mlogclub
703 lines
22 KiB
Go
703 lines
22 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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"cs-agent/internal/ai"
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ragchunk "cs-agent/internal/ai/rag/chunk"
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"cs-agent/internal/ai/rag/vectordb"
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"cs-agent/internal/models"
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"cs-agent/internal/pkg/enums"
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"cs-agent/internal/repositories"
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"github.com/google/uuid"
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"github.com/mlogclub/simple/common/strs"
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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 := repositories.KnowledgeDocumentRepository.Get(sqls.DB(), documentID)
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if document == nil {
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return fmt.Errorf("document not found: %d", documentID)
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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 := repositories.KnowledgeBaseRepository.Get(sqls.DB(), document.KnowledgeBaseID)
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if knowledgeBase == nil {
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return fail(fmt.Errorf("knowledge base not found: %d", document.KnowledgeBaseID))
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}
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existingChunks := repositories.KnowledgeChunkRepository.FindByDocumentID(sqls.DB(), document.ID)
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chunks, err := s.registry.Chunk(ctx, &ragchunk.ChunkRequest{
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KnowledgeBaseID: document.KnowledgeBaseID,
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DocumentID: document.ID,
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DocumentTitle: document.Title,
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ContentType: document.ContentType,
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Content: document.Content,
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PlainText: ExtractPlainText(document.Content, document.ContentType),
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Options: ragchunk.ChunkOptions{
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Provider: firstNonEmptyString(knowledgeBase.ChunkProvider, s.chunkConfig.Provider),
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TargetTokens: firstPositiveInt(knowledgeBase.ChunkTargetTokens, s.chunkConfig.TargetTokens),
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MaxTokens: firstPositiveInt(knowledgeBase.ChunkMaxTokens, s.chunkConfig.MaxTokens),
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OverlapTokens: firstPositiveInt(knowledgeBase.ChunkOverlapTokens, s.chunkConfig.OverlapTokens),
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EnableFallback: s.chunkConfig.EnableFallback,
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},
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})
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if err != nil {
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return fail(fmt.Errorf("failed to chunk document: %w", err))
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}
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if len(chunks) == 0 {
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return fail(fmt.Errorf("no chunks generated from document"))
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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 fail(fmt.Errorf("vectordb provider not initialized"))
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}
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if _, err := ai.Embedding.GetModel(ctx); err != nil {
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return fail(fmt.Errorf("failed to get embedding model: %w", err))
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}
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existingVectorIDs := make([]string, 0, len(existingChunks))
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for _, chunk := range existingChunks {
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if strs.IsNotBlank(chunk.VectorID) {
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existingVectorIDs = append(existingVectorIDs, chunk.VectorID)
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}
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}
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vectors := make([]vectordb.Vector, 0, len(chunks))
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chunkModels := make([]models.KnowledgeChunk, 0, len(chunks))
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dimension := 0
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for i, chunk := range chunks {
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embeddingResult, err := ai.Embedding.GenerateEmbedding(ctx, chunk.Content)
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if err != nil {
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slog.Error("Failed to generate embedding for chunk", "document_id", document.ID, "chunk_index", i, "error", err)
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return fail(fmt.Errorf("failed to generate embedding for chunk %d: %w", i, err))
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}
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if dimension == 0 {
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dimension = embeddingResult.Dimension
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}
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chunkID := buildKnowledgeChunkVectorID(knowledgeBase.ID, document.ID, chunk.ChunkNo)
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providerName := ""
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if chunk.Metadata != nil {
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if value, ok := chunk.Metadata["provider"].(string); ok {
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providerName = value
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}
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}
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chunkModel := models.KnowledgeChunk{
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KnowledgeBaseID: knowledgeBase.ID,
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DocumentID: document.ID,
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ChunkNo: chunk.ChunkNo,
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Title: chunk.Title,
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Content: chunk.Content,
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ContentHash: buildChunkContentHash(chunk.Content),
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CharCount: chunk.CharCount,
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TokenCount: chunk.TokenCount,
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ChunkType: string(chunk.ChunkType),
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SectionPath: chunk.SectionPath,
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Provider: providerName,
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VectorID: chunkID,
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Status: enums.StatusOk,
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CreatedAt: time.Now(),
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UpdatedAt: time.Now(),
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}
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chunkModels = append(chunkModels, chunkModel)
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vectors = append(vectors, vectordb.Vector{
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ID: chunkID,
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Vector: embeddingResult.Vector,
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Payload: vectordb.ChunkPayload{
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KnowledgeBaseID: knowledgeBase.ID,
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DocumentID: document.ID,
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DocumentTitle: document.Title,
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ChunkNo: chunk.ChunkNo,
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ChunkType: string(chunk.ChunkType),
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SectionPath: chunk.SectionPath,
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Content: chunk.Content,
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Title: chunk.Title,
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Provider: providerName,
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},
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})
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}
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if len(vectors) == 0 {
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return fail(fmt.Errorf("no vectors generated"))
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}
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collectionInfo, err := provider.GetCollection(ctx, collectionName)
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if err != nil || collectionInfo == nil {
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if dimension <= 0 {
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return fail(fmt.Errorf("invalid embedding dimension: %d", dimension))
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}
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if err := provider.CreateCollection(ctx, collectionName, dimension); err != nil {
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return fail(fmt.Errorf("failed to create collection: %w", err))
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}
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slog.Info("Created collection for knowledge base", "collection", collectionName, "dimension", dimension)
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}
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if len(existingVectorIDs) > 0 {
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if err := provider.DeleteVectors(ctx, collectionName, existingVectorIDs); err != nil {
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return fail(fmt.Errorf("failed to delete old vectors: %w", err))
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}
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}
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if err := provider.UpsertVectors(ctx, collectionName, vectors); err != nil {
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return fail(fmt.Errorf("failed to upsert vectors: %w", err))
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}
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if err := sqls.WithTransaction(func(ctx *sqls.TxContext) error {
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if err := ctx.Tx.Where("document_id = ?", document.ID).Delete(&models.KnowledgeChunk{}).Error; err != nil {
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return err
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}
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for _, chunk := range chunkModels {
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if err := ctx.Tx.Create(&chunk).Error; err != nil {
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return err
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}
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}
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return nil
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}); err != nil {
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return fail(fmt.Errorf("failed to save chunks: %w", 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", len(chunks)),
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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 := repositories.KnowledgeFAQRepository.Get(sqls.DB(), faqID)
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if faq == nil {
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return fmt.Errorf("faq not found: %d", faqID)
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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 := repositories.KnowledgeBaseRepository.Get(sqls.DB(), faq.KnowledgeBaseID)
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if knowledgeBase == nil {
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return fail(fmt.Errorf("knowledge base not found: %d", faq.KnowledgeBaseID))
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}
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if knowledgeBase.KnowledgeType != string(enums.KnowledgeBaseTypeFAQ) {
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return fail(fmt.Errorf("knowledge base %d is not faq type", knowledgeBase.ID))
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}
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existingChunks := repositories.KnowledgeChunkRepository.FindByFaqID(sqls.DB(), faq.ID)
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content := buildFAQChunkContent(faq)
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if content == "" {
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return fail(fmt.Errorf("faq content is empty"))
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}
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provider := vectordb.GetProvider()
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if provider == nil {
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return fail(fmt.Errorf("vectordb provider not initialized"))
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}
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if _, err := ai.Embedding.GetModel(ctx); err != nil {
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return fail(fmt.Errorf("failed to get embedding model: %w", err))
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}
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embeddingResult, err := ai.Embedding.GenerateEmbedding(ctx, content)
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if err != nil {
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return fail(fmt.Errorf("failed to generate embedding for faq %d: %w", faq.ID, err))
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}
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chunkID := buildKnowledgeFAQChunkVectorID(knowledgeBase.ID, faq.ID, 0)
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chunkModel := models.KnowledgeChunk{
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KnowledgeBaseID: knowledgeBase.ID,
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FaqID: faq.ID,
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ChunkNo: 0,
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Title: faq.Question,
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Content: content,
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ContentHash: buildChunkContentHash(content),
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CharCount: len([]rune(content)),
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TokenCount: len([]rune(content)) / 2,
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ChunkType: string(enums.KnowledgeChunkTypeFAQ),
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Provider: string(enums.KnowledgeChunkProviderFAQ),
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VectorID: chunkID,
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Status: enums.StatusOk,
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CreatedAt: time.Now(),
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UpdatedAt: time.Now(),
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}
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collectionName := s.getCollectionName()
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collectionInfo, err := provider.GetCollection(ctx, collectionName)
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if err != nil || collectionInfo == nil {
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if err := provider.CreateCollection(ctx, collectionName, embeddingResult.Dimension); err != nil {
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return fail(fmt.Errorf("failed to create collection: %w", err))
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}
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}
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existingVectorIDs := make([]string, 0, len(existingChunks))
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for _, chunk := range existingChunks {
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if strs.IsNotBlank(chunk.VectorID) {
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existingVectorIDs = append(existingVectorIDs, chunk.VectorID)
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}
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}
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if len(existingVectorIDs) > 0 {
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if err := provider.DeleteVectors(ctx, collectionName, existingVectorIDs); err != nil {
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return fail(fmt.Errorf("failed to delete old vectors: %w", err))
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}
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}
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if err := provider.UpsertVectors(ctx, collectionName, []vectordb.Vector{{
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ID: chunkID,
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Vector: embeddingResult.Vector,
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Payload: vectordb.ChunkPayload{
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KnowledgeBaseID: knowledgeBase.ID,
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FaqID: faq.ID,
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FaqQuestion: faq.Question,
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ChunkNo: 0,
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ChunkType: string(enums.KnowledgeChunkTypeFAQ),
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Content: content,
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Title: faq.Question,
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Provider: string(enums.KnowledgeChunkProviderFAQ),
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},
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}}); err != nil {
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return fail(fmt.Errorf("failed to upsert vectors: %w", err))
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}
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if err := sqls.WithTransaction(func(ctx *sqls.TxContext) error {
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if err := ctx.Tx.Where("faq_id = ?", faq.ID).Delete(&models.KnowledgeChunk{}).Error; err != nil {
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return err
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}
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return ctx.Tx.Create(&chunkModel).Error
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}); err != nil {
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return fail(fmt.Errorf("failed to save faq chunk: %w", 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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document := repositories.KnowledgeDocumentRepository.Get(sqls.DB(), documentID)
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if document == nil {
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return nil
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}
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chunks := repositories.KnowledgeChunkRepository.Find(sqls.DB(), sqls.NewCnd().Eq("document_id", documentID))
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return s.removeDocumentIndexByChunks(ctx, document.KnowledgeBaseID, documentID, chunks)
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}
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func (s *index) RemoveDocumentIndexFromKnowledgeBase(ctx context.Context, knowledgeBaseID int64, documentID int64) error {
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chunks := repositories.KnowledgeChunkRepository.Find(sqls.DB(), sqls.NewCnd().Eq("document_id", documentID))
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return s.removeDocumentIndexByChunks(ctx, knowledgeBaseID, documentID, chunks)
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}
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func (s *index) RemoveDocumentIndexByChunkModels(ctx context.Context, knowledgeBaseID int64, documentID int64, chunks []models.KnowledgeChunk) error {
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return s.removeDocumentIndexByChunks(ctx, knowledgeBaseID, documentID, chunks)
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}
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func (s *index) removeDocumentIndexByChunks(ctx context.Context, knowledgeBaseID int64, documentID int64, chunks []models.KnowledgeChunk) error {
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if len(chunks) == 0 {
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return nil
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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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vectorIDs := make([]string, 0, len(chunks))
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for _, chunk := range chunks {
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if chunk.VectorID != "" {
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vectorIDs = append(vectorIDs, chunk.VectorID)
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}
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}
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if len(vectorIDs) > 0 {
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if err := provider.DeleteVectors(ctx, collectionName, vectorIDs); err != nil {
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slog.Error("Failed to delete vectors", "error", err)
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}
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}
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if err := sqls.WithTransaction(func(ctx *sqls.TxContext) error {
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return ctx.Tx.Where("document_id = ?", documentID).Delete(&models.KnowledgeChunk{}).Error
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}); 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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faq := repositories.KnowledgeFAQRepository.Get(sqls.DB(), faqID)
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if faq == nil {
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return nil
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}
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chunks := repositories.KnowledgeChunkRepository.FindByFaqID(sqls.DB(), faqID)
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return s.removeFAQIndexByChunks(ctx, faq.KnowledgeBaseID, faqID, chunks)
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}
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func (s *index) RemoveFAQIndexByChunkModels(ctx context.Context, knowledgeBaseID int64, faqID int64, chunks []models.KnowledgeChunk) error {
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return s.removeFAQIndexByChunks(ctx, knowledgeBaseID, faqID, chunks)
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}
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func (s *index) removeFAQIndexByChunks(ctx context.Context, knowledgeBaseID int64, faqID int64, chunks []models.KnowledgeChunk) error {
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if len(chunks) == 0 {
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return nil
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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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vectorIDs := make([]string, 0, len(chunks))
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for _, chunk := range chunks {
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if chunk.VectorID != "" {
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vectorIDs = append(vectorIDs, chunk.VectorID)
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}
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}
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if len(vectorIDs) > 0 {
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if err := provider.DeleteVectors(ctx, collectionName, vectorIDs); err != nil {
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slog.Error("Failed to delete faq vectors", "error", err)
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}
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}
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if err := sqls.WithTransaction(func(ctx *sqls.TxContext) error {
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return ctx.Tx.Where("faq_id = ?", faqID).Delete(&models.KnowledgeChunk{}).Error
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}); 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) 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 {
|
||
knowledgeBase := repositories.KnowledgeBaseRepository.Get(sqls.DB(), knowledgeBaseID)
|
||
if knowledgeBase == nil {
|
||
return fmt.Errorf("knowledge base not found: %d", knowledgeBaseID)
|
||
}
|
||
|
||
if err := s.resetKnowledgeBaseIndexStorage(ctx, knowledgeBaseID); err != nil {
|
||
return err
|
||
}
|
||
|
||
successCount := 0
|
||
failedCount := 0
|
||
if knowledgeBase.KnowledgeType == string(enums.KnowledgeBaseTypeFAQ) {
|
||
faqs := repositories.KnowledgeFAQRepository.Find(sqls.DB(), sqls.NewCnd().
|
||
Eq("knowledge_base_id", knowledgeBaseID).
|
||
Where("status != ?", enums.StatusDeleted))
|
||
if len(faqs) == 0 {
|
||
slog.Info("No faqs found in knowledge base, nothing to rebuild", "knowledge_base_id", knowledgeBaseID)
|
||
return nil
|
||
}
|
||
slog.Info("Rebuilding faq knowledge base index", "knowledge_base_id", knowledgeBaseID, "faq_count", len(faqs))
|
||
for _, faq := range faqs {
|
||
if err := s.IndexFAQByID(ctx, faq.ID); err != nil {
|
||
slog.Error("Failed to index faq", "faq_id", faq.ID, "error", err)
|
||
failedCount++
|
||
} else {
|
||
successCount++
|
||
}
|
||
}
|
||
} else {
|
||
documents := repositories.KnowledgeDocumentRepository.Find(sqls.DB(), sqls.NewCnd().
|
||
Eq("knowledge_base_id", knowledgeBaseID).
|
||
Where("status != ?", enums.StatusDeleted))
|
||
if len(documents) == 0 {
|
||
slog.Info("No documents found in knowledge base, nothing to rebuild", "knowledge_base_id", knowledgeBaseID)
|
||
return nil
|
||
}
|
||
|
||
documentIDs := make([]int64, 0, len(documents))
|
||
for _, doc := range documents {
|
||
documentIDs = append(documentIDs, doc.ID)
|
||
}
|
||
if err := s.markKnowledgeBaseDocumentsIndexPending(knowledgeBaseID, documentIDs); err != nil {
|
||
slog.Error("Failed to mark knowledge base documents index as pending", "knowledge_base_id", knowledgeBaseID, "error", err)
|
||
}
|
||
|
||
slog.Info("Rebuilding knowledge base index", "knowledge_base_id", knowledgeBaseID, "document_count", len(documents))
|
||
for _, doc := range documents {
|
||
if err := s.IndexDocumentByID(ctx, doc.ID); err != nil {
|
||
slog.Error("Failed to index document", "document_id", doc.ID, "error", err)
|
||
failedCount++
|
||
} else {
|
||
successCount++
|
||
}
|
||
}
|
||
}
|
||
|
||
slog.Info("Knowledge base index rebuild completed",
|
||
"knowledge_base_id", knowledgeBaseID,
|
||
"success_count", successCount,
|
||
"failed_count", failedCount)
|
||
|
||
return nil
|
||
}
|
||
|
||
func buildFAQChunkContent(faq *models.KnowledgeFAQ) string {
|
||
if faq == nil {
|
||
return ""
|
||
}
|
||
parts := []string{fmt.Sprintf("问题：%s", faq.Question)}
|
||
var similarQuestions []string
|
||
if faq.SimilarQuestions != "" {
|
||
_ = json.Unmarshal([]byte(faq.SimilarQuestions), &similarQuestions)
|
||
}
|
||
if len(similarQuestions) > 0 {
|
||
parts = append(parts, fmt.Sprintf("相似问：%s", joinSimilarQuestions(similarQuestions)))
|
||
}
|
||
parts = append(parts, fmt.Sprintf("回答：%s", faq.Answer))
|
||
content := ""
|
||
for _, part := range parts {
|
||
if part == "" {
|
||
continue
|
||
}
|
||
if content != "" {
|
||
content += "\n"
|
||
}
|
||
content += part
|
||
}
|
||
return content
|
||
}
|
||
|
||
func joinSimilarQuestions(items []string) string {
|
||
result := ""
|
||
for _, item := range items {
|
||
if item == "" {
|
||
continue
|
||
}
|
||
if result != "" {
|
||
result += "；"
|
||
}
|
||
result += item
|
||
}
|
||
return result
|
||
}
|
||
|
||
func (s *index) markDocumentIndexPending(documentID int64) error {
|
||
return repositories.KnowledgeDocumentRepository.Updates(sqls.DB(), documentID, map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusPending,
|
||
"indexed_at": nil,
|
||
"index_error": "",
|
||
"updated_at": time.Now(),
|
||
})
|
||
}
|
||
|
||
func (s *index) markDocumentIndexIndexed(documentID int64) error {
|
||
now := time.Now()
|
||
return repositories.KnowledgeDocumentRepository.Updates(sqls.DB(), documentID, map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusIndexed,
|
||
"indexed_at": &now,
|
||
"index_error": "",
|
||
"updated_at": now,
|
||
})
|
||
}
|
||
|
||
func (s *index) markDocumentIndexFailed(documentID int64, err error) error {
|
||
return repositories.KnowledgeDocumentRepository.Updates(sqls.DB(), documentID, map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusFailed,
|
||
"index_error": truncateIndexError(err),
|
||
"updated_at": time.Now(),
|
||
})
|
||
}
|
||
|
||
func (s *index) markKnowledgeBaseDocumentsIndexPending(knowledgeBaseID int64, documentIDs []int64) error {
|
||
if len(documentIDs) == 0 {
|
||
return nil
|
||
}
|
||
return sqls.DB().Model(&models.KnowledgeDocument{}).
|
||
Where("knowledge_base_id = ?", knowledgeBaseID).
|
||
Where("id IN ?", documentIDs).
|
||
Updates(map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusPending,
|
||
"indexed_at": nil,
|
||
"index_error": "",
|
||
"updated_at": time.Now(),
|
||
}).Error
|
||
}
|
||
|
||
func (s *index) markFAQIndexPending(faqID int64) error {
|
||
return repositories.KnowledgeFAQRepository.Updates(sqls.DB(), faqID, map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusPending,
|
||
"indexed_at": nil,
|
||
"index_error": "",
|
||
"updated_at": time.Now(),
|
||
})
|
||
}
|
||
|
||
func (s *index) markFAQIndexIndexed(faqID int64) error {
|
||
now := time.Now()
|
||
return repositories.KnowledgeFAQRepository.Updates(sqls.DB(), faqID, map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusIndexed,
|
||
"indexed_at": &now,
|
||
"index_error": "",
|
||
"updated_at": now,
|
||
})
|
||
}
|
||
|
||
func (s *index) markFAQIndexFailed(faqID int64, err error) error {
|
||
return repositories.KnowledgeFAQRepository.Updates(sqls.DB(), faqID, map[string]any{
|
||
"index_status": enums.KnowledgeDocumentIndexStatusFailed,
|
||
"index_error": truncateIndexError(err),
|
||
"updated_at": time.Now(),
|
||
})
|
||
}
|
||
|
||
func truncateIndexError(err error) string {
|
||
if err == nil {
|
||
return ""
|
||
}
|
||
message := err.Error()
|
||
if len(message) <= 1000 {
|
||
return message
|
||
}
|
||
return message[:1000]
|
||
}
|
||
|
||
func (s *index) resetKnowledgeBaseIndexStorage(ctx context.Context, knowledgeBaseID int64) error {
|
||
collectionName := s.getCollectionName()
|
||
provider := vectordb.GetProvider()
|
||
if provider == nil {
|
||
return fmt.Errorf("vectordb provider not initialized")
|
||
}
|
||
|
||
chunks := repositories.KnowledgeChunkRepository.Find(sqls.DB(), sqls.NewCnd().Eq("knowledge_base_id", knowledgeBaseID))
|
||
vectorIDs := make([]string, 0, len(chunks))
|
||
for _, chunk := range chunks {
|
||
if strs.IsNotBlank(chunk.VectorID) {
|
||
vectorIDs = append(vectorIDs, chunk.VectorID)
|
||
}
|
||
}
|
||
if len(vectorIDs) > 0 {
|
||
if err := provider.DeleteVectors(ctx, collectionName, vectorIDs); err != nil {
|
||
return fmt.Errorf("failed to delete vectors for knowledge base %d before rebuild: %w", knowledgeBaseID, err)
|
||
}
|
||
}
|
||
|
||
if err := sqls.WithTransaction(func(ctx *sqls.TxContext) error {
|
||
return ctx.Tx.Where("knowledge_base_id = ?", knowledgeBaseID).Delete(&models.KnowledgeChunk{}).Error
|
||
}); err != nil {
|
||
return fmt.Errorf("failed to clear chunks before rebuild: %w", err)
|
||
}
|
||
|
||
slog.Info("Knowledge base index storage reset",
|
||
"knowledge_base_id", knowledgeBaseID,
|
||
"collection", collectionName)
|
||
return nil
|
||
}
|