chore: import clean Agent Desk snapshot (2/7)

internal/ai/rag/index_faq_helpers.go

@@ -0,0 +1,61 @@
+package rag
+
+import (
+	"context"
+	"fmt"
+	"time"
+
+	"agent-desk/internal/ai"
+	"agent-desk/internal/ai/rag/vectordb"
+	"agent-desk/internal/models"
+	"agent-desk/internal/pkg/enums"
+)
+
+func buildFAQChunkModel(knowledgeBase models.KnowledgeBase, faq models.KnowledgeFAQ, content string) (models.KnowledgeChunk, string) {
+	chunkID := buildKnowledgeFAQChunkVectorID(knowledgeBase.ID, faq.ID, 0)
+	now := time.Now()
+	sectionPath := loadKnowledgeDirectoryPath(faq.DirectoryID)
+	return models.KnowledgeChunk{
+		KnowledgeBaseID: knowledgeBase.ID,
+		FaqID:           faq.ID,
+		ChunkNo:         0,
+		Title:           faq.Question,
+		Content:         content,
+		ContentHash:     buildChunkContentHash(content),
+		CharCount:       len([]rune(content)),
+		TokenCount:      len([]rune(content)) / 2,
+		ChunkType:       string(enums.KnowledgeChunkTypeFAQ),
+		SectionPath:     sectionPath,
+		Provider:        string(enums.KnowledgeChunkProviderFAQ),
+		VectorID:        chunkID,
+		Status:          enums.StatusOk,
+		CreatedAt:       now,
+		UpdatedAt:       now,
+	}, chunkID
+}
+
+func (s *index) prepareFAQVector(ctx context.Context, knowledgeBase models.KnowledgeBase, faq models.KnowledgeFAQ, content string) (vectordb.Vector, models.KnowledgeChunk, int, error) {
+	embeddingResult, err := ai.Embedding.GenerateEmbedding(ctx, content)
+	if err != nil {
+		return vectordb.Vector{}, models.KnowledgeChunk{}, 0, fmt.Errorf("failed to generate embedding for faq %d: %w", faq.ID, err)
+	}
+
+	chunkModel, chunkID := buildFAQChunkModel(knowledgeBase, faq, content)
+	sectionPath := chunkModel.SectionPath
+	vector := vectordb.Vector{
+		ID:     chunkID,
+		Vector: embeddingResult.Vector,
+		Payload: vectordb.ChunkPayload{
+			KnowledgeBaseID: knowledgeBase.ID,
+			FaqID:           faq.ID,
+			FaqQuestion:     faq.Question,
+			ChunkNo:         0,
+			ChunkType:       string(enums.KnowledgeChunkTypeFAQ),
+			SectionPath:     sectionPath,
+			Content:         content,
+			Title:           faq.Question,
+			Provider:        string(enums.KnowledgeChunkProviderFAQ),
+		},
+	}
+	return vector, chunkModel, embeddingResult.Dimension, nil
+}