345 lines
10 KiB
Go
345 lines
10 KiB
Go
package ws
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import (
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"encoding/json"
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"errors"
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"fmt"
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"github.com/tidwall/gjson"
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"kefu/lib"
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"kefu/models"
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"kefu/tools"
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"log"
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)
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// 反向提问
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func GptRevQuestion(entId, visitorId string, kefuInfo models.User, question string) string {
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config := models.GetEntConfigsMap(entId, "chatGPTUrl", "chatGPTSecret", "chatGPTRevQuestion", "QdrantHistoryNum", "BigModelName")
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if config["chatGPTRevQuestion"] == "" || config["chatGPTUrl"] == "" || config["chatGPTSecret"] == "" {
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return ""
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}
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//大模型名称
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bigModelName := config["BigModelName"]
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if bigModelName == "" {
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bigModelName = "gpt-3.5-turbo-16k"
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}
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//是否传递历史记录
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gptMessages := make([]lib.Gpt3Dot5Message, 0)
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gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
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Role: "system",
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Content: config["chatGPTRevQuestion"],
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})
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num := 5
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if config["QdrantHistoryNum"] != "" {
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num = tools.Str2Int(config["QdrantHistoryNum"])
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}
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messages := models.FindMessageByQueryPage(1, uint(num), "visitor_id = ?", visitorId)
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for i := len(messages) - 1; i >= 1; i-- {
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reqContent := messages[i].Content
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if messages[i].MesType == "visitor" {
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gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
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Role: "user",
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Content: reqContent,
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})
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} else {
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gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
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Role: "assistant",
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Content: reqContent,
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})
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}
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}
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gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
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Role: "user",
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Content: config["chatGPTRevQuestion"] + question,
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})
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openaiUrl := config["chatGPTUrl"]
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openaiKey := config["chatGPTSecret"]
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gpt := lib.NewChatGptTool(openaiUrl, openaiKey)
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log.Println("反向提问调用GPT请求参数：", gptMessages)
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content, _ := gpt.ChatGPT3Dot5Turbo(gptMessages, bigModelName)
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if content == "" {
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return ""
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}
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VisitorMessage(visitorId, content, kefuInfo)
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go KefuMessage(visitorId, content, kefuInfo)
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models.CreateMessage(kefuInfo.Name, visitorId, content, "kefu", entId, "read")
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return content
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}
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// 调用GPT-3.5流式响应
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func Gpt3Knowledge(entId, visitorId string, kefuInfo models.User, content string) string {
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config := models.GetEntConfigsMap(entId, "chatGPTUrl", "chatGPTSecret", "QdrantAIStatus", "QdrantAICollect", "chatGPTSystem", "chatGPTPrompt",
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"RobotName", "RobotAvator", "QdrantScore", "QdrantHistoryNum",
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"BaiduCheckApiKey", "BaiduCheckSecretKey", "BaiduCheckDisableStream",
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"BigModelName", "EmbeddingModelName", "SearchEmptyInterrupt",
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"chatGPTHistory")
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lib.QdrantBase = models.FindConfig("QdrantBase")
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lib.QdrantPort = models.FindConfig("QdrantPort")
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_, ok := ClientList[visitorId]
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messageContent := ""
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//没有开启AI回复
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if config["QdrantAIStatus"] != "true" || config["chatGPTUrl"] == "" || config["chatGPTSecret"] == "" {
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return ""
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}
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//大模型名称
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bigModelName := config["BigModelName"]
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if bigModelName == "" {
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bigModelName = "gpt-3.5-turbo-16k"
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}
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//调用百度文本审核用户问题
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isBaiduCheck := false
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if config["BaiduCheckApiKey"] != "" && config["BaiduCheckSecretKey"] != "" {
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isBaiduCheck = true
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b := lib.BaiduCheck{
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API_KEY: config["BaiduCheckApiKey"],
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SECRET_KEY: config["BaiduCheckSecretKey"],
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}
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r, err := b.Check(content)
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log.Println("百度审核结果:", content, r)
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ok := gjson.Get(r, "conclusion").String()
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if err == nil && ok != "合规" {
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messageContent = "对不起，内容包含敏感信息！"
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VisitorMessage(visitorId, messageContent, kefuInfo)
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return messageContent
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}
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}
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kefuInfo.Nickname = tools.Ifelse(config["RobotName"] != "", config["RobotName"], kefuInfo.Nickname).(string)
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kefuInfo.Avator = tools.Ifelse(config["RobotAvator"] != "", config["RobotAvator"], kefuInfo.Avator).(string)
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openaiUrl := config["chatGPTUrl"]
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openaiKey := config["chatGPTSecret"]
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collectName := config["QdrantAICollect"]
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embeddingModelName := config["EmbeddingModelName"]
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promptMessage := config["chatGPTPrompt"]
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if promptMessage != "" {
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content = promptMessage + content
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}
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system := ""
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if config["chatGPTSystem"] != "" {
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system = config["chatGPTSystem"]
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}
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system = fmt.Sprintf("%s\n当前时间：%s", system, tools.GetNowTime())
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//是否传递历史记录
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gptMessages := make([]lib.Gpt3Dot5Message, 0)
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history := make([]lib.CozeCompletionMessage, 0)
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num := 5
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if config["QdrantHistoryNum"] != "" {
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num = tools.Str2Int(config["QdrantHistoryNum"])
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}
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messages := models.FindMessageByQueryPage(1, uint(num), "visitor_id = ?", visitorId)
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for i := len(messages) - 1; i >= 1; i-- {
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reqContent := messages[i].Content
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if messages[i].MesType == "visitor" {
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gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
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Role: "user",
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Content: reqContent,
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})
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//扣子智能体
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if bigModelName == "coze" {
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history = append(history, lib.CozeCompletionMessage{
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Role: "user",
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Content: reqContent,
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ContentType: "text",
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})
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}
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} else {
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gptMessages = append(gptMessages, lib.Gpt3Dot5Message{
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Role: "assistant",
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Content: reqContent,
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})
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//扣子智能体
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if bigModelName == "coze" {
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history = append(history, lib.CozeCompletionMessage{
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Role: "assistant",
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Content: reqContent,
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ContentType: "text",
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Type: "answer",
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})
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}
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}
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}
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//扣子智能体
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if bigModelName == "coze" {
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coze := &lib.Coze{
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BOT_ID: config["chatGPTUrl"],
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API_KEY: config["chatGPTSecret"],
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}
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messageContent, _ := coze.ChatCoze(visitorId, visitorId, content, history)
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if ok && messageContent != "" {
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VisitorMessage(visitorId, messageContent, kefuInfo)
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go KefuMessage(visitorId, messageContent, kefuInfo)
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models.CreateMessage(kefuInfo.Name, visitorId, messageContent, "kefu", entId, "read")
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}
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return messageContent
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}
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gpt := lib.NewChatGptTool(openaiUrl, openaiKey)
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score := 0.78
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if config["QdrantScore"] != "" {
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score = tools.Str2Float64(config["QdrantScore"])
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}
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searchEmptyInterrupt := false
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if config["SearchEmptyInterrupt"] == "true" {
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searchEmptyInterrupt = true
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}
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message, err, _ := GptEmbedding(openaiUrl, openaiKey, embeddingModelName, collectName, content, system, score, searchEmptyInterrupt, gptMessages)
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if err != nil {
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log.Println(err)
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return ""
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}
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log.Println("调用GPT请求参数：", message)
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stream, err := gpt.ChatGPTHttpStream(message, bigModelName, visitorId)
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if err != nil {
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log.Println(err)
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return ""
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}
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msgId := tools.Now()
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for {
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response, err := stream.Recv()
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if err != nil {
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log.Println("调用GPT响应流：", err)
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break
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}
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if len(response.Choices) < 1 {
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continue
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}
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data := response.Choices[0].Delta.Content
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if data == "" {
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continue
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}
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log.Println(data)
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messageContent += data
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tempContent := messageContent
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//百度文本审核时屏蔽流式输出
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if isBaiduCheck && config["BaiduCheckDisableStream"] != "" {
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tempContent = "正在回复内容..."
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}
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VisitorMessageSameMsgId(uint(msgId), visitorId, tempContent, kefuInfo)
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}
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stream.Close()
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//AI反向提问
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//go GptRevQuestion(entId, visitorId, kefuInfo, content)
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//记录日志
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go llmLog(entId, kefuInfo.Name, bigModelName, messageContent, message)
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if ok && messageContent != "" {
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//调用百度文本审核AI回复结果
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if config["BaiduCheckApiKey"] != "" && config["BaiduCheckSecretKey"] != "" {
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b := lib.BaiduCheck{
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API_KEY: config["BaiduCheckApiKey"],
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SECRET_KEY: config["BaiduCheckSecretKey"],
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}
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r, err := b.Check(messageContent)
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log.Println("百度审核结果:", messageContent, r)
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ok := gjson.Get(r, "conclusion").String()
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if err == nil && ok != "合规" {
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messageContent = "对不起，AI回复内容包含敏感信息！"
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}
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VisitorMessageSameMsgId(uint(msgId), visitorId, messageContent, kefuInfo)
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}
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go KefuMessage(visitorId, messageContent, kefuInfo)
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models.CreateMessage(kefuInfo.Name, visitorId, messageContent, "kefu", entId, "read")
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return messageContent
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}
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return messageContent
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}
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// 调用GPT embedding接口并查询qdrant集合数据
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func GptEmbedding(openaiUrl, openaiKey, embeddingModelName, collectionName, keywords, system string, score float64, searchEmptyInterrupt bool, history []lib.Gpt3Dot5Message) ([]lib.Gpt3Dot5Message, error, []string) {
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message := make([]lib.Gpt3Dot5Message, 0)
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urls := make([]string, 0)
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if collectionName == "" {
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message = append(message, lib.Gpt3Dot5Message{
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Role: "system",
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Content: system,
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})
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message = append(message, history...)
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message = append(message, lib.Gpt3Dot5Message{
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Role: "user",
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Content: keywords,
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})
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return message, nil, urls
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}
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gpt := lib.NewChatGptTool(openaiUrl, openaiKey)
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//调用GPT text-embedding-ada-002
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if embeddingModelName == "" {
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embeddingModelName = "text-embedding-ada-002"
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}
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response, err := gpt.GetEmbedding(keywords, embeddingModelName)
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if err != nil {
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return nil, err, urls
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}
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var embeddingResponse lib.EmbeddingResponse
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json.Unmarshal([]byte(response), &embeddingResponse)
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params := map[string]interface{}{"exact": false, "hnsw_ef": 128}
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if len(embeddingResponse.Data) == 0 {
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return nil, errors.New(response), urls
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}
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vector := embeddingResponse.Data[0].Embedding
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limit := 10
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points, err := lib.SearchPoints(collectionName, params, vector, limit, score)
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if err != nil {
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log.Println(err)
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}
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log.Println("qdrant相似搜索结果：", string(points))
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result := gjson.Get(string(points), "result").Array()
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if len(result) == 0 && searchEmptyInterrupt {
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return message, errors.New("搜索知识库为空，中断AI回复"), urls
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}
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content := ""
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line := ""
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for key, row := range result {
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key++
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line = fmt.Sprintf("%d. %s\n", key, row.Get("payload.text").String())
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url := row.Get("payload.url").String()
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if url != "" && key <= 3 {
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urls = append(urls, url)
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}
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//arr := []rune(line)
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//if len(arr) > 500 {
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// line = string(arr[:500])
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//}
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content += line
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}
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//if content == "" {
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// content = "我是知识库AI机器人\n"
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//}
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message = append(message, lib.Gpt3Dot5Message{
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Role: "system",
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Content: fmt.Sprintf("%s\n%s\n", system, content),
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})
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message = append(message, history...)
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//userQuestion := fmt.Sprintf("%s%s", promptMessage, keywords)
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//userQuestion := keywords
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message = append(message, lib.Gpt3Dot5Message{
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Role: "user",
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Content: keywords,
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})
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urls = tools.RemoveDuplicateStrings(urls)
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return message, nil, urls
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}
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// 记录LLM日志
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func llmLog(entId, kefuName, modelName, answer string, questions []lib.Gpt3Dot5Message) {
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q := ""
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for _, question := range questions {
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q += question.Role + ":" + question.Content + "\n"
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}
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llm := models.LlmLog{
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EntID: entId,
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KefuName: kefuName,
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ModelName: modelName,
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Question: q,
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Answer: answer,
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}
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llm.AddLlmLog()
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}
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