fix #I6WHKN [app_chatgpt]敏感语处理有问题，要修置顶

2025-02-23 04:11:36 +02:00 · 2023-04-18 01:16:41 +08:00
parent 3072c7399b
commit 437da3d5b3
3 changed files with 48 additions and 46 deletions
--- a/app_chatgpt/models/ai_robot.py
+++ b/app_chatgpt/models/ai_robot.py
@@ -40,7 +40,7 @@ GPT-3	A set of models that can understand and generate natural language
    # begin gpt 参数
    # 1. stop：表示聊天机器人停止生成回复的条件，可以是一段文本或者一个列表，当聊天机器人生成的回复中包含了这个条件，就会停止继续生成回复。
    # 2. temperature：控制回复的“新颖度”，值越高，聊天机器人生成的回复越不确定和随机，值越低，聊天机器人生成的回复会更加可预测和常规化。
-    # 3. top_p：与temperature有些类似，也是控制回复的“新颖度”。不同的是，top_p控制的是回复中概率最高的几个可能性的累计概率之和，值越小，生成的回复越保守，值越大，生成的回复越新颖。
+    # 3. top_p：言语连贯性，与temperature有些类似，也是控制回复的“新颖度”。不同的是，top_p控制的是回复中概率最高的几个可能性的累计概率之和，值越小，生成的回复越保守，值越大，生成的回复越新颖。
    # 4. frequency_penalty：用于控制聊天机器人回复中出现频率过高的词汇的惩罚程度。聊天机器人会尝试避免在回复中使用频率较高的词汇，以提高回复的多样性和新颖度。
    # 5. presence_penalty：与frequency_penalty相对，用于控制聊天机器人回复中出现频率较低的词汇的惩罚程度。聊天机器人会尝试在回复中使用频率较低的词汇，以提高回复的多样性和新颖度。
    max_tokens = fields.Integer('Max response', default=600,
@@ -50,7 +50,7 @@ GPT-3	A set of models that can understand and generate natural language
                                (including system message, examples, message history, and user query) and the model's response.
                                One token is roughly 4 characters for typical English text.
                                """)
-    temperature = fields.Float(string='Temperature', default=0.9,
+    temperature = fields.Float(string='Temperature', default=0.8,
                               help="""
                               Controls randomness. Lowering the temperature means that the model will produce
                               more repetitive and deterministic responses.
@@ -105,38 +105,46 @@ GPT-3	A set of models that can understand and generate natural language
    def action_disconnect(self):
        requests.delete('https://chatgpt.com/v1/disconnect')

-    def get_ai(self, data, author_id=False, answer_id=False, param={}):
-        #     通用方法
-        # author_id: 请求的 partner_id 对象
-        # answer_id: 回答的 partner_id 对象
-        # kwargs，dict 形式的可变参数
-        self.ensure_one()
-        # 前置勾子，一般返回 False，有问题返回响应内容
-        res_pre = self.get_ai_pre(data, author_id, answer_id, param)
-        if res_pre:
-            return res_pre
-        if hasattr(self, 'get_%s' % self.provider):
-            res = getattr(self, 'get_%s' % self.provider)(data, author_id, answer_id, param)
-        else:
-            res = _('No robot provider found')
-        
-        # 后置勾子，返回处理后的内容，用于处理敏感词等
-        res_post = self.get_ai_post(res, author_id, answer_id,  param)
-        return res_post
-
    def get_ai_pre(self, data, author_id=False, answer_id=False, param={}):
        if self.is_filtering:
            search = WordsSearch()
            search.SetKeywords([])
-            content = data[0]['content']
+            if isinstance(data, list):
+                content = data[len(data)-1]['content']
+            else:
+                content = data
            sensi = search.FindFirst(content)
            if sensi is not None:
+                _logger.error('==========敏感词：%s' % sensi['Keyword'])
                return _('温馨提示：您发送的内容含有敏感词，请修改内容后再向我发送。')
        else:
            return False

+    def get_ai(self, data, author_id=False, answer_id=False, param={}):
+        #     通用方法
+        # author_id: 请求的 partner_id 对象
+        # answer_id: 回答的 partner_id 对象
+        # param，dict 形式的参数
+        # 调整输出为2个参数：res_post详细内容，is_ai是否ai的响应
+        
+        self.ensure_one()
+        # 前置勾子，一般返回 False，有问题返回响应内容，用于处理敏感词等
+        res_pre = self.get_ai_pre(data, author_id, answer_id, param)
+        if res_pre:
+            # 有错误内容，则返回上级内容及 is_ai为假
+            return res_pre, False
+        if not hasattr(self, 'get_%s' % self.provider):
+            res = _('No robot provider found')
+            return res, False
+        
+        res = getattr(self, 'get_%s' % self.provider)(data, author_id, answer_id, param)
+        # 后置勾子，返回处理后的内容
+        res_post, is_ai = self.get_ai_post(res, author_id, answer_id,  param)
+        return res_post, is_ai
+    
    def get_ai_post(self, res, author_id=False, answer_id=False, param={}):
        if res and author_id and isinstance(res, openai.openai_object.OpenAIObject) or isinstance(res, list):
+            # 返回是个对象，那么就是ai
            usage = json.loads(json.dumps(res['usage']))
            content = json.loads(json.dumps(res['choices'][0]['message']['content']))
            data = content.replace(' .', '.').strip()
@@ -169,9 +177,10 @@ GPT-3	A set of models that can understand and generate natural language
                            'first_ask_time': ask_date
                        })
                    ai_use.write(vals)
+            return data, True
        else:
-            data = res
-        return data
+            # 直接返回错误语句，那么就是非ai
+            return res, False
    
    def get_ai_system(self, content=None):
        # 获取基础ai角色设定, role system
@@ -221,7 +230,7 @@ GPT-3	A set of models that can understand and generate natural language

        # 处理传参，传过来的优先于 robot 默认的
        max_tokens = param.get('max_tokens') or self.max_tokens or 600,
-        temperature = param.get('temperature') or self.temperature or 0.9,
+        temperature = param.get('temperature') or self.temperature or 0.8,
        top_p = param.get('top_p') or self.top_p or 0.6,
        frequency_penalty = param.get('frequency_penalty') or self.frequency_penalty or 0.5,
        presence_penalty = param.get('presence_penalty') or self.presence_penalty or 0.5,
@@ -250,7 +259,7 @@ GPT-3	A set of models that can understand and generate natural language
                model=self.ai_model,
                messages=messages,
                n=1,
-                temperature=self.temperature or 0.9,
+                temperature=self.temperature or 0.8,
                max_tokens=self.max_tokens or 600,
                top_p=self.top_p or 0.6,
                frequency_penalty=self.frequency_penalty or 0.5,
@@ -276,7 +285,7 @@ GPT-3	A set of models that can understand and generate natural language
            pdata = {
                "model": self.ai_model,
                "prompt": data,
-                "temperature": 0.9,
+                "temperature": 0.8,
                "max_tokens": max_tokens,
                "top_p": 1,
                "frequency_penalty": 0.0,
@@ -311,9 +320,9 @@ GPT-3	A set of models that can understand and generate natural language
        else:
            messages = [{"role": "user", "content": data}]

-        # 处理传参，传过来的优先于 robot 默认的
+        # todo: 处理传参，传过来的优先于 robot 默认的，当前有问题，无法做tuple转换
        max_tokens = param.get('max_tokens') or self.max_tokens or 600,
-        temperature = param.get('temperature') or self.temperature or 0.9,
+        temperature = param.get('temperature') or self.temperature or 0.8,
        top_p = param.get('top_p') or self.top_p or 0.6,
        frequency_penalty = param.get('frequency_penalty') or self.frequency_penalty or 0.5,
        presence_penalty = param.get('presence_penalty') or self.presence_penalty or 0.5,
@@ -330,7 +339,7 @@ GPT-3	A set of models that can understand and generate natural language
            messages=messages,
            # 返回的回答数量
            n=1,
-            temperature=self.temperature or 0.9,
+            temperature=self.temperature or 0.8,
            max_tokens=self.max_tokens or 600,
            top_p=self.top_p or 0.6,
            frequency_penalty=self.frequency_penalty or 0.5,
--- a/app_chatgpt/models/lib/sensi_words.txt
+++ b/app_chatgpt/models/lib/sensi_words.txt
@@ -10567,7 +10567,6 @@ fuck兴奋剂
 禽獸
 明慧网
 jiejie
-的
 嫩穴
 床上激情自拍图
 6。4
@@ -13533,7 +13532,6 @@ re xue ying hao
 烂
 淫荡老师
 小姨子的小嫩屄
-create
 亚洲激情BT
 省长的儿媳妇
 苹果日报
@@ -14077,7 +14075,6 @@ gong fu
 89年的鬥爭
 台湾十八电影
 小淫虫电影
-CREATE
 外阴
 外？？挂
 毛爷爷复活
--- a/app_chatgpt/models/mail_channel.py
+++ b/app_chatgpt/models/mail_channel.py
@@ -63,7 +63,7 @@ class Channel(models.Model):
        answer_id = user_id.partner_id
        # todo: 只有个人配置的群聊才给配置
        param = self.get_ai_config(ai)
-        res = ai.get_ai(messages, author_id, answer_id, param)
+        res, is_ai = ai.get_ai(messages, author_id, answer_id, param)
        if res:
            res = res.replace('\n', '<br/>')
            channel.with_user(user_id).message_post(body=res, message_type='comment', subtype_xmlid='mail.mt_comment', parent_id=message.id)
@@ -144,22 +144,18 @@ class Channel(models.Model):
        if not msg:
            return rdata
        # api_key = self.env['ir.config_parameter'].sudo().get_param('app_chatgpt.openapi_api_key')
-        api_key = ''
-        if ai:
+        # ai处理，不要自问自答
+        if ai and answer_id != message.author_id:
            api_key = ai.openapi_api_key
            if not api_key:
                _logger.warning(_("ChatGPT Robot【%s】have not set open api key."))
                return rdata
-        try:
-            openapi_context_timeout = int(self.env['ir.config_parameter'].sudo().get_param('app_chatgpt.openapi_context_timeout')) or 60
-        except:
-            openapi_context_timeout = 60
-        sync_config = self.env['ir.config_parameter'].sudo().get_param('app_chatgpt.openai_sync_config')
-        openai.api_key = api_key
-        # print(msg_vals)
-        # print(msg_vals.get('record_name', ''))
-        # print('self.channel_type :',self.channel_type)
-        if ai:
+            try:
+                openapi_context_timeout = int(self.env['ir.config_parameter'].sudo().get_param('app_chatgpt.openapi_context_timeout')) or 60
+            except:
+                openapi_context_timeout = 60
+            sync_config = self.env['ir.config_parameter'].sudo().get_param('app_chatgpt.openai_sync_config')
+            openai.api_key = api_key
            # 非4版本，取0次。其它取3 次历史
            chat_count = 0 if '4' in ai.ai_model else 3
            if author_id != answer_id.id and self.channel_type == 'chat':