package service

import (
	"context"
	"database/sql"
	"errors"
	"fmt"
	"rag/consts/document"
	"rag/consts/public"
	"rag/dao"
	"rag/model/dto"
	"rag/model/entity"

	"gitea.com/red-future/common/beans"
	"gitea.com/red-future/common/rag/eino"
	gmq "github.com/bjang03/gmq/core/gmq"
	"github.com/bjang03/gmq/mq"
	"github.com/bjang03/gmq/types"
	"github.com/gogf/gf/v2/frame/g"
	"github.com/gogf/gf/v2/util/gconv"
	"github.com/pgvector/pgvector-go"
)

var DocumentChunk = new(documentChunkService)

type documentChunkService struct{}

const (
	DatasetIndexStatusReady = "ready"
)

// Update 更新文件块
func (s *documentChunkService) Update(ctx context.Context, req *dto.UpdateDocumentChunkReq) (err error) {
	_, err = dao.DocumentChunk.Update(ctx, req)
	return
}

// List 获取文件块列表
func (s *documentChunkService) List(ctx context.Context, req *dto.ListDocumentChunkReq) (res *dto.ListDocumentChunkRes, err error) {
	list, total, err := dao.DocumentChunk.List(ctx, req)
	if err != nil {
		return
	}
	res = &dto.ListDocumentChunkRes{
		Total: total,
	}
	err = gconv.Struct(list, &res.List)
	return
}

func (s *documentChunkService) DocsChunkMsg(ctx context.Context, msg any) (err error) {
	var req = make([]*dto.VectorDocumentChunkMsg, 0)
	msgMap := gconv.Map(msg)
	if err = gconv.Structs(msgMap["data"], &req); err != nil {
		g.Log().Error(ctx, "DocsChunkMsg err:", err)
		return
	}
	if len(req) == 0 {
		g.Log().Error(ctx, "DocsChunkMsg err:", "msg is empty")
		return
	}

	ctx = context.WithValue(ctx, "user", &beans.User{
		TenantId: req[0].TenantId,
		UserName: req[0].Creator,
	})

	// 调用eino接口获取向量
	var vectorDocsStr = make([]string, 0, len(req))
	for _, t := range req {
		vectorDocsStr = append(vectorDocsStr, t.Content)
	}
	embeddings, err := eino.EmbedStrings(ctx, vectorDocsStr)
	if err != nil {
		g.Log().Error(ctx, "DocsChunkMsg err:", err)
		err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusFailed.Code())
		return
	}

	// 获取向量维度
	dimension := 0
	if len(embeddings) > 0 {
		dimension = len(embeddings[0])
	}

	// 创建或更新DatasetIndex
	err = s.createOrUpdateDatasetIndex(ctx, req[0].DatasetId, dimension, int64(len(req)))
	if err != nil {
		g.Log().Error(ctx, "CreateOrUpdateDatasetIndex err:", err)
		err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusFailed.Code())
		return
	}

	// 更新向量文档
	for i, embedding := range embeddings {
		req[i].Vector = pgvector.NewVector(gconv.Float32s(embedding))
		req[i].VectorStatus = document.VectorStatusCompleted.Code()
		req[i].Status = document.StatusEnable.Code()
	}
	_, err = dao.DocumentChunk.BatchInsert(ctx, req)
	if err != nil {
		g.Log().Error(ctx, "DocsChunkMsg err:", err)
		err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusFailed.Code())
		return
	}

	err = s.publishKnowledgeDocumentMsg(ctx, req[0].TenantId, req[0].Creator, req[0].DocumentId, document.VectorStatusCompleted.Code())

	return
}

// createOrUpdateDatasetIndex 创建或更新数据集索引
func (s *documentChunkService) createOrUpdateDatasetIndex(ctx context.Context, datasetId int64, dimension int, vectorCount int64) (err error) {
	// 查询数据集是否已有索引
	existIndex, err := dao.DatasetIndex.GetByDatasetId(ctx, datasetId)
	if err != nil && !errors.Is(err, sql.ErrNoRows) {
		return err
	}

	// 已有索引 → 只更新数量
	if existIndex != nil {
		_ = dao.DatasetIndex.IncVectorCount(ctx, existIndex.Id, vectorCount)
		return nil
	}

	// ====================== 创建新索引 ======================
	indexName := fmt.Sprintf("idx_dataset_%d_vector", datasetId) // 真实PG索引名
	// 1. 插入索引配置
	index := &entity.DatasetIndex{
		DatasetId:   datasetId,
		Name:        indexName,
		Dimension:   dimension,
		FieldType:   "float",
		MetricType:  "COSINE",
		Status:      gconv.PtrInt8(1),
		VectorCount: vectorCount,
		Description: fmt.Sprintf("数据集%d向量索引", datasetId),
	}
	_, err = dao.DatasetIndex.Insert(ctx, index)
	if err != nil {
		return err
	}

	// 2. 真正创建 PGVector 索引（唯一真实索引！）
	err = s.createRealPGVectorIndex(ctx, indexName)
	return err
}

// createRealPGVectorIndex 真正在PostgreSQL创建向量索引（真实可用）
func (s *documentChunkService) createRealPGVectorIndex(ctx context.Context, indexName string) error {
	// 执行真实建索引语句
	err := dao.DatasetIndex.InsertIndex(ctx, indexName)
	if err != nil {
		g.Log().Error(ctx, "创建向量索引失败：", err)
		return err
	}
	g.Log().Info(ctx, "PGVector真实索引创建成功："+indexName)
	return nil
}

// publishKnowledgeDocumentMsg 发布消息
func (s *documentChunkService) publishKnowledgeDocumentMsg(ctx context.Context, tenantId uint64, creator string, documentId int64, vectorStatus document.VectorStatus) (err error) {
	knowledgeDocumentMsg := dto.KnowledgeDocumentMsg{
		TenantId:     tenantId,
		Creator:      creator,
		Id:           documentId,
		VectorStatus: vectorStatus,
	}
	err = gmq.GetGmq("primary").GmqPublish(ctx, &mq.RedisPubMessage{
		PubMessage: types.PubMessage{
			Topic: public.KnowledgeDocumentVectorStatusTopic,
			Data:  knowledgeDocumentMsg,
		},
	})
	return
}