package ws

import (
	"encoding/json"
	"errors"
	"fmt"
	"github.com/tidwall/gjson"
	"kefu/lib"
	"kefu/models"
	"kefu/tools"
	"log"
)

// 反向提问
func GptRevQuestion(entId, visitorId string, kefuInfo models.User, question string) string {
	config := models.GetEntConfigsMap(entId, "chatGPTUrl", "chatGPTSecret", "chatGPTRevQuestion", "QdrantHistoryNum", "BigModelName")
	if config["chatGPTRevQuestion"] == "" || config["chatGPTUrl"] == "" || config["chatGPTSecret"] == "" {
		return ""
	}
	//大模型名称
	bigModelName := config["BigModelName"]
	if bigModelName == "" {
		bigModelName = "gpt-3.5-turbo-16k"
	}
	//是否传递历史记录
	gptMessages := make([]lib.Gpt3Dot5Message, 0)
	gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
		Role:    "system",
		Content: config["chatGPTRevQuestion"],
	})
	num := 5
	if config["QdrantHistoryNum"] != "" {
		num = tools.Str2Int(config["QdrantHistoryNum"])
	}

	messages := models.FindMessageByQueryPage(1, uint(num), "visitor_id = ?", visitorId)
	for i := len(messages) - 1; i >= 1; i-- {
		reqContent := messages[i].Content
		if messages[i].MesType == "visitor" {
			gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
				Role:    "user",
				Content: reqContent,
			})
		} else {
			gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
				Role:    "assistant",
				Content: reqContent,
			})
		}
	}
	gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
		Role:    "user",
		Content: config["chatGPTRevQuestion"] + question,
	})
	openaiUrl := config["chatGPTUrl"]
	openaiKey := config["chatGPTSecret"]
	gpt := lib.NewChatGptTool(openaiUrl, openaiKey)
	log.Println("反向提问调用GPT请求参数：", gptMessages)
	content, _ := gpt.ChatGPT3Dot5Turbo(gptMessages, bigModelName)
	if content == "" {
		return ""
	}
	VisitorMessage(visitorId, content, kefuInfo)
	go KefuMessage(visitorId, content, kefuInfo)
	models.CreateMessage(kefuInfo.Name, visitorId, content, "kefu", entId, "read")
	return content
}

// 调用GPT-3.5流式响应
func Gpt3Knowledge(entId, visitorId string, kefuInfo models.User, content string) string {
	config := models.GetEntConfigsMap(entId, "chatGPTUrl", "chatGPTSecret", "QdrantAIStatus", "QdrantAICollect", "chatGPTSystem", "chatGPTPrompt",
		"RobotName", "RobotAvator", "QdrantScore", "QdrantHistoryNum",
		"BaiduCheckApiKey", "BaiduCheckSecretKey", "BaiduCheckDisableStream",
		"BigModelName", "EmbeddingModelName", "SearchEmptyInterrupt",
		"chatGPTHistory")
	lib.QdrantBase = models.FindConfig("QdrantBase")
	lib.QdrantPort = models.FindConfig("QdrantPort")
	_, ok := ClientList[visitorId]
	messageContent := ""
	//没有开启AI回复
	if config["QdrantAIStatus"] != "true" || config["chatGPTUrl"] == "" || config["chatGPTSecret"] == "" {
		return ""
	}
	//大模型名称
	bigModelName := config["BigModelName"]
	if bigModelName == "" {
		bigModelName = "gpt-3.5-turbo-16k"
	}

	//调用百度文本审核用户问题
	isBaiduCheck := false
	if config["BaiduCheckApiKey"] != "" && config["BaiduCheckSecretKey"] != "" {
		isBaiduCheck = true
		b := lib.BaiduCheck{
			API_KEY:    config["BaiduCheckApiKey"],
			SECRET_KEY: config["BaiduCheckSecretKey"],
		}
		r, err := b.Check(content)
		log.Println("百度审核结果:", content, r)
		ok := gjson.Get(r, "conclusion").String()
		if err == nil && ok != "合规" {
			messageContent = "对不起，内容包含敏感信息！"
			VisitorMessage(visitorId, messageContent, kefuInfo)
			return messageContent
		}
	}

	kefuInfo.Nickname = tools.Ifelse(config["RobotName"] != "", config["RobotName"], kefuInfo.Nickname).(string)
	kefuInfo.Avator = tools.Ifelse(config["RobotAvator"] != "", config["RobotAvator"], kefuInfo.Avator).(string)
	openaiUrl := config["chatGPTUrl"]
	openaiKey := config["chatGPTSecret"]
	collectName := config["QdrantAICollect"]
	embeddingModelName := config["EmbeddingModelName"]
	promptMessage := config["chatGPTPrompt"]
	if promptMessage != "" {
		content = promptMessage + content
	}

	system := ""
	if config["chatGPTSystem"] != "" {
		system = config["chatGPTSystem"]
	}
	system = fmt.Sprintf("%s\n当前时间：%s", system, tools.GetNowTime())

	//是否传递历史记录
	gptMessages := make([]lib.Gpt3Dot5Message, 0)
	history := make([]lib.CozeCompletionMessage, 0)
	num := 5
	if config["QdrantHistoryNum"] != "" {
		num = tools.Str2Int(config["QdrantHistoryNum"])
	}
	messages := models.FindMessageByQueryPage(1, uint(num), "visitor_id = ?", visitorId)
	for i := len(messages) - 1; i >= 1; i-- {
		reqContent := messages[i].Content
		if messages[i].MesType == "visitor" {
			gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
				Role:    "user",
				Content: reqContent,
			})
			//扣子智能体
			if bigModelName == "coze" {
				history = append(history, lib.CozeCompletionMessage{
					Role:        "user",
					Content:     reqContent,
					ContentType: "text",
				})
			}
		} else {
			gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
				Role:    "assistant",
				Content: reqContent,
			})
			//扣子智能体
			if bigModelName == "coze" {
				history = append(history, lib.CozeCompletionMessage{
					Role:        "assistant",
					Content:     reqContent,
					ContentType: "text",
					Type:        "answer",
				})
			}
		}
	}
	//扣子智能体
	if bigModelName == "coze" {
		coze := &lib.Coze{
			BOT_ID:  config["chatGPTUrl"],
			API_KEY: config["chatGPTSecret"],
		}
		messageContent, _ := coze.ChatCoze(visitorId, visitorId, content, history)
		if ok && messageContent != "" {
			VisitorMessage(visitorId, messageContent, kefuInfo)
			go KefuMessage(visitorId, messageContent, kefuInfo)
			models.CreateMessage(kefuInfo.Name, visitorId, messageContent, "kefu", entId, "read")
		}
		return messageContent
	}
	gpt := lib.NewChatGptTool(openaiUrl, openaiKey)
	score := 0.78

	if config["QdrantScore"] != "" {
		score = tools.Str2Float64(config["QdrantScore"])
	}
	searchEmptyInterrupt := false
	if config["SearchEmptyInterrupt"] == "true" {
		searchEmptyInterrupt = true
	}
	message, err, _ := GptEmbedding(openaiUrl, openaiKey, embeddingModelName, collectName, content, system, score, searchEmptyInterrupt, gptMessages)
	if err != nil {
		log.Println(err)
		return ""
	}
	log.Println("调用GPT请求参数：", message)
	stream, err := gpt.ChatGPTHttpStream(message, bigModelName, visitorId)
	if err != nil {
		log.Println(err)
		return ""
	}
	msgId := tools.Now()
	for {
		response, err := stream.Recv()
		if err != nil {
			log.Println("调用GPT响应流：", err)
			break
		}
		if len(response.Choices) < 1 {
			continue
		}

		data := response.Choices[0].Delta.Content
		if data == "" {
			continue
		}
		log.Println(data)
		messageContent += data
		tempContent := messageContent
		//百度文本审核时屏蔽流式输出
		if isBaiduCheck && config["BaiduCheckDisableStream"] != "" {
			tempContent = "正在回复内容..."
		}
		VisitorMessageSameMsgId(uint(msgId), visitorId, tempContent, kefuInfo)
	}
	stream.Close()
	//AI反向提问
	//go GptRevQuestion(entId, visitorId, kefuInfo, content)
	//记录日志
	go llmLog(entId, kefuInfo.Name, bigModelName, messageContent, message)
	if ok && messageContent != "" {

		//调用百度文本审核AI回复结果
		if config["BaiduCheckApiKey"] != "" && config["BaiduCheckSecretKey"] != "" {
			b := lib.BaiduCheck{
				API_KEY:    config["BaiduCheckApiKey"],
				SECRET_KEY: config["BaiduCheckSecretKey"],
			}
			r, err := b.Check(messageContent)
			log.Println("百度审核结果:", messageContent, r)
			ok := gjson.Get(r, "conclusion").String()
			if err == nil && ok != "合规" {
				messageContent = "对不起，AI回复内容包含敏感信息！"
			}
			VisitorMessageSameMsgId(uint(msgId), visitorId, messageContent, kefuInfo)
		}

		go KefuMessage(visitorId, messageContent, kefuInfo)
		models.CreateMessage(kefuInfo.Name, visitorId, messageContent, "kefu", entId, "read")
		return messageContent
	}
	return messageContent
}

// 调用GPT embedding接口并查询qdrant集合数据
func GptEmbedding(openaiUrl, openaiKey, embeddingModelName, collectionName, keywords, system string, score float64, searchEmptyInterrupt bool, history []lib.Gpt3Dot5Message) ([]lib.Gpt3Dot5Message, error, []string) {
	message := make([]lib.Gpt3Dot5Message, 0)
	urls := make([]string, 0)
	if collectionName == "" {
		message = append(message, lib.Gpt3Dot5Message{
			Role:    "system",
			Content: system,
		})
		message = append(message, history...)
		message = append(message, lib.Gpt3Dot5Message{
			Role:    "user",
			Content: keywords,
		})
		return message, nil, urls
	}
	gpt := lib.NewChatGptTool(openaiUrl, openaiKey)
	//调用GPT text-embedding-ada-002
	if embeddingModelName == "" {
		embeddingModelName = "text-embedding-ada-002"
	}
	response, err := gpt.GetEmbedding(keywords, embeddingModelName)
	if err != nil {
		return nil, err, urls
	}
	var embeddingResponse lib.EmbeddingResponse
	json.Unmarshal([]byte(response), &embeddingResponse)

	params := map[string]interface{}{"exact": false, "hnsw_ef": 128}
	if len(embeddingResponse.Data) == 0 {
		return nil, errors.New(response), urls
	}
	vector := embeddingResponse.Data[0].Embedding
	limit := 10
	points, err := lib.SearchPoints(collectionName, params, vector, limit, score)
	if err != nil {
		log.Println(err)
	}
	log.Println("qdrant相似搜索结果：", string(points))
	result := gjson.Get(string(points), "result").Array()
	if len(result) == 0 && searchEmptyInterrupt {
		return message, errors.New("搜索知识库为空，中断AI回复"), urls
	}
	content := ""
	line := ""

	for key, row := range result {
		key++
		line = fmt.Sprintf("%d. %s\n", key, row.Get("payload.text").String())
		url := row.Get("payload.url").String()
		if url != "" && key <= 3 {
			urls = append(urls, url)
		}
		//arr := []rune(line)
		//if len(arr) > 500 {
		//	line = string(arr[:500])
		//}
		content += line
	}
	//if content == "" {
	//	content = "我是知识库AI机器人\n"
	//}
	message = append(message, lib.Gpt3Dot5Message{
		Role:    "system",
		Content: fmt.Sprintf("%s\n%s\n", system, content),
	})

	message = append(message, history...)
	//userQuestion := fmt.Sprintf("%s%s", promptMessage, keywords)
	//userQuestion := keywords
	message = append(message, lib.Gpt3Dot5Message{
		Role:    "user",
		Content: keywords,
	})
	urls = tools.RemoveDuplicateStrings(urls)
	return message, nil, urls
}

// 记录LLM日志
func llmLog(entId, kefuName, modelName, answer string, questions []lib.Gpt3Dot5Message) {
	q := ""
	for _, question := range questions {
		q += question.Role + ":" + question.Content + "\n"
	}
	llm := models.LlmLog{
		EntID:     entId,
		KefuName:  kefuName,
		ModelName: modelName,
		Question:  q,
		Answer:    answer,
	}
	llm.AddLlmLog()
}