package service

import (
	"context"
	"fmt"
	"rag/common/eino"
	"rag/consts/task"
	"rag/dao"
	"rag/model/dto"
	"rag/model/entity"

	"gitea.com/red-future/common/beans"
	"github.com/cloudwego/eino/components/indexer"
	"github.com/cloudwego/eino/components/retriever"
	"github.com/cloudwego/eino/schema"
	"github.com/gogf/gf/v2/frame/g"
	"github.com/gogf/gf/v2/util/gconv"
)

var DocumentVector = new(documentVectorService)

type documentVectorService struct{}

// Query 执行RAG查询
func (s *documentVectorService) Query(ctx context.Context, req *dto.RAGQueryReq) (*dto.RAGQueryRes, error) {
	if req.TopK <= 0 {
		req.TopK = 5
	}

	// 4. 使用向量检索器进行查询
	r, err := eino.NewPGVectorRetriever(&eino.PGVectorRetrieverConfig{
		Embedder:    eino.EmbedderDashscope,
		DefaultTopK: req.TopK,
	})
	if err != nil {
		g.Log().Errorf(ctx, "初始化向量检索器失败: %v", err)
		return nil, fmt.Errorf("初始化向量检索器失败: %w", err)
	}

	// 5. 执行向量检索
	docs, err := r.Retrieve(ctx, req.Content, retriever.WithEmbedding(eino.EmbedderDashscope), retriever.WithDSLInfo(map[string]any{
		"dataset_ids": req.DatasetIds,
	}))
	if err != nil {
		g.Log().Errorf(ctx, "向量检索失败: %v", err)
		return nil, fmt.Errorf("向量检索失败: %w", err)
	}

	messages := make([]*schema.Message, 0)
	err = gconv.Struct(req.History, &messages)
	if err != nil {
		g.Log().Errorf(ctx, "转换历史消息失败: %v", err)
		return nil, fmt.Errorf("转换历史消息失败: %w", err)
	}

	replyMsg, err := eino.NewChatModel(ctx, req.Content, docs, messages)
	if err != nil {
		g.Log().Errorf(ctx, "向量检索失败: %v", err)
		return nil, fmt.Errorf("向量检索失败: %w", err)
	}

	return &dto.RAGQueryRes{
		Answer: replyMsg.Content,
	}, nil
}

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

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

func (s *documentVectorService) DocsChunkMsg(ctx context.Context, msg any) (err error) {
	var docs = make([]*schema.Document, 0)
	msgMap := gconv.Map(msg)
	if err = gconv.Structs(msgMap["data"], &docs); err != nil {
		g.Log().Error(ctx, "DocsChunkMsg err:", err)
		return
	}
	if len(docs) == 0 {
		g.Log().Error(ctx, "DocsChunkMsg err:", "msg is empty")
		return
	}
	ctx = context.WithValue(ctx, "user", &beans.User{
		TenantId: gconv.Uint64(docs[0].MetaData[entity.DocumentVectorCol.TenantId]),
		UserName: gconv.String(docs[0].MetaData[entity.DocumentVectorCol.Creator]),
	})
	idx := eino.NewPGVectorIndexer(&eino.PGVectorIndexerOptions{
		BatchSize: 10,
	})
	documentId := gconv.Int64(docs[0].MetaData[entity.DocumentVectorCol.DocumentId])
	rows, err := idx.Store(ctx, docs, indexer.WithEmbedding(eino.EmbedderDashscope))
	if err != nil || rows == 0 {
		g.Log().Error(ctx, "DocsChunkMsg rows: , err:", rows, err)
		// 写入任务进度失败 任务类型为sql存储
		remark := " 向量存储数量: " + gconv.String(rows)
		if err != nil {
			remark = "向量存储失败: " + err.Error()
		}
		err = Task.WriteTaskProgress(ctx, &dto.WriteTaskProgressReq{
			TaskId:   documentId,
			TaskType: task.TaskTypeGenerateVector,
			Status:   task.TaskStatusFailed,
			Remark:   remark,
		})
		return
	}
	// 写入任务进度成功 任务类型为sql存储
	err = Task.WriteTaskProgress(ctx, &dto.WriteTaskProgressReq{
		TaskId:   documentId,
		TaskType: task.TaskTypeGenerateVector,
		Status:   task.TaskStatusCompleted,
		Remark:   "向量生成完成",
	})
	return
}