files with 4991 additions and 136 deletions
+2 -2
View File
@@ -198,7 +198,7 @@ func New(cfg *config.Config, log *logger.Logger) (*App, error) {
knowledgeRetriever = knowledge.NewRetriever(knowledgeDB, embedder, retrievalConfig, log.Logger)
// 创建索引器
knowledgeIndexer = knowledge.NewIndexer(knowledgeDB, embedder, log.Logger)
knowledgeIndexer = knowledge.NewIndexer(knowledgeDB, embedder, log.Logger, &cfg.Knowledge.Indexing)
// 注册知识检索工具到MCP服务器
knowledge.RegisterKnowledgeTool(mcpServer, knowledgeRetriever, knowledgeManager, log.Logger)
@@ -1102,7 +1102,7 @@ func initializeKnowledge(
knowledgeRetriever := knowledge.NewRetriever(knowledgeDB, embedder, retrievalConfig, logger)
// 创建索引器
knowledgeIndexer := knowledge.NewIndexer(knowledgeDB, embedder, logger)
knowledgeIndexer := knowledge.NewIndexer(knowledgeDB, embedder, logger, &cfg.Knowledge.Indexing)
// 注册知识检索工具到MCP服务器
knowledge.RegisterKnowledgeTool(mcpServer, knowledgeRetriever, knowledgeManager, logger)
+20
View File
@@ -595,6 +595,26 @@ type KnowledgeConfig struct {
BasePath string `yaml:"base_path" json:"base_path"` // 知识库路径
Embedding EmbeddingConfig `yaml:"embedding" json:"embedding"`
Retrieval RetrievalConfig `yaml:"retrieval" json:"retrieval"`
Indexing IndexingConfig `yaml:"indexing,omitempty" json:"indexing,omitempty"` // 索引构建配置
}
// IndexingConfig 索引构建配置(用于控制知识库索引构建时的行为)
type IndexingConfig struct {
// 分块配置
ChunkSize int `yaml:"chunk_size,omitempty" json:"chunk_size,omitempty"` // 每个块的最大 token 数(估算),默认 512
ChunkOverlap int `yaml:"chunk_overlap,omitempty" json:"chunk_overlap,omitempty"` // 块之间的重叠 token 数,默认 50
MaxChunksPerItem int `yaml:"max_chunks_per_item,omitempty" json:"max_chunks_per_item,omitempty"` // 单个知识项的最大块数量,0 表示不限制
// 速率限制配置(用于避免 API 速率限制)
RateLimitDelayMs int `yaml:"rate_limit_delay_ms,omitempty" json:"rate_limit_delay_ms,omitempty"` // 请求间隔时间(毫秒),0 表示不使用固定延迟
MaxRPM int `yaml:"max_rpm,omitempty" json:"max_rpm,omitempty"` // 每分钟最大请求数,0 表示不限制
// 重试配置(用于处理临时错误)
MaxRetries int `yaml:"max_retries,omitempty" json:"max_retries,omitempty"` // 最大重试次数,默认 3
RetryDelayMs int `yaml:"retry_delay_ms,omitempty" json:"retry_delay_ms,omitempty"` // 重试间隔(毫秒),默认 1000
// 批处理配置(用于批量嵌入,当前未使用,保留扩展)
BatchSize int `yaml:"batch_size,omitempty" json:"batch_size,omitempty"` // 批量处理大小,0 表示逐个处理
}
// EmbeddingConfig 嵌入配置
+10
View File
@@ -1062,6 +1062,16 @@ func updateKnowledgeConfig(doc *yaml.Node, cfg config.KnowledgeConfig) {
setIntInMap(retrievalNode, "top_k", cfg.Retrieval.TopK)
setFloatInMap(retrievalNode, "similarity_threshold", cfg.Retrieval.SimilarityThreshold)
setFloatInMap(retrievalNode, "hybrid_weight", cfg.Retrieval.HybridWeight)
// 更新索引配置
indexingNode := ensureMap(knowledgeNode, "indexing")
setIntInMap(indexingNode, "chunk_size", cfg.Indexing.ChunkSize)
setIntInMap(indexingNode, "chunk_overlap", cfg.Indexing.ChunkOverlap)
setIntInMap(indexingNode, "max_chunks_per_item", cfg.Indexing.MaxChunksPerItem)
setIntInMap(indexingNode, "max_rpm", cfg.Indexing.MaxRPM)
setIntInMap(indexingNode, "rate_limit_delay_ms", cfg.Indexing.RateLimitDelayMs)
setIntInMap(indexingNode, "max_retries", cfg.Indexing.MaxRetries)
setIntInMap(indexingNode, "retry_delay_ms", cfg.Indexing.RetryDelayMs)
}
func updateRobotsConfig(doc *yaml.Node, cfg config.RobotsConfig) {
+32 -10
View File
@@ -75,7 +75,7 @@ func (h *KnowledgeHandler) GetItems(c *gin.Context) {
groupedByCategory[cat] = append(groupedByCategory[cat], item)
}
// 转换为CategoryWithItems格式
// 转换为 CategoryWithItems 格式
categoriesWithItems := make([]*knowledge.CategoryWithItems, 0, len(groupedByCategory))
for cat, catItems := range groupedByCategory {
categoriesWithItems = append(categoriesWithItems, &knowledge.CategoryWithItems{
@@ -107,7 +107,7 @@ func (h *KnowledgeHandler) GetItems(c *gin.Context) {
categoryPageMode := c.Query("categoryPage") != "false" // 默认使用分类分页
// 分页参数
limit := 50 // 默认每页50条(分类分页时为分类数,项分页时为项数)
limit := 50 // 默认每页 50 条(分类分页时为分类数,项分页时为项数)
offset := 0
if limitStr := c.Query("limit"); limitStr != "" {
if parsed, err := parseInt(limitStr); err == nil && parsed > 0 && parsed <= 500 {
@@ -120,7 +120,7 @@ func (h *KnowledgeHandler) GetItems(c *gin.Context) {
}
}
// 如果指定了category参数,且使用分类分页模式,则只返回该分类
// 如果指定了 category 参数,且使用分类分页模式,则只返回该分类
if category != "" && categoryPageMode {
// 单分类模式:返回该分类的所有知识项(不分页)
items, total, err := h.manager.GetItemsSummary(category, 0, 0)
@@ -150,9 +150,9 @@ func (h *KnowledgeHandler) GetItems(c *gin.Context) {
if categoryPageMode {
// 按分类分页模式(默认)
// limit表示每页分类数,推荐5-10个分类
// limit 表示每页分类数,推荐 5-10 个分类
if limit <= 0 || limit > 100 {
limit = 10 // 默认每页10个分类
limit = 10 // 默认每页 10 个分类
}
categoriesWithItems, totalCategories, err := h.manager.GetCategoriesWithItems(limit, offset)
@@ -172,7 +172,7 @@ func (h *KnowledgeHandler) GetItems(c *gin.Context) {
}
// 按项分页模式(向后兼容)
// 是否包含完整内容(默认false,只返回摘要)
// 是否包含完整内容(默认 false,只返回摘要)
includeContent := c.Query("includeContent") == "true"
if includeContent {
@@ -358,7 +358,7 @@ func (h *KnowledgeHandler) ScanKnowledgeBase(c *gin.Context) {
)
}
// 如果连续失败2次,立即停止增量索引
// 如果连续失败 2 次,立即停止增量索引
if consecutiveFailures >= 2 {
h.logger.Error("连续索引失败次数过多,立即停止增量索引",
zap.Int("consecutiveFailures", consecutiveFailures),
@@ -397,7 +397,7 @@ func (h *KnowledgeHandler) ScanKnowledgeBase(c *gin.Context) {
func (h *KnowledgeHandler) GetRetrievalLogs(c *gin.Context) {
conversationID := c.Query("conversationId")
messageID := c.Query("messageId")
limit := 50 // 默认50条
limit := 50 // 默认 50
if limitStr := c.Query("limit"); limitStr != "" {
if parsed, err := parseInt(limitStr); err == nil && parsed > 0 {
@@ -441,18 +441,40 @@ func (h *KnowledgeHandler) GetIndexStatus(c *gin.Context) {
if h.indexer != nil {
lastError, lastErrorTime := h.indexer.GetLastError()
if lastError != "" {
// 如果错误是最近发生的(5分钟内),则返回错误信息
// 如果错误是最近发生的(5 分钟内),则返回错误信息
if time.Since(lastErrorTime) < 5*time.Minute {
status["last_error"] = lastError
status["last_error_time"] = lastErrorTime.Format(time.RFC3339)
}
}
// 获取重建索引状态
isRebuilding, totalItems, current, failed, lastItemID, lastChunks, startTime := h.indexer.GetRebuildStatus()
if isRebuilding {
status["is_rebuilding"] = true
status["rebuild_total"] = totalItems
status["rebuild_current"] = current
status["rebuild_failed"] = failed
status["rebuild_start_time"] = startTime.Format(time.RFC3339)
if lastItemID != "" {
status["rebuild_last_item_id"] = lastItemID
}
if lastChunks > 0 {
status["rebuild_last_chunks"] = lastChunks
}
// 重建中时,is_complete 为 false
status["is_complete"] = false
// 计算重建进度百分比
if totalItems > 0 {
status["progress_percent"] = float64(current) / float64(totalItems) * 100
}
}
}
c.JSON(http.StatusOK, status)
}
// Search 搜索知识库(用于API调用,Agent内部使用Retriever
// Search 搜索知识库(用于 API 调用,Agent 内部使用 Retriever
func (h *KnowledgeHandler) Search(c *gin.Context) {
var req knowledge.SearchRequest
if err := c.ShouldBindJSON(&req); err != nil {
+149 -31
View File
@@ -6,39 +6,75 @@ import (
"fmt"
"net/http"
"strings"
"sync"
"time"
"cyberstrike-ai/internal/config"
"cyberstrike-ai/internal/openai"
"go.uber.org/zap"
"golang.org/x/time/rate"
)
// Embedder 文本嵌入器
type Embedder struct {
openAIClient *openai.Client
config *config.KnowledgeConfig
openAIConfig *config.OpenAIConfig // 用于获取API Key
logger *zap.Logger
openAIClient *openai.Client
config *config.KnowledgeConfig
openAIConfig *config.OpenAIConfig // 用于获取 API Key
logger *zap.Logger
rateLimiter *rate.Limiter // 速率限制器
rateLimitDelay time.Duration // 请求间隔时间
maxRetries int // 最大重试次数
retryDelay time.Duration // 重试间隔
mu sync.Mutex // 保护 rateLimiter
}
// NewEmbedder 创建新的嵌入器
func NewEmbedder(cfg *config.KnowledgeConfig, openAIConfig *config.OpenAIConfig, openAIClient *openai.Client, logger *zap.Logger) *Embedder {
// 初始化速率限制器
var rateLimiter *rate.Limiter
var rateLimitDelay time.Duration
// 如果配置了 MaxRPM,根据 RPM 计算速率限制
if cfg.Indexing.MaxRPM > 0 {
rpm := cfg.Indexing.MaxRPM
rateLimiter = rate.NewLimiter(rate.Every(time.Minute/time.Duration(rpm)), rpm)
logger.Info("知识库索引速率限制已启用", zap.Int("maxRPM", rpm))
} else if cfg.Indexing.RateLimitDelayMs > 0 {
// 如果没有配置 MaxRPM 但配置了固定延迟,使用固定延迟模式
rateLimitDelay = time.Duration(cfg.Indexing.RateLimitDelayMs) * time.Millisecond
logger.Info("知识库索引固定延迟已启用", zap.Duration("delay", rateLimitDelay))
}
// 重试配置
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
}
return &Embedder{
openAIClient: openAIClient,
config: cfg,
openAIConfig: openAIConfig,
logger: logger,
openAIClient: openAIClient,
config: cfg,
openAIConfig: openAIConfig,
logger: logger,
rateLimiter: rateLimiter,
rateLimitDelay: rateLimitDelay,
maxRetries: maxRetries,
retryDelay: retryDelay,
}
}
// EmbeddingRequest OpenAI嵌入请求
// EmbeddingRequest OpenAI 嵌入请求
type EmbeddingRequest struct {
Model string `json:"model"`
Input []string `json:"input"`
}
// EmbeddingResponse OpenAI嵌入响应
// EmbeddingResponse OpenAI 嵌入响应
type EmbeddingResponse struct {
Data []EmbeddingData `json:"data"`
Error *EmbeddingError `json:"error,omitempty"`
@@ -56,12 +92,69 @@ type EmbeddingError struct {
Type string `json:"type"`
}
// EmbedText 对文本进行嵌入
func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error) {
if e.openAIClient == nil {
return nil, fmt.Errorf("OpenAI客户端未初始化")
// waitRateLimiter 等待速率限制器
func (e *Embedder) waitRateLimiter() {
e.mu.Lock()
defer e.mu.Unlock()
if e.rateLimiter != nil {
// 等待令牌
ctx := context.Background()
if err := e.rateLimiter.Wait(ctx); err != nil {
e.logger.Warn("速率限制器等待失败", zap.Error(err))
}
}
if e.rateLimitDelay > 0 {
time.Sleep(e.rateLimitDelay)
}
}
// EmbedText 对文本进行嵌入(带重试和速率限制)
func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error) {
if e.openAIClient == nil {
return nil, fmt.Errorf("OpenAI 客户端未初始化")
}
var lastErr error
for attempt := 0; attempt < e.maxRetries; attempt++ {
// 速率限制
if attempt > 0 {
// 重试时等待更长时间
waitTime := e.retryDelay * time.Duration(attempt)
e.logger.Debug("重试前等待", zap.Int("attempt", attempt+1), zap.Duration("waitTime", waitTime))
select {
case <-ctx.Done():
return nil, ctx.Err()
case <-time.After(waitTime):
}
} else {
e.waitRateLimiter()
}
result, err := e.doEmbedText(ctx, text)
if err == nil {
return result, nil
}
lastErr = err
// 检查是否是可重试的错误(429 速率限制、5xx 服务器错误、网络错误)
if !e.isRetryableError(err) {
return nil, err
}
e.logger.Debug("嵌入请求失败,准备重试",
zap.Int("attempt", attempt+1),
zap.Int("maxRetries", e.maxRetries),
zap.Error(err))
}
return nil, fmt.Errorf("达到最大重试次数 (%d): %v", e.maxRetries, lastErr)
}
// doEmbedText 执行实际的嵌入请求(内部方法)
func (e *Embedder) doEmbedText(ctx context.Context, text string) ([]float32, error) {
// 使用配置的嵌入模型
model := e.config.Embedding.Model
if model == "" {
@@ -73,7 +166,7 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
Input: []string{text},
}
// 清理baseURL:去除前后空格和尾部斜杠
// 清理 baseURL:去除前后空格和尾部斜杠
baseURL := strings.TrimSpace(e.config.Embedding.BaseURL)
baseURL = strings.TrimSuffix(baseURL, "/")
if baseURL == "" {
@@ -83,24 +176,24 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
// 构建请求
body, err := json.Marshal(req)
if err != nil {
return nil, fmt.Errorf("序列化请求失败: %w", err)
return nil, fmt.Errorf("序列化请求失败%w", err)
}
requestURL := baseURL + "/embeddings"
httpReq, err := http.NewRequestWithContext(ctx, http.MethodPost, requestURL, strings.NewReader(string(body)))
if err != nil {
return nil, fmt.Errorf("创建请求失败: %w", err)
return nil, fmt.Errorf("创建请求失败%w", err)
}
httpReq.Header.Set("Content-Type", "application/json")
// 使用配置的API Key,如果没有则使用OpenAI配置的
// 使用配置的 API Key,如果没有则使用 OpenAI 配置的
apiKey := strings.TrimSpace(e.config.Embedding.APIKey)
if apiKey == "" && e.openAIConfig != nil {
apiKey = e.openAIConfig.APIKey
}
if apiKey == "" {
return nil, fmt.Errorf("API Key未配置")
return nil, fmt.Errorf("API Key 未配置")
}
httpReq.Header.Set("Authorization", "Bearer "+apiKey)
@@ -110,7 +203,7 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
}
resp, err := httpClient.Do(httpReq)
if err != nil {
return nil, fmt.Errorf("发送请求失败: %w", err)
return nil, fmt.Errorf("发送请求失败%w", err)
}
defer resp.Body.Close()
@@ -132,7 +225,7 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
if len(requestBodyPreview) > 200 {
requestBodyPreview = requestBodyPreview[:200] + "..."
}
e.logger.Debug("嵌入API请求",
e.logger.Debug("嵌入 API 请求",
zap.String("url", httpReq.URL.String()),
zap.String("model", model),
zap.String("requestBody", requestBodyPreview),
@@ -148,12 +241,12 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
if len(bodyPreview) > 500 {
bodyPreview = bodyPreview[:500] + "..."
}
return nil, fmt.Errorf("解析响应失败 (URL: %s, 状态码: %d, 响应长度: %d字节): %w\n请求体: %s\n响应内容预览: %s",
return nil, fmt.Errorf("解析响应失败 (URL: %s, 状态码%d, 响应长度%d字节): %w\n请求体%s\n响应内容预览%s",
requestURL, resp.StatusCode, len(bodyBytes), err, requestBodyPreview, bodyPreview)
}
if embeddingResp.Error != nil {
return nil, fmt.Errorf("OpenAI API错误 (状态码: %d): 类型=%s, 消息=%s",
return nil, fmt.Errorf("OpenAI API 错误 (状态码%d): 类型=%s, 消息=%s",
resp.StatusCode, embeddingResp.Error.Type, embeddingResp.Error.Message)
}
@@ -162,7 +255,7 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
if len(bodyPreview) > 500 {
bodyPreview = bodyPreview[:500] + "..."
}
return nil, fmt.Errorf("HTTP请求失败 (URL: %s, 状态码: %d): 响应内容=%s", requestURL, resp.StatusCode, bodyPreview)
return nil, fmt.Errorf("HTTP 请求失败 (URL: %s, 状态码%d): 响应内容=%s", requestURL, resp.StatusCode, bodyPreview)
}
if len(embeddingResp.Data) == 0 {
@@ -170,11 +263,11 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
if len(bodyPreview) > 500 {
bodyPreview = bodyPreview[:500] + "..."
}
return nil, fmt.Errorf("未收到嵌入数据 (状态码: %d, 响应长度: %d字节)\n响应内容: %s",
return nil, fmt.Errorf("未收到嵌入数据 (状态码%d, 响应长度%d字节)\n响应内容%s",
resp.StatusCode, len(bodyBytes), bodyPreview)
}
// 转换为float32
// 转换为 float32
embedding := make([]float32, len(embeddingResp.Data[0].Embedding))
for i, v := range embeddingResp.Data[0].Embedding {
embedding[i] = float32(v)
@@ -183,23 +276,48 @@ func (e *Embedder) EmbedText(ctx context.Context, text string) ([]float32, error
return embedding, nil
}
// isRetryableError 判断是否是可重试的错误
func (e *Embedder) isRetryableError(err error) bool {
if err == nil {
return false
}
errStr := err.Error()
// 429 速率限制错误
if strings.Contains(errStr, "429") || strings.Contains(errStr, "rate limit") {
return true
}
// 5xx 服务器错误
if strings.Contains(errStr, "500") || strings.Contains(errStr, "502") ||
strings.Contains(errStr, "503") || strings.Contains(errStr, "504") {
return true
}
// 网络错误
if strings.Contains(errStr, "timeout") || strings.Contains(errStr, "connection") ||
strings.Contains(errStr, "network") || strings.Contains(errStr, "EOF") {
return true
}
return false
}
// EmbedTexts 批量嵌入文本
func (e *Embedder) EmbedTexts(ctx context.Context, texts []string) ([][]float32, error) {
if len(texts) == 0 {
return nil, nil
}
// OpenAI API支持批量,但为了简单起见,我们逐个处理
// 实际可以使用批量API以提高效率
embeddings := make([][]float32, len(texts))
for i, text := range texts {
embedding, err := e.EmbedText(ctx, text)
if err != nil {
return nil, fmt.Errorf("嵌入文本[%d]失败: %w", i, err)
return nil, fmt.Errorf("嵌入文本 [%d] 失败%w", i, err)
}
embeddings[i] = embedding
}
return embeddings, nil
}
+256 -92
View File
@@ -10,56 +10,104 @@ import (
"sync"
"time"
"cyberstrike-ai/internal/config"
"github.com/google/uuid"
"go.uber.org/zap"
)
// Indexer 索引器,负责将知识项分块并向量化
type Indexer struct {
db *sql.DB
embedder *Embedder
logger *zap.Logger
chunkSize int // 每个块的最大token数(估算)
overlap int // 块之间的重叠token数
db *sql.DB
embedder *Embedder
logger *zap.Logger
chunkSize int // 每个块的最大 token 数(估算)
overlap int // 块之间的重叠 token
maxChunks int // 单个知识项的最大块数量(0 表示不限制)
// 错误跟踪
mu sync.RWMutex
lastError string // 最近一次错误信息
mu sync.RWMutex
lastError string // 最近一次错误信息
lastErrorTime time.Time // 最近一次错误时间
errorCount int // 连续错误计数
errorCount int // 连续错误计数
// 重建索引状态跟踪
rebuildMu sync.RWMutex
isRebuilding bool // 是否正在重建索引
rebuildTotalItems int // 重建总项数
rebuildCurrent int // 当前已处理项数
rebuildFailed int // 重建失败项数
rebuildStartTime time.Time // 重建开始时间
rebuildLastItemID string // 最近处理的项 ID
rebuildLastChunks int // 最近处理的项的分块数
}
// NewIndexer 创建新的索引器
func NewIndexer(db *sql.DB, embedder *Embedder, logger *zap.Logger) *Indexer {
func NewIndexer(db *sql.DB, embedder *Embedder, logger *zap.Logger, indexingCfg *config.IndexingConfig) *Indexer {
chunkSize := 512
overlap := 50
maxChunks := 0
if indexingCfg != nil {
if indexingCfg.ChunkSize > 0 {
chunkSize = indexingCfg.ChunkSize
}
if indexingCfg.ChunkOverlap >= 0 {
overlap = indexingCfg.ChunkOverlap
}
if indexingCfg.MaxChunksPerItem > 0 {
maxChunks = indexingCfg.MaxChunksPerItem
}
}
return &Indexer{
db: db,
embedder: embedder,
logger: logger,
chunkSize: 512, // 默认512 tokens
overlap: 50, // 默认50 tokens重叠
chunkSize: chunkSize,
overlap: overlap,
maxChunks: maxChunks,
}
}
// ChunkText 将文本分块(支持重叠)
// ChunkText 将文本分块(支持重叠,保留标题上下文
func (idx *Indexer) ChunkText(text string) []string {
// 按Markdown标题分割
chunks := idx.splitByMarkdownHeaders(text)
// 按 Markdown 标题分割,获取带标题的块
sections := idx.splitByMarkdownHeadersWithContent(text)
// 如果块太大,进一步分割
// 处理每个块
result := make([]string, 0)
for _, chunk := range chunks {
if idx.estimateTokens(chunk) <= idx.chunkSize {
result = append(result, chunk)
for _, section := range sections {
// 构建完整的标题路径(如 "SQL 注入 > 检测方法 > 工具扫描")
headerPath := strings.Join(section.HeaderPath, " > ")
// 如果块太大,进一步分割
if idx.estimateTokens(section.Content) <= idx.chunkSize {
// 块大小合适,直接添加标题前缀
if headerPath != "" {
result = append(result, fmt.Sprintf("[%s] %s", headerPath, section.Content))
} else {
result = append(result, section.Content)
}
} else {
// 按段落分割
subChunks := idx.splitByParagraphs(chunk)
for _, subChunk := range subChunks {
if idx.estimateTokens(subChunk) <= idx.chunkSize {
result = append(result, subChunk)
// 块太大,按段落分割
paragraphs := idx.splitByParagraphs(section.Content)
for _, para := range paragraphs {
if idx.estimateTokens(para) <= idx.chunkSize {
// 段落大小合适,添加标题前缀
if headerPath != "" {
result = append(result, fmt.Sprintf("[%s] %s", headerPath, para))
} else {
result = append(result, para)
}
} else {
// 按句子分割(支持重叠
chunksWithOverlap := idx.splitBySentencesWithOverlap(subChunk)
result = append(result, chunksWithOverlap...)
// 段落仍太大,按句子分割(带重叠和标题前缀
sentenceChunks := idx.splitBySentencesWithOverlap(para)
for _, chunk := range sentenceChunks {
if headerPath != "" {
result = append(result, fmt.Sprintf("[%s] %s", headerPath, chunk))
} else {
result = append(result, chunk)
}
}
}
}
}
@@ -68,43 +116,104 @@ func (idx *Indexer) ChunkText(text string) []string {
return result
}
// splitByMarkdownHeaders 按Markdown标题分割
func (idx *Indexer) splitByMarkdownHeaders(text string) []string {
// 匹配Markdown标题 (# ## ### 等)
// Section 表示一个带标题路径的文本块
type Section struct {
HeaderPath []string // 标题路径(如 ["# SQL 注入", "## 检测方法"]
Content string // 块内容
}
// splitByMarkdownHeadersWithContent 按 Markdown 标题分割,返回带标题路径的块
func (idx *Indexer) splitByMarkdownHeadersWithContent(text string) []Section {
// 匹配 Markdown 标题 (# ## ### 等)
headerRegex := regexp.MustCompile(`(?m)^#{1,6}\s+.+$`)
// 找到所有标题位置
matches := headerRegex.FindAllStringIndex(text, -1)
if len(matches) == 0 {
return []string{text}
// 没有标题,返回整个文本
return []Section{{HeaderPath: []string{}, Content: text}}
}
chunks := make([]string, 0)
sections := make([]Section, 0)
currentHeaderPath := []string{}
lastPos := 0
for _, match := range matches {
for i, match := range matches {
start := match[0]
if start > lastPos {
chunks = append(chunks, strings.TrimSpace(text[lastPos:start]))
end := match[1]
// 提取当前标题
headerLine := strings.TrimSpace(text[start:end])
// 计算标题层级(# 的数量)
level := 0
for _, ch := range headerLine {
if ch == '#' {
level++
} else {
break
}
}
lastPos = start
// 更新标题路径
// 移除比当前层级深或等于的子标题
newPath := make([]string, 0)
for _, h := range currentHeaderPath {
hLevel := 0
for _, ch := range h {
if ch == '#' {
hLevel++
} else {
break
}
}
if hLevel < level {
newPath = append(newPath, h)
}
}
newPath = append(newPath, headerLine)
currentHeaderPath = newPath
// 提取上一个标题到当前标题之间的内容
if start > lastPos {
content := strings.TrimSpace(text[lastPos:start])
// 使用上一层的标题路径
prevPath := make([]string, len(currentHeaderPath))
copy(prevPath, currentHeaderPath)
if i > 0 {
// 去掉当前标题,使用父级路径
if len(prevPath) > 0 {
prevPath = prevPath[:len(prevPath)-1]
}
}
sections = append(sections, Section{
HeaderPath: prevPath,
Content: content,
})
}
lastPos = end
}
// 添加最后一部分
// 添加最后一部分(最后一个标题之后的内容)
if lastPos < len(text) {
chunks = append(chunks, strings.TrimSpace(text[lastPos:]))
content := strings.TrimSpace(text[lastPos:])
sections = append(sections, Section{
HeaderPath: currentHeaderPath,
Content: content,
})
}
// 过滤空块
result := make([]string, 0)
for _, chunk := range chunks {
if strings.TrimSpace(chunk) != "" {
result = append(result, chunk)
result := make([]Section, 0)
for _, section := range sections {
if strings.TrimSpace(section.Content) != "" {
result = append(result, section)
}
}
if len(result) == 0 {
return []string{text}
return []Section{{HeaderPath: []string{}, Content: text}}
}
return result
@@ -124,8 +233,12 @@ func (idx *Indexer) splitByParagraphs(text string) []string {
// splitBySentences 按句子分割(用于内部,不包含重叠逻辑)
func (idx *Indexer) splitBySentences(text string) []string {
// 简单的句子分割(按句号、问号、感叹号)
sentenceRegex := regexp.MustCompile(`[.!?]+\s+`)
// 简单的句子分割(按句号、问号、感叹号,支持中英文
// . ! ? = 英文标点
// \u3002 = 。(中文句号)
// \uFF01 = (中文叹号)
// \uFF1F = (中文问号)
sentenceRegex := regexp.MustCompile(`[.!?\x{3002}\x{FF01}\x{FF1F}]+`)
sentences := sentenceRegex.Split(text, -1)
result := make([]string, 0)
for _, s := range sentences {
@@ -221,13 +334,13 @@ func (idx *Indexer) splitBySentencesSimple(text string) []string {
return result
}
// extractLastTokens 从文本末尾提取指定token数量的内容
// extractLastTokens 从文本末尾提取指定 token 数量的内容
func (idx *Indexer) extractLastTokens(text string, tokenCount int) string {
if tokenCount <= 0 || text == "" {
return ""
}
// 估算字符数(1 token ≈ 4字符)
// 估算字符数(1 token ≈ 4 字符)
charCount := tokenCount * 4
runes := []rune(text)
@@ -236,12 +349,11 @@ func (idx *Indexer) extractLastTokens(text string, tokenCount int) string {
}
// 从末尾提取指定数量的字符
// 尝试在句子边界处截断,避免截断句子中间
startPos := len(runes) - charCount
extracted := string(runes[startPos:])
// 尝试找到第一个句子边界(句号、问号、感叹号后的空格
sentenceBoundary := regexp.MustCompile(`[.!?]+\s+`)
// 尝试找到第一个句子边界(支持中英文标点
sentenceBoundary := regexp.MustCompile(`[.!?\x{3002}\x{FF01}\x{FF1F}]+`)
matches := sentenceBoundary.FindStringIndex(extracted)
if len(matches) > 0 && matches[0] > 0 {
// 在句子边界处截断,保留完整句子
@@ -251,41 +363,51 @@ func (idx *Indexer) extractLastTokens(text string, tokenCount int) string {
return strings.TrimSpace(extracted)
}
// estimateTokens 估算token数(简单估算:1 token ≈ 4字符)
// estimateTokens 估算 token 数(简单估算:1 token ≈ 4 字符)
func (idx *Indexer) estimateTokens(text string) int {
return len([]rune(text)) / 4
}
// IndexItem 索引知识项(分块并向量化)
func (idx *Indexer) IndexItem(ctx context.Context, itemID string) error {
// 获取知识项(包含categorytitle,用于向量化)
// 获取知识项(包含 categorytitle,用于向量化)
var content, category, title string
err := idx.db.QueryRow("SELECT content, category, title FROM knowledge_base_items WHERE id = ?", itemID).Scan(&content, &category, &title)
if err != nil {
return fmt.Errorf("获取知识项失败: %w", err)
return fmt.Errorf("获取知识项失败%w", err)
}
// 删除旧的向量(在 RebuildIndex 中已经统一清空,这里保留是为了单独调用 IndexItem 时的兼容性)
_, err = idx.db.Exec("DELETE FROM knowledge_embeddings WHERE item_id = ?", itemID)
if err != nil {
return fmt.Errorf("删除旧向量失败: %w", err)
return fmt.Errorf("删除旧向量失败%w", err)
}
// 分块
chunks := idx.ChunkText(content)
// 应用最大块数限制
if idx.maxChunks > 0 && len(chunks) > idx.maxChunks {
idx.logger.Info("知识项块数量超过限制,已截断",
zap.String("itemId", itemID),
zap.Int("originalChunks", len(chunks)),
zap.Int("maxChunks", idx.maxChunks))
chunks = chunks[:idx.maxChunks]
}
idx.logger.Info("知识项分块完成", zap.String("itemId", itemID), zap.Int("chunks", len(chunks)))
// 跟踪该知识项的错误
itemErrorCount := 0
var firstError error
firstErrorChunkIndex := -1
// 向量化每个块(包含categorytitle信息,以便向量检索时能匹配到风险类型)
// 向量化每个块(包含 categorytitle 信息,以便向量检索时能匹配到风险类型)
for i, chunk := range chunks {
// 将categorytitle信息包含到向量化的文本中
// 格式:"[风险类型: {category}] [标题: {title}]\n{chunk内容}"
// 这样向量嵌入就会包含风险类型信息,即使SQL过滤失败,向量相似度也能帮助匹配
textForEmbedding := fmt.Sprintf("[风险类型: %s] [标题: %s]\n%s", category, title, chunk)
// 将 categorytitle 信息包含到向量化的文本中
// 格式:"[风险类型{category}] [标题{title}]\n{chunk 内容}"
// 这样向量嵌入就会包含风险类型信息,即使 SQL 过滤失败,向量相似度也能帮助匹配
textForEmbedding := fmt.Sprintf("[风险类型%s] [标题%s]\n%s", category, title, chunk)
embedding, err := idx.embedder.EmbedText(ctx, textForEmbedding)
if err != nil {
@@ -305,17 +427,17 @@ func (idx *Indexer) IndexItem(ctx context.Context, itemID string) error {
zap.String("chunkPreview", chunkPreview),
zap.Error(err),
)
// 更新全局错误跟踪
errorMsg := fmt.Sprintf("向量化失败 (知识项: %s): %v", itemID, err)
errorMsg := fmt.Sprintf("向量化失败 (知识项%s): %v", itemID, err)
idx.mu.Lock()
idx.lastError = errorMsg
idx.lastErrorTime = time.Now()
idx.mu.Unlock()
}
// 如果连续失败2个块,立即停止处理该知识项(降低阈值,更快停止)
// 这样可以避免继续浪费API调用,同时也能更快地检测到配置问题
// 如果连续失败 2 个块,立即停止处理该知识项(降低阈值,更快停止)
// 这样可以避免继续浪费 API 调用,同时也能更快地检测到配置问题
if itemErrorCount >= 2 {
idx.logger.Error("知识项连续向量化失败,停止处理",
zap.String("itemId", itemID),
@@ -344,6 +466,13 @@ func (idx *Indexer) IndexItem(ctx context.Context, itemID string) error {
}
idx.logger.Info("知识项索引完成", zap.String("itemId", itemID), zap.Int("chunks", len(chunks)))
// 更新重建状态中的最近处理信息
idx.rebuildMu.Lock()
idx.rebuildLastItemID = itemID
idx.rebuildLastChunks = len(chunks)
idx.rebuildMu.Unlock()
return nil
}
@@ -352,23 +481,38 @@ func (idx *Indexer) HasIndex() (bool, error) {
var count int
err := idx.db.QueryRow("SELECT COUNT(*) FROM knowledge_embeddings").Scan(&count)
if err != nil {
return false, fmt.Errorf("检查索引失败: %w", err)
return false, fmt.Errorf("检查索引失败%w", err)
}
return count > 0, nil
}
// RebuildIndex 重建所有索引
func (idx *Indexer) RebuildIndex(ctx context.Context) error {
// 设置重建状态
idx.rebuildMu.Lock()
idx.isRebuilding = true
idx.rebuildTotalItems = 0
idx.rebuildCurrent = 0
idx.rebuildFailed = 0
idx.rebuildStartTime = time.Now()
idx.rebuildLastItemID = ""
idx.rebuildLastChunks = 0
idx.rebuildMu.Unlock()
// 重置错误跟踪
idx.mu.Lock()
idx.lastError = ""
idx.lastErrorTime = time.Time{}
idx.errorCount = 0
idx.mu.Unlock()
rows, err := idx.db.Query("SELECT id FROM knowledge_base_items")
if err != nil {
return fmt.Errorf("查询知识项失败: %w", err)
// 重置重建状态
idx.rebuildMu.Lock()
idx.isRebuilding = false
idx.rebuildMu.Unlock()
return fmt.Errorf("查询知识项失败:%w", err)
}
defer rows.Close()
@@ -376,34 +520,36 @@ func (idx *Indexer) RebuildIndex(ctx context.Context) error {
for rows.Next() {
var id string
if err := rows.Scan(&id); err != nil {
return fmt.Errorf("扫描知识项ID失败: %w", err)
// 重置重建状态
idx.rebuildMu.Lock()
idx.isRebuilding = false
idx.rebuildMu.Unlock()
return fmt.Errorf("扫描知识项 ID 失败:%w", err)
}
itemIDs = append(itemIDs, id)
}
idx.rebuildMu.Lock()
idx.rebuildTotalItems = len(itemIDs)
idx.rebuildMu.Unlock()
idx.logger.Info("开始重建索引", zap.Int("totalItems", len(itemIDs)))
// 在开始重建前,先清空所有旧的向量,确保进度从0开始
// 这样 GetIndexStatus 可以准确反映重建进度
_, err = idx.db.Exec("DELETE FROM knowledge_embeddings")
if err != nil {
idx.logger.Warn("清空旧索引失败", zap.Error(err))
// 继续执行,即使清空失败也尝试重建
} else {
idx.logger.Info("已清空旧索引,开始重建")
}
// 注意:不再清空所有旧索引,而是按增量方式更新
// 每个知识项在 IndexItem 中会先删除自己的旧向量,然后插入新向量
// 这样配置更新后只重新索引变化的知识项,保留其他知识项的索引
failedCount := 0
consecutiveFailures := 0
maxConsecutiveFailures := 2 // 连续失败2次后立即停止(降低阈值,更快停止)
maxConsecutiveFailures := 2 // 连续失败 2 次后立即停止(降低阈值,更快停止)
firstFailureItemID := ""
var firstFailureError error
for i, itemID := range itemIDs {
if err := idx.IndexItem(ctx, itemID); err != nil {
failedCount++
consecutiveFailures++
// 只在第一个失败时记录详细日志
if consecutiveFailures == 1 {
firstFailureItemID = itemID
@@ -414,15 +560,15 @@ func (idx *Indexer) RebuildIndex(ctx context.Context) error {
zap.Error(err),
)
}
// 如果连续失败过多,可能是配置问题,立即停止索引
if consecutiveFailures >= maxConsecutiveFailures {
errorMsg := fmt.Sprintf("连续 %d 个知识项索引失败,可能存在配置问题(如嵌入模型配置错误、API密钥无效、余额不足等)。第一个失败项: %s, 错误: %v", consecutiveFailures, firstFailureItemID, firstFailureError)
errorMsg := fmt.Sprintf("连续 %d 个知识项索引失败,可能存在配置问题(如嵌入模型配置错误、API 密钥无效、余额不足等)。第一个失败项%s, 错误%v", consecutiveFailures, firstFailureItemID, firstFailureError)
idx.mu.Lock()
idx.lastError = errorMsg
idx.lastErrorTime = time.Now()
idx.mu.Unlock()
idx.logger.Error("连续索引失败次数过多,立即停止索引",
zap.Int("consecutiveFailures", consecutiveFailures),
zap.Int("totalItems", len(itemIDs)),
@@ -430,17 +576,17 @@ func (idx *Indexer) RebuildIndex(ctx context.Context) error {
zap.String("firstFailureItemId", firstFailureItemID),
zap.Error(firstFailureError),
)
return fmt.Errorf("连续索引失败次数过多: %v", firstFailureError)
return fmt.Errorf("连续索引失败次数过多%v", firstFailureError)
}
// 如果失败的知识项过多,记录警告但继续处理(降低阈值到30%)
// 如果失败的知识项过多,记录警告但继续处理(降低阈值到 30%
if failedCount > len(itemIDs)*3/10 && failedCount == len(itemIDs)*3/10+1 {
errorMsg := fmt.Sprintf("索引失败的知识项过多 (%d/%d),可能存在配置问题。第一个失败项: %s, 错误: %v", failedCount, len(itemIDs), firstFailureItemID, firstFailureError)
errorMsg := fmt.Sprintf("索引失败的知识项过多 (%d/%d),可能存在配置问题。第一个失败项%s, 错误%v", failedCount, len(itemIDs), firstFailureItemID, firstFailureError)
idx.mu.Lock()
idx.lastError = errorMsg
idx.lastErrorTime = time.Now()
idx.mu.Unlock()
idx.logger.Error("索引失败的知识项过多,可能存在配置问题",
zap.Int("failedCount", failedCount),
zap.Int("totalItems", len(itemIDs)),
@@ -450,20 +596,31 @@ func (idx *Indexer) RebuildIndex(ctx context.Context) error {
}
continue
}
// 成功时重置连续失败计数和第一个失败信息
if consecutiveFailures > 0 {
consecutiveFailures = 0
firstFailureItemID = ""
firstFailureError = nil
}
// 减少进度日志频率(每10个或每10%记录一次)
// 更新重建进度
idx.rebuildMu.Lock()
idx.rebuildCurrent = i + 1
idx.rebuildFailed = failedCount
idx.rebuildMu.Unlock()
// 减少进度日志频率(每 10 个或每 10% 记录一次)
if (i+1)%10 == 0 || (len(itemIDs) > 0 && (i+1)*100/len(itemIDs)%10 == 0 && (i+1)*100/len(itemIDs) > 0) {
idx.logger.Info("索引进度", zap.Int("current", i+1), zap.Int("total", len(itemIDs)), zap.Int("failed", failedCount))
}
}
// 重置重建状态
idx.rebuildMu.Lock()
idx.isRebuilding = false
idx.rebuildMu.Unlock()
idx.logger.Info("索引重建完成", zap.Int("totalItems", len(itemIDs)), zap.Int("failedCount", failedCount))
return nil
}
@@ -474,3 +631,10 @@ func (idx *Indexer) GetLastError() (string, time.Time) {
defer idx.mu.RUnlock()
return idx.lastError, idx.lastErrorTime
}
// GetRebuildStatus 获取重建索引状态
func (idx *Indexer) GetRebuildStatus() (isRebuilding bool, totalItems int, current int, failed int, lastItemID string, lastChunks int, startTime time.Time) {
idx.rebuildMu.RLock()
defer idx.rebuildMu.RUnlock()
return idx.isRebuilding, idx.rebuildTotalItems, idx.rebuildCurrent, idx.rebuildFailed, idx.rebuildLastItemID, idx.rebuildLastChunks, idx.rebuildStartTime
}
+42
View File
@@ -459,6 +459,9 @@ async function updateIndexProgress() {
const isComplete = status.is_complete || false;
const lastError = status.last_error || '';
// 检查是否正在重建索引(优先使用重建状态)
const isRebuilding = status.is_rebuilding || false;
if (totalItems === 0) {
// 没有知识项,隐藏进度条
progressContainer.style.display = 'none';
@@ -524,6 +527,45 @@ async function updateIndexProgress() {
return;
}
// 优先处理重建状态
if (isRebuilding) {
const rebuildTotal = status.rebuild_total || totalItems;
const rebuildCurrent = status.rebuild_current || 0;
const rebuildFailed = status.rebuild_failed || 0;
const rebuildLastItemID = status.rebuild_last_item_id || '';
const rebuildLastChunks = status.rebuild_last_chunks || 0;
const rebuildStartTime = status.rebuild_start_time || '';
// 计算进度百分比(使用重建进度)
let rebuildProgress = progressPercent;
if (rebuildTotal > 0) {
rebuildProgress = (rebuildCurrent / rebuildTotal) * 100;
}
progressContainer.innerHTML = `
<div class="knowledge-index-progress">
<div class="progress-header">
<span class="progress-icon">🔨</span>
<span class="progress-text">正在重建索引${rebuildCurrent}/${rebuildTotal} (${rebuildProgress.toFixed(1)}%) - 失败${rebuildFailed}</span>
</div>
<div class="progress-bar-container">
<div class="progress-bar" style="width: ${rebuildProgress}%"></div>
</div>
<div class="progress-hint">
${rebuildLastItemID ? `正在处理:${escapeHtml(rebuildLastItemID.substring(0, 36))}... (${rebuildLastChunks} chunks)` : '正在处理...'}
${rebuildStartTime ? `<br>开始时间:${new Date(rebuildStartTime).toLocaleString()}` : ''}
</div>
</div>
`;
// 重建中时继续轮询
if (!indexProgressInterval) {
indexProgressInterval = setInterval(updateIndexProgress, 2000);
}
return;
}
if (isComplete) {
progressContainer.innerHTML = `
<div class="knowledge-index-progress-complete">
File diff suppressed because it is too large Load Diff
+46
View File
@@ -172,6 +172,43 @@ async function loadConfig(loadTools = true) {
// 允许0.0值,只有undefined/null时才使用默认值
retrievalWeightInput.value = (hybridWeight !== undefined && hybridWeight !== null) ? hybridWeight : 0.7;
}
// 索引配置
const indexing = knowledge.indexing || {};
const chunkSizeInput = document.getElementById('knowledge-indexing-chunk-size');
if (chunkSizeInput) {
chunkSizeInput.value = indexing.chunk_size || 512;
}
const chunkOverlapInput = document.getElementById('knowledge-indexing-chunk-overlap');
if (chunkOverlapInput) {
chunkOverlapInput.value = indexing.chunk_overlap ?? 50;
}
const maxChunksPerItemInput = document.getElementById('knowledge-indexing-max-chunks-per-item');
if (maxChunksPerItemInput) {
maxChunksPerItemInput.value = indexing.max_chunks_per_item ?? 0;
}
const maxRpmInput = document.getElementById('knowledge-indexing-max-rpm');
if (maxRpmInput) {
maxRpmInput.value = indexing.max_rpm ?? 0;
}
const rateLimitDelayInput = document.getElementById('knowledge-indexing-rate-limit-delay-ms');
if (rateLimitDelayInput) {
rateLimitDelayInput.value = indexing.rate_limit_delay_ms ?? 300;
}
const maxRetriesInput = document.getElementById('knowledge-indexing-max-retries');
if (maxRetriesInput) {
maxRetriesInput.value = indexing.max_retries ?? 3;
}
const retryDelayInput = document.getElementById('knowledge-indexing-retry-delay-ms');
if (retryDelayInput) {
retryDelayInput.value = indexing.retry_delay_ms ?? 1000;
}
}
// 填充机器人配置
@@ -728,6 +765,15 @@ async function applySettings() {
const val = parseFloat(document.getElementById('knowledge-retrieval-hybrid-weight')?.value);
return isNaN(val) ? 0.7 : val; // 允许0.0值,只有NaN时才使用默认值
})()
},
indexing: {
chunk_size: parseInt(document.getElementById("knowledge-indexing-chunk-size")?.value) || 512,
chunk_overlap: parseInt(document.getElementById("knowledge-indexing-chunk-overlap")?.value) ?? 50,
max_chunks_per_item: parseInt(document.getElementById("knowledge-indexing-max-chunks-per-item")?.value) ?? 0,
max_rpm: parseInt(document.getElementById("knowledge-indexing-max-rpm")?.value) ?? 0,
rate_limit_delay_ms: parseInt(document.getElementById("knowledge-indexing-rate-limit-delay-ms")?.value) ?? 300,
max_retries: parseInt(document.getElementById("knowledge-indexing-max-retries")?.value) ?? 3,
retry_delay_ms: parseInt(document.getElementById("knowledge-indexing-retry-delay-ms")?.value) ?? 1000
}
};
+38 -1
View File
@@ -1203,7 +1203,44 @@
<small class="form-hint">向量检索的权重(0-1),1.0表示纯向量检索,0.0表示纯关键词检索</small>
</div>
</div>
</div>
<div class="settings-subsection-header">
<h5>索引配置</h5>
</div>
<div class="form-group">
<label for="knowledge-indexing-chunk-size">分块大小(Chunk Size</label>
<input type="number" id="knowledge-indexing-chunk-size" min="128" max="4096" placeholder="512" />
<small class="form-hint">每个块的最大 token 数(默认 512),长文本会被分割成多个块</small>
</div>
<div class="form-group">
<label for="knowledge-indexing-chunk-overlap">分块重叠(Chunk Overlap</label>
<input type="number" id="knowledge-indexing-chunk-overlap" min="0" max="512" placeholder="50" />
<small class="form-hint">块之间的重叠 token 数(默认 50),保持上下文连贯性</small>
</div>
<div class="form-group">
<label for="knowledge-indexing-max-chunks-per-item">单个知识项最大块数</label>
<input type="number" id="knowledge-indexing-max-chunks-per-item" min="0" max="1000" placeholder="0" />
<small class="form-hint">单个知识项的最大块数量(0 表示不限制),防止单个文件消耗过多 API 配额</small>
</div>
<div class="form-group">
<label for="knowledge-indexing-max-rpm">每分钟最大请求数(Max RPM</label>
<input type="number" id="knowledge-indexing-max-rpm" min="0" max="1000" placeholder="0" />
<small class="form-hint">每分钟最大请求数(默认 0 表示不限制),如 OpenAI 默认 200 RPM</small>
</div>
<div class="form-group">
<label for="knowledge-indexing-rate-limit-delay-ms">请求间隔延迟(毫秒)</label>
<input type="number" id="knowledge-indexing-rate-limit-delay-ms" min="0" max="10000" placeholder="300" />
<small class="form-hint">请求间隔毫秒数(默认 300),用于避免 API 速率限制,设为 0 不限制</small>
</div>
<div class="form-group">
<label for="knowledge-indexing-max-retries">最大重试次数</label>
<input type="number" id="knowledge-indexing-max-retries" min="0" max="10" placeholder="3" />
<small class="form-hint">最大重试次数(默认 3),遇到速率限制或服务器错误时自动重试</small>
</div>
<div class="form-group">
<label for="knowledge-indexing-retry-delay-ms">重试间隔(毫秒)</label>
<input type="number" id="knowledge-indexing-retry-delay-ms" min="0" max="10000" placeholder="1000" />
<small class="form-hint">重试间隔毫秒数(默认 1000),每次重试会递增延迟</small>
</div> </div>
<div class="settings-actions">
<button class="btn-primary" onclick="applySettings()">应用配置</button>
File diff suppressed because it is too large Load Diff