package model

import (
	"encoding/json"

	"github.com/pgvector/pgvector-go"
)

// ArticleEmbedding stores vector embeddings for semantic article search.
// Reference: Chatwoot ArticleEmbedding (enterprise) + P2B M9 spec
type ArticleEmbedding struct {
	Base
	ArticleID       uint            `gorm:"not null;index" json:"article_id"`
	Embedding       json.RawMessage `gorm:"type:jsonb" json:"embedding"`    // original JSONB storage (backward compat)
	VectorEmbedding pgvector.Vector `gorm:"type:vector" json:"-"`           // dimension follows the configured embedding model
	Term            string          `gorm:"type:text;not null" json:"term"` // searchable text content

	Article Article `gorm:"foreignKey:ArticleID" json:"article,omitempty"`
}

func (ArticleEmbedding) TableName() string { return "article_embeddings" }