WeKnora deployment source with the reviewed wiki identity, summary and recovery fixes

internal/application/repository/retriever/elasticsearch/structs.go

@@ -0,0 +1,78 @@
+package elasticsearch
+
+import (
+	"maps"
+	"slices"
+
+	"github.com/Tencent/WeKnora/internal/types"
+)
+
+// VectorEmbedding defines the Elasticsearch document structure for vector embeddings
+type VectorEmbedding struct {
+	Content         string    `json:"content"           gorm:"column:content;not null"`     // Text content of the chunk
+	SourceID        string    `json:"source_id"         gorm:"column:source_id;not null"`   // ID of the source document
+	SourceType      int       `json:"source_type"       gorm:"column:source_type;not null"` // Type of the source document
+	ChunkID         string    `json:"chunk_id"          gorm:"column:chunk_id"`             // Unique ID of the text chunk
+	KnowledgeID     string    `json:"knowledge_id"      gorm:"column:knowledge_id"`         // ID of the knowledge item
+	KnowledgeBaseID string    `json:"knowledge_base_id" gorm:"column:knowledge_base_id"`    // ID of the knowledge base
+	TagID           string    `json:"tag_id"            gorm:"column:tag_id"`               // Tag ID for categorization
+	Embedding       []float32 `json:"embedding"         gorm:"column:embedding;not null"`   // Vector embedding of the content
+	IsEnabled       bool      `json:"is_enabled"`                                           // Whether the chunk is enabled
+	IsRecommended   bool      `json:"is_recommended"`                                       // Whether the chunk is recommended
+}
+
+// VectorEmbeddingWithScore extends VectorEmbedding with similarity score
+type VectorEmbeddingWithScore struct {
+	VectorEmbedding
+	Score float64 // Similarity score from vector search
+}
+
+// ToDBVectorEmbedding converts IndexInfo to Elasticsearch document format
+func ToDBVectorEmbedding(embedding *types.IndexInfo, additionalParams map[string]interface{}) *VectorEmbedding {
+	vector := &VectorEmbedding{
+		Content:         embedding.Content,
+		SourceID:        embedding.SourceID,
+		SourceType:      int(embedding.SourceType),
+		ChunkID:         embedding.ChunkID,
+		KnowledgeID:     embedding.KnowledgeID,
+		KnowledgeBaseID: embedding.KnowledgeBaseID,
+		TagID:           embedding.TagID,
+		IsEnabled:       embedding.IsEnabled,
+		IsRecommended:   embedding.IsRecommended,
+	}
+	// Add embedding data if available in additionalParams
+	if additionalParams != nil && slices.Contains(slices.Collect(maps.Keys(additionalParams)), "embedding") {
+		if embeddingMap, ok := additionalParams["embedding"].(map[string][]float32); ok {
+			vector.Embedding = embeddingMap[embedding.SourceID]
+		}
+	}
+	// Get is_enabled from additionalParams if available
+	if additionalParams != nil {
+		if chunkEnabledMap, ok := additionalParams["chunk_enabled"].(map[string]bool); ok {
+			if enabled, exists := chunkEnabledMap[embedding.ChunkID]; exists {
+				vector.IsEnabled = enabled
+			}
+		}
+	}
+	return vector
+}
+
+// FromDBVectorEmbeddingWithScore converts Elasticsearch document to IndexWithScore domain model
+func FromDBVectorEmbeddingWithScore(id string,
+	embedding *VectorEmbeddingWithScore,
+	matchType types.MatchType,
+) *types.IndexWithScore {
+	return &types.IndexWithScore{
+		ID:              id,
+		SourceID:        embedding.SourceID,
+		SourceType:      types.SourceType(embedding.SourceType),
+		ChunkID:         embedding.ChunkID,
+		KnowledgeID:     embedding.KnowledgeID,
+		KnowledgeBaseID: embedding.KnowledgeBaseID,
+		TagID:           embedding.TagID,
+		Content:         embedding.Content,
+		Score:           embedding.Score,
+		MatchType:       matchType,
+		IsEnabled:       embedding.IsEnabled,
+	}
+}