开发消息分流demo
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4
ai.py
4
ai.py
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@ -3,8 +3,6 @@ import json
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from zhipuai import ZhipuAI
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from zhipuai import ZhipuAI
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import flask
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import flask
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# client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi") # 请填写您自己的APIKey
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# client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi") # 请填写您自己的APIKey
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# response = client.chat.completions.create(
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# response = client.chat.completions.create(
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# model="glm-4", # 请填写您要调用的模型名称
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# model="glm-4", # 请填写您要调用的模型名称
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@ -21,9 +19,9 @@ def app_chat():
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data = json.loads(flask.globals.request.get_data())
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data = json.loads(flask.globals.request.get_data())
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# print(data)
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# print(data)
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uid = data["user_id"]
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uid = data["user_id"]
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if not data["text"][-1] in ['?', '?', '.', '。', ',', ',', '!', '!']:
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if not data["text"][-1] in ['?', '?', '.', '。', ',', ',', '!', '!']:
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data["text"] += "。"
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data["text"] += "。"
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data["text"] += ""
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# 使用ZhipuAI库调用模型生成回复
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# 使用ZhipuAI库调用模型生成回复
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client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi") # 请填写您自己的APIKey
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client = ZhipuAI(api_key="73bdeed728677bc80efc6956478a2315.VerNWJMCwN9L5gTi") # 请填写您自己的APIKey
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@ -18,7 +18,10 @@ func Use(vals ...interface{}) {
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_ = val
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_ = val
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}
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}
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}
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}
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var keyWordMap = map[string]string{
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"生成课程表": "./examples/classExample.jpg",
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"生成校历": "./examples/SchoolCalendar.png",
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}
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type SendTextRequest struct {
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type SendTextRequest struct {
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InGroup bool `json:"in_group"` //本来想用于区分在群聊和非群聊时的上下文记忆规则,但是最终没有实现...
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InGroup bool `json:"in_group"` //本来想用于区分在群聊和非群聊时的上下文记忆规则,但是最终没有实现...
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UserID string `json:"user_id"`
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UserID string `json:"user_id"`
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@ -94,7 +97,7 @@ func main() {
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func sendMessage(msg *openwechat.Message, self *openwechat.Self) {
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func sendMessage(msg *openwechat.Message, self *openwechat.Self) {
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if msg.IsTickledMe() {
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if msg.IsTickledMe() {
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msg.ReplyText("别拍了,机器人是会被拍坏掉的。")
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msg.ReplyText("你好。我是大数据分析与知识处理课程的AI助手,专门为学习相关课程的学生提供帮助,包括解答疑问、提供学习资料、辅助分析和理解复杂概念等。如果您有任何与大数据分析、知识处理相关的问题,欢迎随时向我提问。")
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return
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return
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}
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}
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@ -147,7 +150,16 @@ func sendMessage(msg *openwechat.Message, self *openwechat.Self) {
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}
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}
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}
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}
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} else {
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}else {
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// 遍历 map
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for keyword, value := range keyWordMap {
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if strings.Contains(msg.Content, keyword) {
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img, _ := os.Open(value)
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defer img.Close()
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msg.ReplyImage(img)
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return
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}
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}
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// 调用GPT
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// 调用GPT
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sender, _ := msg.Sender()
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sender, _ := msg.Sender()
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