t
2bbf42b741
Remove Agent Desk users, roles, login sessions, tokens, and local permission persistence. Expose the backend as an embeddable ai-agent module with host-provided subject lookup and operation authorization callbacks, and complete the frontend/backend repository split.
332 lines
10 KiB
Go
332 lines
10 KiB
Go
package rag
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import (
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"context"
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"crypto/sha256"
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"encoding/hex"
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"encoding/json"
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"fmt"
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"log/slog"
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"time"
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"code.tczkiot.com/wlw/ai-agent/internal/ai"
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ragchunk "code.tczkiot.com/wlw/ai-agent/internal/ai/rag/chunk"
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"code.tczkiot.com/wlw/ai-agent/internal/ai/rag/vectordb"
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"code.tczkiot.com/wlw/ai-agent/internal/models"
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"code.tczkiot.com/wlw/ai-agent/internal/pkg/enums"
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"code.tczkiot.com/wlw/ai-agent/internal/repositories"
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"github.com/google/uuid"
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"github.com/mlogclub/simple/sqls"
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)
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type ChunkingConfig struct {
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Provider string
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TargetTokens int
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MaxTokens int
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OverlapTokens int
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EnableFallback bool
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}
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type index struct {
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chunkConfig ChunkingConfig
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registry *ragchunk.Registry
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}
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const knowledgeCollectionName = "knowledge_chunks"
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var Index = &index{
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chunkConfig: ChunkingConfig{
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Provider: string(enums.KnowledgeChunkProviderStructured),
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TargetTokens: 300,
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MaxTokens: 400,
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OverlapTokens: 40,
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EnableFallback: true,
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},
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registry: ragchunk.NewDefaultRegistry(),
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}
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func (s *index) IndexDocumentByID(ctx context.Context, documentID int64) error {
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document, err := s.loadDocumentByID(documentID)
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if err != nil {
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return err
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}
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return s.IndexDocument(ctx, *document)
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}
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func (s *index) IndexDocument(ctx context.Context, document models.KnowledgeDocument) error {
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start := time.Now()
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if err := s.markDocumentIndexPending(document.ID); err != nil {
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slog.Error("Failed to mark knowledge document index as pending", "document_id", document.ID, "error", err)
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}
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fail := func(err error) error {
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if updateErr := s.markDocumentIndexFailed(document.ID, err); updateErr != nil {
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slog.Error("Failed to mark knowledge document index as failed", "document_id", document.ID, "error", updateErr)
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}
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return err
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}
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// TODO 这里每次都查询下知识库不太友好
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knowledgeBase, err := s.loadDocumentKnowledgeBase(document)
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if err != nil {
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return fail(err)
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}
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vectors, chunkCount, err := s.runDocumentIndex(ctx, document, *knowledgeBase)
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if err != nil {
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return fail(err)
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}
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if err := s.markDocumentIndexIndexed(document.ID); err != nil {
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slog.Error("Failed to mark knowledge document index as indexed", "document_id", document.ID, "error", err)
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}
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slog.Info("Document indexed successfully",
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slog.Any("document_id", document.ID),
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slog.Any("chunks_count", chunkCount),
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slog.Any("vectors_count", len(vectors)),
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slog.Any("time_taken", time.Since(start).String()),
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)
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return nil
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}
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func (s *index) IndexFAQByID(ctx context.Context, faqID int64) error {
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faq, err := s.loadFAQByID(faqID)
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if err != nil {
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return err
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}
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if err := s.markFAQIndexPending(faq.ID); err != nil {
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slog.Error("Failed to mark knowledge faq index as pending", "faq_id", faq.ID, "error", err)
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}
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fail := func(err error) error {
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if updateErr := s.markFAQIndexFailed(faq.ID, err); updateErr != nil {
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slog.Error("Failed to mark knowledge faq index as failed", "faq_id", faq.ID, "error", updateErr)
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}
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return err
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}
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knowledgeBase, err := s.loadFAQKnowledgeBase(*faq)
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if err != nil {
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return fail(err)
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}
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if err := s.runFAQIndex(ctx, *faq, *knowledgeBase); err != nil {
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return fail(err)
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}
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if err := s.markFAQIndexIndexed(faq.ID); err != nil {
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slog.Error("Failed to mark knowledge faq index as indexed", "faq_id", faq.ID, "error", err)
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}
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return nil
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}
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func (s *index) RemoveDocumentIndex(ctx context.Context, documentID int64) error {
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chunks := repositories.KnowledgeChunkRepository.FindByDocumentID(sqls.DB(), documentID)
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if len(chunks) == 0 {
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return nil
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}
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if err := s.deleteChunkVectors(ctx, s.collectChunkVectorIDs(chunks)); err != nil {
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slog.Error("Failed to delete vectors", "error", err)
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}
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if err := repositories.KnowledgeChunkRepository.DeleteByDocumentID(sqls.DB(), documentID); err != nil {
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return fmt.Errorf("failed to delete chunks: %w", err)
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}
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slog.Info("Document index removed", "document_id", documentID, "chunks_removed", len(chunks))
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return nil
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}
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func (s *index) RemoveFAQIndex(ctx context.Context, faqID int64) error {
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chunks := repositories.KnowledgeChunkRepository.FindByFaqID(sqls.DB(), faqID)
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if len(chunks) == 0 {
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return nil
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}
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if err := s.deleteChunkVectors(ctx, s.collectChunkVectorIDs(chunks)); err != nil {
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slog.Error("Failed to delete faq vectors", "error", err)
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}
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if err := repositories.KnowledgeChunkRepository.DeleteByFaqID(sqls.DB(), faqID); err != nil {
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return fmt.Errorf("failed to delete faq chunks: %w", err)
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}
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slog.Info("FAQ index removed", "faq_id", faqID, "chunks_removed", len(chunks))
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return nil
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}
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func (s *index) RemoveKnowledgeBaseIndex(ctx context.Context, knowledgeBaseID int64) error {
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chunks := repositories.KnowledgeChunkRepository.FindByKnowledgeBaseID(sqls.DB(), knowledgeBaseID)
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if len(chunks) == 0 {
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return nil
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}
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if err := s.deleteChunkVectors(ctx, s.collectChunkVectorIDs(chunks)); err != nil {
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slog.Error("Failed to delete knowledge base vectors", "knowledge_base_id", knowledgeBaseID, "error", err)
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}
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if err := repositories.KnowledgeChunkRepository.DeleteByKnowledgeBaseID(sqls.DB(), knowledgeBaseID); err != nil {
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return fmt.Errorf("failed to delete chunks for knowledge base %d: %w", knowledgeBaseID, err)
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}
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slog.Info("Knowledge base index removed", "knowledge_base_id", knowledgeBaseID, "chunks_removed", len(chunks))
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return nil
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}
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func (s *index) getCollectionName() string {
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return knowledgeCollectionName
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}
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func buildKnowledgeChunkVectorID(knowledgeBaseID int64, documentID int64, chunkNo int) string {
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raw := fmt.Sprintf("kb:%d:doc:%d:chunk:%d", knowledgeBaseID, documentID, chunkNo)
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return uuid.NewSHA1(uuid.NameSpaceOID, []byte(raw)).String()
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}
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func buildKnowledgeFAQChunkVectorID(knowledgeBaseID int64, faqID int64, chunkNo int) string {
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raw := fmt.Sprintf("kb:%d:faq:%d:chunk:%d", knowledgeBaseID, faqID, chunkNo)
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return uuid.NewSHA1(uuid.NameSpaceOID, []byte(raw)).String()
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}
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func buildChunkContentHash(content string) string {
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sum := sha256.Sum256([]byte(content))
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return hex.EncodeToString(sum[:])
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}
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func firstPositiveInt(values ...int) int {
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for _, value := range values {
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if value > 0 {
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return value
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}
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}
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return 0
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}
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func firstNonEmptyString(values ...string) string {
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for _, value := range values {
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if value != "" {
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return value
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}
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}
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return ""
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}
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func (s *index) EnsureCollection(ctx context.Context) error {
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dimension, err := ai.Embedding.GetDimension(ctx)
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if err != nil {
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return fmt.Errorf("failed to get embedding dimension: %w", err)
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}
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collectionName := s.getCollectionName()
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provider := vectordb.GetProvider()
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if provider == nil {
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return fmt.Errorf("vectordb provider not initialized")
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}
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existing, err := provider.GetCollection(ctx, collectionName)
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if err == nil && existing != nil {
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return nil
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}
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return provider.CreateCollection(ctx, collectionName, dimension)
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}
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func (s *index) RebuildKnowledgeBaseIndex(ctx context.Context, knowledgeBaseID int64) error {
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knowledgeBase := repositories.KnowledgeBaseRepository.Get(sqls.DB(), knowledgeBaseID)
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if knowledgeBase == nil {
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return fmt.Errorf("knowledge base not found: %d", knowledgeBaseID)
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}
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if err := s.resetKnowledgeBaseIndexStorage(ctx, knowledgeBaseID); err != nil {
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return err
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}
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successCount := 0
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failedCount := 0
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if knowledgeBase.KnowledgeType == string(enums.KnowledgeBaseTypeFAQ) {
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faqs := repositories.KnowledgeFAQRepository.Find(sqls.DB(), sqls.NewCnd().
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Eq("knowledge_base_id", knowledgeBaseID).
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Where("status != ?", enums.StatusDeleted))
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if len(faqs) == 0 {
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slog.Info("No faqs found in knowledge base, nothing to rebuild", "knowledge_base_id", knowledgeBaseID)
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return nil
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}
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slog.Info("Rebuilding faq knowledge base index", "knowledge_base_id", knowledgeBaseID, "faq_count", len(faqs))
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for _, faq := range faqs {
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if err := s.IndexFAQByID(ctx, faq.ID); err != nil {
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slog.Error("Failed to index faq", "faq_id", faq.ID, "error", err)
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failedCount++
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} else {
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successCount++
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}
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}
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} else {
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documents := repositories.KnowledgeDocumentRepository.Find(sqls.DB(), sqls.NewCnd().
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Eq("knowledge_base_id", knowledgeBaseID).
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Where("status != ?", enums.StatusDeleted))
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if len(documents) == 0 {
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slog.Info("No documents found in knowledge base, nothing to rebuild", "knowledge_base_id", knowledgeBaseID)
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return nil
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}
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documentIDs := make([]int64, 0, len(documents))
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for _, doc := range documents {
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documentIDs = append(documentIDs, doc.ID)
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}
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if err := s.markKnowledgeBaseDocumentsIndexPending(knowledgeBaseID, documentIDs); err != nil {
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slog.Error("Failed to mark knowledge base documents index as pending", "knowledge_base_id", knowledgeBaseID, "error", err)
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}
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slog.Info("Rebuilding knowledge base index", "knowledge_base_id", knowledgeBaseID, "document_count", len(documents))
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for _, doc := range documents {
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if err := s.IndexDocumentByID(ctx, doc.ID); err != nil {
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slog.Error("Failed to index document", "document_id", doc.ID, "error", err)
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failedCount++
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} else {
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successCount++
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}
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}
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}
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slog.Info("Knowledge base index rebuild completed",
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"knowledge_base_id", knowledgeBaseID,
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"success_count", successCount,
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"failed_count", failedCount)
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return nil
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}
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func buildFAQChunkContent(faq models.KnowledgeFAQ) string {
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parts := []string{fmt.Sprintf("问题：%s", faq.Question)}
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var similarQuestions []string
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if faq.SimilarQuestions != "" {
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_ = json.Unmarshal([]byte(faq.SimilarQuestions), &similarQuestions)
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}
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if len(similarQuestions) > 0 {
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parts = append(parts, fmt.Sprintf("相似问：%s", joinSimilarQuestions(similarQuestions)))
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}
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parts = append(parts, fmt.Sprintf("回答：%s", faq.Answer))
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content := ""
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for _, part := range parts {
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if part == "" {
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continue
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}
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if content != "" {
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content += "\n"
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}
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content += part
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}
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return content
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}
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func joinSimilarQuestions(items []string) string {
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result := ""
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for _, item := range items {
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if item == "" {
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continue
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}
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if result != "" {
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result += "；"
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}
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result += item
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}
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return result
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}
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func (s *index) resetKnowledgeBaseIndexStorage(ctx context.Context, knowledgeBaseID int64) error {
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chunks := repositories.KnowledgeChunkRepository.FindByKnowledgeBaseID(sqls.DB(), knowledgeBaseID)
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return s.cleanupKnowledgeBaseChunks(ctx, knowledgeBaseID, chunks)
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}
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